References
Published online by Cambridge University Press: 13 June 2019
Summary
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Chapter
- Information
- Game Theory for Next Generation Wireless and Communication NetworksModeling, Analysis, and Design, pp. 459 - 493Publisher: Cambridge University PressPrint publication year: 2019
References
Gu, Y., Saad, W., Bennis, M., Debbah, M., and Han, Z., “Matching theory for future wireless networks: Fundamentals and applications,” IEEE Communications Magazine, vol. 53, no. 5, pp. 52–59, May 2015.Google Scholar
Manlove, D. F., Algorithmics of matching under preferences, vol. 2, World Scientific, Singapore, 2013.Google Scholar
Roth, A. E. and Sotomayor, M. A. O., Two-sided matching, a study in game-theoretic modeling and analysis, Cambridge University Press, Cambridge, UK, 1992.Google Scholar
Gale, D. and Shapley, L. S., “College admissions and the stability of marriage,” The American Mathematical Monthly, vol. 69, no. 1, pp. 9–15, 1962.Google Scholar
Gu, Y., Jiang, C., Cai, L. X., Pan, M., Song, L., and Han, Z., “Dynamic path to stability in the LTE-unlicensed with user mobility: A dynamic matching framework,” IEEE Transactions on Wireless Communications, vol. 15, no. 7, pp. 4547–4561, July 2017.Google Scholar
J. Zhou, and Fan, M., “LTE-U forum and coexistence overview,” https://mentor.ieee.org/802.19/dcn/15/19-15-0057-00-0000-lte-u-forum-and-coexistence-overview.pdf, 2015.Google Scholar
QUALCOMM, “LTE in unlicensed spectrum: Harmonious coexistence with wi-fi,” Tech. Rep., Jun. 2014.Google Scholar
J. Wannstrom, “Carrier aggregation,” www.3gpp.org/technologies/keywords-acronyms/101-carrier-aggregation-explained, Jun. 2013.Google Scholar
Cavalcante, A. M., Almeida, E., Vieira, R. D., Chaves, F., Paiva, R. C., Abinader, F., Choudhury, S., Tuomaala, E., and Doppler, K., “Performance evaluation of LTE and Wi-Fi coexistence in unlicensed bands,” in Vehicular Technology Conference (VTC Spring), 2013 IEEE 77th. IEEE, 2013, pp. 1–6.Google Scholar
Abinader, F. M., Almeida, E. P., Chaves, F. S., Cavalcante, A. M., Vieira, R. D., Paiva, R. C., Sobrinho, A. M., Choudhury, S., Tuomaala, E., Doppler, K., et al., “Enabling the coexistence of LTE and Wi-Fi in unlicensed bands,” IEEE Communications Magazine, vol. 52, no. 11, pp. 54–61, 2014.Google Scholar
Gu, Y., Zhang, Y., Cai, L. X., Pan, M., Song, L., and Han, Z., “LTE-unlicensed coexistence mechanism: A matching game framework,” IEEE Wireless Communications, vol. 23, no. 6, pp. 54–60, 2016.CrossRefGoogle Scholar
Bertsimas, D. and Tsitsiklis, J. N., Introduction to linear optimization, vol. 6, Athena Scientific, Belmont, MA, 1997.Google Scholar
El-Atta, A. H. A. and Moussa, M. I., “Student project allocation with preference lists over (student, project) pairs,” in Computer and Electrical Engineering, 2009 . ICCEE’09. Second International Conference on. IEEE, 2009, vol. 1, pp. 375–379.Google Scholar
Pantisano, F., Bennis, M., Saad, W., Valentin, S., and Debbah, M., “Matching with externalities for context-aware user-cell association in small cell networks,” in Globecom Workshops (GC Wkshps), 2013 IEEE. IEEE, 2013, pp. 4483–4488.Google Scholar
LG Electronics, RP-151109, “New SI proposal: Feasibility study on LTE-based V2X services,” www.3gpp.org/DynaReport/TDocExMtg--RP-68--31197.htm.Google Scholar
3GPP, TR22.885, “Study on LTE support for V2X services,” www.3gpp.org/DynaReport/22885.htm.Google Scholar
Sun, W., Yuan, D., Ström, E. G., and Brännström, F., “Resource sharing and power allocation for D2D-based safety-critical V2X communications,” in Communication Workshop (ICCW), 2015 IEEE International Conference on. IEEE, 2015, pp. 2399–2405.Google Scholar
Saad, W., Han, Z., Hjorungnes, A., Niyato, D., and Hossain, E., “Coalition formation games for distributed cooperation among roadside units in vehicular networks,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 1, pp. 48–60, 2011.Google Scholar
Gu, Y., Cai, L. X., Pan, M., Song, L., and Han, Z., “Exploiting the stable fixture matching game for content sharing in D2D-based LTE-V2X communications,” in IEEE Globe Communication Conference (Globecom), Washington DC, December 2016.Google Scholar
Irving, R. W. and Scott, S., “The stable fixtures problem – a many-to-many extension of stable roommates,” Discrete Applied Mathematics, vol. 155, no. 16, pp. 2118–2129, 2007.Google Scholar
Zhao, D., Li, X., and Ma, H., “How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint,” in 2014 Proceedings of IEEE INFOCOM, Toronto, Canada, Apr. 2014.Google Scholar
Gao, L., Iosifidis, G., Huang, J., and Tassiulas, L., “Hybrid data pricing for network-assisted user-provided connectivity,” in 2014 Proceedings of IEEE INFOCOM, Toronto, Canada, Apr. 2014.Google Scholar
Luo, T., Tan, H., and Xia, L., “Profit-maximizing incentive for participatory sensing,” in 2014 Proceedings of IEEE INFOCOM, Toronto, Canada, Apr. 2014.Google Scholar
Guo, Y., Duan, L., and Zhang, R., “Optimal pricing and load sharing for energy saving with communications cooperation,” IEEE Transactions on Wireless Communications, vol. PP, no. 99, pp. 951–964, Sep. 2015.Google Scholar
Green, J. R. and Stokey, N. L., “A Comparison of Tournaments and Contracts,” Journal of Political Economy, vol. 91, no. 3, pp. 349–364, Jun. 1983.Google Scholar
Gao, L., Wang, X., Xu, Y., and Zhang, Q., “Spectrum trading in cognitive radio networks: A contract-theoretic modeling approach,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 4, pp. 843–855, Apr. 2011.Google Scholar
Duan, L., Gao, L., and Huang, J., “Cooperative spectrum sharing: A contract-based approach,” IEEE Transactions on Mobile Computing, vol. 13, no. 1, pp. 174–187, Jan. 2014.Google Scholar
Duan, L., Kubo, T., Sugiyama, K., Huang, J., Hasegawa, T., and Walrand, J., “Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing,” in 2012 Proceedings of IEEE INFOCOM, Orlando, FL, Mar. 2012.Google Scholar
Hasan, Z. and Bhargava, V., “Relay selection for OFDM wireless systems under asymmetric information: A contract-theory based approach,” IEEE Transactions on Wireless Communications, vol. 12, no. 8, pp. 3824–3837, Aug. 2013.Google Scholar
Xu, C., Song, L., and Han, Z., Resource management for device-to-device underlay communication, Springer, Germany, 2014.Google Scholar
Min, H., Lee, J., Park, S., and Hong, D., “Capacity enhancement using an interference limited area for device-to-device uplink underlaying cellular networks,” IEEE Transactions on Wireless Communications, vol. 10, no. 12, pp. 3995–4000, Dec. 2011.Google Scholar
Quek, T. Q. S., de la Roche, G., Guvenc, I., and Kountouris, M., Small cell networks: Deployment, PHY techniques, and resource management, Cambridge University Press, Cambridge, UK, 2013.Google Scholar
Yaacoub, E., “On the use of device-to-device communications for QoS and data rate enhancement in LTE public safety networks,” in IEEE WCNC – Workshop on Device-to-Device and Public Safety Communications, Istanbul, Turkey, Apr. 2014.Google Scholar
Song, L., Niyato, D., Han, Z., and Hossain, E., Wireless device-to-device communications and networks, Cambridge University Press, Cambridge, UK, 2014.Google Scholar
Golrezaei, N., Molisch, A. F., Dimakis, A. G., and Caire, G., “Femtocaching and device-to-device collaboration: A new architecture for wireless video distribution,” IEEE Communications Magazine, vol. 51, no. 4, pp. 142–149, Apr. 2013.Google Scholar
Werin, L. and Wijkander, H., Contract economics, Blackwell Publishers, Oxford, UK, 1992.Google Scholar
Zhang, Y., Song, L., Saad, W., Dawy, Z., and Han, Z., “Contract-based incentive mechanisms for device-to-device communications in cellular networks,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 10, pp. 2144–2155, Oct. 2015.Google Scholar
Feng, D., Lu, L., Yuan-Wu, Y., Li, G. Y., Feng, G., and Li, S., “Device-to-device communications underlaying cellular networks,” IEEE Transactions on Communications, vol. 61, no. 8, pp. 3541–3551, Aug. 2013.Google Scholar
Xu, C., Song, L., Han, Z., Li, D., and Jiao, B., “Resource Allocation Using A Reverse Iterative Combinatorial Auction for Device-to-Device Underlay Cellular Networks,” in IEEE Globe Communication Conference (GLOBECOM), Anaheim, CA, Dec. 2012.Google Scholar
Gao, L., Wang, X., Xu, Y., and Zhang, Q., “Spectrum trading in cognitive radio networks: A contract-theoretic modeling approach,” IEEE Journal on Selected Areas in Communications (JSAC), vol. 29, no. 4, pp. 843–855, Apr. 2011.Google Scholar
Gao, L., Huang, J., Chen, Y., and Shou, B., “Contract-based cooperative spectrum sharing,” IEEE Transactions on Mobile Computing, vol. 13, no. 1, pp. 174–187, Jan. 2014.Google Scholar
Chatzimilioudis, G., Konstantinidis, A., Laoudias, C., and Zeinalipour-Yazti, D., “Crowd-sourcing with smartphones,” IEEE Internet Computing, vol. 16, no. 5, pp. 36–44, Sept. 2012.Google Scholar
Angwin, J. and Valentino-Devries, J., “Apple, Google collect user data,” The Wall Street Journal, 2011.Google Scholar
Chernova, S., DePalma, N., Morant, E., and Breazeal, C., “Crowdsourcing human-robot interaction: Application from virtual to physical worlds,” in RO-MAN, 2011 IEEE, Atlanta, GA, Jul. 2011.Google Scholar
Varshney, L. R., “Privacy and reliability in crowdsourcing service delivery,” in SRII Global Conference (SRII), San Jose, CA, Jul. 2012.Google Scholar
Karmann, A., “Multiple-task and multiple-agent models: Incentive contracts and an application to point pollution control,” Annals of Operations Research, vol. 54, no. 1, pp. 57–78, Dec. 1994.Google Scholar
Meng, D. and Tian, G., “Multi-task Incentive Contract and Performance Measurement with Multidimensional Types,” Games and Economic Behavior, vol. 77, no. 1, pp. 377–404, Jan. 2013.Google Scholar
Fehr, E. and Schmidt, K. M., “Fairness and incentives in a multi-task principal-agent model,” Scandinavian Journal of Economics, vol. 106, no. 3, pp. 453–474, Sep. 2004.Google Scholar
Holmstrom, B. and Milgrom, P., “Multitask principal-agent analyses: Incentive contracts, asset ownership, and job design,” Journal of Law, Economics, & Organization, vol. 7, no. sp, pp. 24–52, Jan. 1991.Google Scholar
Zhang, Y., Gu, Y., Liu, L., Pan, M., Dawy, Z., and Han, Z., “Incentive mechanism in crowdsourcing with moral hazard,” in IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, Mar. 2015.Google Scholar
Bergemann, D., Shen, J., Xu, Y., and Yeh, E., “Multi-dimensional mechanism design with limited information,” in Proceedings of the 13th ACM Conference on Electronic Commerce, Valencia, Spain, Jun. 2012, pp. 162–178.Google Scholar
Thiagarajan, A., Ravindranath, L., LaCurts, K., Madden, S., Balakrishnan, H., Toledo, S., and Eriksson, J., “Vtrack: Accurate, energy-aware road traffic delay estimation using mobile phones,” in Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, Berkeley, California, 2009, pp. 85–98.Google Scholar
Rana, R. K., Chou, C. T., Kanhere, S. S., Bulusu, N., and Hu, W., “Ear-phone: An end-to-end participatory urban noise mapping system,” in Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, Stockholm, Sweden, 2010, pp. 105–116.Google Scholar
Barzel, Y., “Measurement cost and the organization of markets,” Journal of Law and Economics, vol. 25, no. 1, pp. 27–48, Apr. 1982.Google Scholar
Danforth, B., “Variance-covariance matrix,” www.unc.edu/∼jjharden/methods/vcv_week3.pdf, 2009.Google Scholar
Leger, M. N., Vega-Montoto, L., and Wentzell, P. D., “Methods for systematic investigation of measurement error covariance matrices,” Chemometrics and Intelligent Laboratory Systems, vol. 77, no. 12, pp. 181–205, May 2005.Google Scholar
Stevens, M. and D’Hond, E., “Crowdsourcing of pollution data using smartphones,” in UbiComp’10, Copenhagen, Denmark, Sep. 2010.Google Scholar
Bebchuk, L. A., Fried, J. M., and Walker, D., “Managerial power and rent extraction in the design of executive compensation,” University of Chicago Law Review, vol. 69, no. 2, pp. 751–846, Jul. 2002.Google Scholar
Bolton, P., Scheinkman, J., and Xiong, W., “Executive compensation and short-termist behavior in speculative markets,” Working paper, National Bureau of Economic Research, May 2003.Google Scholar
Investopedia, “Certainty equivalent,” www.investopedia.com/terms/c/certaintyequivalent.asp, 2003.Google Scholar
Holmstrom, B. and Tirole, J., “Market Liquidity and Performance Monitoring,” The Journal of Political Economy, vol. 101, no. 4, pp. 678–709, Aug. 1993.Google Scholar
Bengt Holmstrom, P. M., “Multitask Principal-Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design,” Journal of Law, Economics, & Organization, vol. 7, pp. 24– 52, 1991.Google Scholar
Mohan, P., Padmanabhan, V. N., and Ramjee, R., “Nericell: Rich monitoring of road and traffic conditions using mobile smartphones,” in Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, Nov. 2008, pp. 323–336.Google Scholar
Letaief, K. and Zhang, W., “Cooperative communications for cognitive radio networks,” Proceedings of the IEEE, vol. 97, no. 5, pp. 878–893, May 2009.Google Scholar
Kim, H. and Shin, K. G., “Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks,” IEEE Transactions on Mobile Computing, vol. 7, no. 5, pp. 533–545, May 2008.Google Scholar
Hossain, E., Niyato, D., and Han, Z., Dynamic spectrum access and management in cognitive radio networks, Cambridge University Press, Cambridge, UK, 2009.Google Scholar
Zhu, X., Shen, L., and Yum, T.-S. P., “Analysis of cognitive radio spectrum access with optimal channel reservation,” IEEE Communications Letters, vol. 11, no. 4, pp. 304–306, Apr. 2007.Google Scholar
Brodersen, R. W., Wolisz, A., Cabric, D., and Mishra, S. M., “Corvus: A cognitive radio approach for usage of virtual unlicensed spectrum,” white paper, 2004.Google Scholar
Wang, S., Xu, P., Xu, X., Tang, S., Li, X., and Liu, X., “TODA truthful online double auction for spectrum allocation in wireless networks,” in IEEE Symposium on New Frontiers in Dynamic Spectrum, Singapore, Apr. 2010.Google Scholar
Pan, M., Li, P., Song, Y., Fang, Y., and Lin, P., “Spectrum clouds: A session based spectrum trading system for multi-hop cognitive radio networks,” in INFOCOM 2012 Proceedings of IEEE Conference on Computer Communications, Orlando, FL, Mar. 2012.Google Scholar
Laffont, J.-J. and Tirole, J., “The dynamics of incentive contracts,” Econometrica, vol. 56, no. 5, pp. 1153–1175, 1988.Google Scholar
Akerlof, G., “The Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism,” in Essential readings in economics, pp. 175–188, Macmillan Education UK, London, 1995.Google Scholar
Zhang, Y., Gu, Y., Pan, M., Dawy, Z., Song, L., and Han, Z., “Financing contract with adverse selection and moral hazard for spectrum trading in cognitive radio networks,” in Third IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP 2015), Chengdu, China, Jul. 2015.Google Scholar
Han, Z., Niyato, D., Saad, W., Basar, T., and Hjorungnes, A., Game theory in wireless and communication networks: Theory, models and applications, Cambridge University Press, Cambridge, UK, 2011.Google Scholar
Nie, N. and Comaniciu, C., “Adaptive channel allocation spectrum etiquette for cognitive radio networks,” in First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Nov. 2005, pp. 269–278.Google Scholar
Niyato, D., Hossain, E., and Han, Z., “Dynamics of multiple-seller and multiple-buyer spectrum trading in cognitive radio networks: A game-theoretic modeling approach,” IEEE Transactions on Mobile Computing, vol. 8, no. 8, pp. 1009–1022, Aug. 2009.Google Scholar
Xie, R., Yu, F. R., and Ji, H., “Spectrum sharing and resource allocation for energy-efficient heterogeneous cognitive radio networks with femtocells,” in IEEE International Conference on Communications (ICC), Ottawa, Canada, Jun. 2012, pp. 1661–1665.Google Scholar
Zhou, X. and Zheng, H., “Trust: A general framework for truthful double spectrum auctions,” in INFOCOM 2009, IEEE, Rio de Janeiro, Brazil, Apr. 2009, pp. 999–1007.Google Scholar
Gandhi, S., Buragohain, C., Cao, L., Zheng, H., and Suri, S., “A general framework for wireless spectrum auctions,” in 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Apr. 2007, , pp. 22–33.Google Scholar
Wang, X., Li, Z., Xu, P., Xu, Y., Gao, X., and Chen, H. H., “Spectrum sharing in cognitive radio networks: An auction-based approach,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 3, pp. 587–596, Jun. 2010.Google Scholar
Zhang, Y., Song, L., Saad, W., Dawy, Z., and Han, Z., “Contract-based incentive mechanisms for device-to-device communications in cellular networks,” IEEE Journal of Selected Areas in Communication (JSAC), vol. 33, no. 10, pp. 2144–2155, Oct. 2015.Google Scholar
Zhang, Y., Gu, Y., Song, L., Dawy, Z., and Han, Z., “Tournament based incentive mechanism designs for mobile crowdsourcing,” in IEEE Global Communications Conference (GLOBECOM), San Diego, CA, Dec. 2015.Google Scholar
Edward, R. M. T. Prescott, C., “Pareto optima and competitive equilibria with adverse selection and moral hazard,” Econometrica, vol. 52, no. 1, pp. 21–45, Jan. 1984.Google Scholar
Darrough, M. N. and Stoughton, N. M., “Moral hazard and adverse selection: The question of financial structure,” The Journal of Finance, vol. 41, no. 2, pp. 501–513, Jun. 1986.Google Scholar
Laffont, J.-J. and Tirole, J., A theory of incentives in procurement and regulation, The MIT Press, Cambridge, MA, 1993.Google Scholar
Nikolos, I. K., Valavanis, K. P., Tsourveloudis, N. C., and Kostaras, A. N., “Evolutionary algorithm based offline/online path planner for UAV navigation,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 33, no. 6, pp. 898–912, Dec. 2003.Google Scholar
Başar, T. and Olsder, G. J., Dynamic noncooperative game theory, SIAM Series in Classics in Applied Mathematics, Philadelphia, PA, Jan. 1999.Google Scholar
Yu, H., Meier, K., Argyle, M., and Beard, R. W., “Cooperative path planning for target tracking in urban environments using unmanned air and ground vehicles,” IEEE/ASME Transactions on Mechatronics, vol. 20, no. 2, pp. 541–552, Apr. 2015.Google Scholar
Zhou, H., Kong, H., Wei, L., Creighton, D., and Nahavandi, S., “Efficient road detection and tracking for unmanned aerial vehicle,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 1, pp. 297–309, July 2014.Google Scholar
Hu, J. and Xu, Z., “Distributed cooperative control for deployment and task allocation of unmanned aerial vehicle networks,” IET Control Theory & Applications, vol. 7, no. 11, pp. 1574–1582, July 2013.Google Scholar
Lakshmivarahan, S. and Narendra, K. S., “Learning algorithms for two-person zero-sum stochastic games with incomplete information: A unified approach,” SIAM J. Control Optim., vol. 20, no. 4, pp. 541–552, 1982.Google Scholar
Ghosh, M. K., McDonald, D., and Sinha, S., “Zero-sum stochastic games with partial information,” Journal of Optimization Theory and Applications, vol. 121, no. 1, pp. 99– 118, Apr. 2004.Google Scholar
Takahashi, M., “Stochastic games with infinitely many strategies,” Journal of Science of the Hiroshima University Series, vol. 26, pp. 123–134, 1962.Google Scholar
Fink, M. A., “Equilibrium in a stochastic n-person game,” Journal of Science of the Hiroshima University Series A-1, vol. 28, pp. 89–93, 1964.Google Scholar
Nowak, A. S. and Szajowski, K., “Nonzero-sum stochastic games,” Stochastic and Differential Games, Annals of the International Society of Dynamic Games, vol. 4, pp. 297–342, 1999.Google Scholar
Jaskiewicz, A. and Nowak, A. S., “Nonzero-sum stochastic games,” in Handbook of Dynamic Game Theory, Başar, Tamer and Zaccour, Georges, Eds., Springer, New York, pp. 1–64, 2018.Google Scholar
Alos-Ferrer, C. and Ritzberger, K., “Equilibrium existence for large perfect information games,” Journal of Mathematical Economics, vol. 62, pp. 5–18, 2016.Google Scholar
Altman, E., Avrachenkov, K., Bonneau, N., Debbah, M., El-Azouzi, R., and Sadoc Menasche, D., “Constrained cost-coupled stochastic games with independent state processes,” Operations Research Letters, vol. 36, pp. 160–164, 2008.Google Scholar
Amir, R., “Continuous stochastic games of capital accumulation with convex transitions,” Games and Economic Behavior, vol. 15, pp. 132–148, 1996.Google Scholar
Hamadene, S. and Mu, R., “Existence of Nash equilibrium points for Markovian non-zero-sum stochastic differential games with unbounded coefficients,” Stochastics, vol. 87, no. 1, pp. 85–111, July 2014.Google Scholar
Sorin, S., “Asymptotic properties of a non-zero sum stochastic game,” International Journal of Game Theory, vol. 15, no. 2, pp. 101–107, Feb. 1985.Google Scholar
Simon, R. S., “The challenge of non-zero-sum stochastic games,” International Journal of Game Theory, vol. 45, no. 1, pp. 191–204, Mar. 2016.Google Scholar
Kahneman, D. and Tversky, A., “Prospect theory: An analysis of decision under risk,” Econometrica, vol. 47, pp. 263–291, 1979.Google Scholar
Quattrone, G. A. and Tversky, A., “Contrasting rational and psychological analyses of political choice,” The American Political Science Review, vol. 82, no. 3, pp. 719–736, 1988.Google Scholar
Camerer, C., Babcock, L., Loewenstein, G., and Thaler, R., “Labor supply of New York City cab drivers: One day at a time,” Quarterly Journal of Economics, no. 111, pp. 408–441, May 1997.Google Scholar
Kahneman, D. and Tversky, A., Choices, values, and frames, Cambridge University Press, Cambridge, UK, 2000.Google Scholar
Wang, Y., Nakao, A., and Ma, J., “Psychological research and application in autonomous networks and systems: A new interesting field,” in Proceedings of International Conference on Intelligent Computing and Integrated Systems, Guilin, China, Oct. 2010.Google Scholar
Metzger, L. P. and Riegery, M. O., “Equilibria in games with prospect theory preferences,” Working Paper, Nov. 2009.Google Scholar
Aumann, R. J., “Rationality and bounded rationality,” Games and Economic Behavior, vol. 21, no. 1, pp. 2–14, Oct. 1997.Google Scholar
Samuelson, L., “Bounded rationality and game theory,” The Quarterly Review of Economics and Finance, vol. 36, no. 1, pp. 17–35, 1996.Google Scholar
Selten, R., “Bounded rationality,” Journal of Institutional and Theoretical Economics (JITE), vol. 146, no. 4, pp. 649–658, Dec. 1990.Google Scholar
Simon, H. A., “A behavioral model of rational choice,” Quarterly Journal of Economics, vol. 59, pp. 99–118, 1955.Google Scholar
Simon, H. A., “Rational choice and the structure of the environment,” Psychological Review, vol. 63, no. 2, pp. 129–138, 1956.Google Scholar
Simon, H. A., “Invariants of human behavior,” Annual Review of Psychology, vol. 41, pp. 1–19, 1990.Google Scholar
Stirling, W. C., Goodrich, M. A., and Packard, D. J., “Satisficing equilibria: A non-classical theory of games and decisions,” Autonomous Agents and Multi-Agent Systems, vol. 5, pp. 305–328, 2002.Google Scholar
McKelvey, R. D. and Palfrey, T. R., “Quantal response equilibria for normal form games,” Games and Economic Behavior, vol. 10, no. 1, pp. 6–38, July 1995.Google Scholar
McKelvey, R. D. and Palfrey, T. R., “Quantal response equilibria for extensive form games,” Experimental Economics, vol. 1, no. 1, pp. 9–41, June 1998.Google Scholar
Zhang, B. and Hofbauer, J., “Quantal response methods for equilibrium selection in 2x2 coordination games,” Games and Economic Behavior, vol. 97, pp. 19–31, May 2016.Google Scholar
Sandholm, W. H., “Evolutionary game theory,” Encyclopedia of Complexity and Systems Science, 2009.Google Scholar
Nama, H., Mandayam, N., and Yates, R., “Network formation among selfish energy-constrained wireless devices,” in Proceedings of IEEE International Conference on Computer Communications (INFOCOM), Phoenix, AZ, Apr. 2008.Google Scholar
Li, T. and Mandayam, N., “When users interfere with protocols: Prospect theory in wireles networks using random access as an example,” IEEE Transactions on Wireless Communication, vol. 13, no. 4, pp. 1888–1907, Feb. 2014.Google Scholar
Etesami, S. R., Saad, W., Mandayam, N. B., and Poor, H. V., “Stochastic games for smart grid energy management with prospect prosumers,” IEEE Transactions on Automatic Control, to appear 2018.Google Scholar
Saad, W., Glass, A., Mandayam, N., and Poor, H. V., “Towards a user-centric grid: A behavioral perspective,” Proceedings of the IEEE, vol. 104, no. 4, pp. 865–882, Apr. 2016.Google Scholar
Sanjab, A., Saad, W. and Başar, T., “Prospect theory for enhanced cyber-physical security of drone delivery systems: A network interdiction game,” in Proceedings of the International Conference on Communications, Paris, France, May 2017.Google Scholar
Wang, Y., Saad, W., Mandayam, N., and Poor, H. V., “Load shifting in the smart grid: To participate or not?” IEEE Transactions on Smart Grid, vol. 7, no. 6, pp. 2604–2614, Nov. 2016.Google Scholar
Saad, W., Sanjab, A., Wang, Y., Kamhoua, C. and Kwiat, K., “Hardware trojan detection game: A prospect-theoretic approach,” IEEE Transactions on Vehicular Technology, vol. 66, no. 9, pp. 7697–7710, Sept. 2017.Google Scholar
El-Rahi, G., Etesami, S. R., Saad, W., Mandayam, N. B. and Poor, H. V., “Managing price uncertainty in prosumer-centric energy trading: A prospect-theoretic Stackelberg game approach,” IEEE Transactions on Smart Grid, 2018.Google Scholar
Xiao, L., Liu, J., Li, Y., Mandayam, N. B., and Poor, H. V., “Prospect theoretic analysis of anti-jamming communications in cognitive radio networks,” in Proceedings of IEEE Global Communication Conference, Austin, TX, USA, Dec. 2014.Google Scholar
Xu, D., Li, Y., Xiao, L., Mandayam, N. B., and Poor, H. V., “Prospect theoretic study of cloud storage defense against advanced persistent threats,” in Proceedings of the IEEE Global Communication Conference, Washington, DC, Dec. 2016.Google Scholar
El-Rahi, G., Sanjab, A., Saad, W., Mandayam, N. B., and Poor, H. V., “Prospect theory for enhanced smart gird resilience using distributed energy storage,” in Proceedings of the 54th Allerton Conference on Communication, Control, and Computing, Monticello, IL, Sept. 2016.Google Scholar
Wang, Y., Saad, W., Mandayam, N., and Poor, H. V., “Integrating energy storage in the smart grid: A prospect-theoretic approach,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, May 2014.Google Scholar
Yang, Y., Park, L. T., Mandayam, N. B., Seskar, I., Glass, A. L., and Sinha, N., “Prospect pricing in cognitive radio networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 1, pp. 56–70, Mar. 2015.Google Scholar
Tversky, A. and Kahneman, D., “Advances in prospect theory: Cumulative representation of uncertainty,” Journal of Risk and Uncertainty, vol. 5, pp. 297–323, Oct. 1992.Google Scholar
Perlaza, S., Tembine, H., Lasaulce, S., and Debbah, M., “Quality-of-service provisioning in decentralized networks: A satisfaction equilibrium approach,” IEEE Journal on Selected Topics in Signal Processing, Special Issue on Game Theory, vol. 6, no. 2, pp. 104–116, Apr. 2012.Google Scholar
Perlaza, S. M., Tembine, H., Lasaulce, S., and Debbah, M., “Satisfaction equilibrium: A general framework for QoS provisioning in self-configuring networks,” in Proceedings of the IEEE Global Communication Conference, Miami, FL, Dec. 2010.Google Scholar
Wright, J. R. and Leyton-Brown, K., “Predicting human behavior in unrepeated, simultaneous-move games,” Games and Economic Behavior, vol. 106, pp. 16–37, Nov. 2017.Google Scholar
Sanjab, A. and Saad, W., “Data injection attacks on smart grids with multiple adversaries: A game-theoretic perspective,” IEEE Transactions on Smart Grid, vol. 7, no. 4, pp. 2038– 2049, July 2016.Google Scholar
Christin, N., Grossklags, J., and Chuang, J., “Near rationality and competitive equilibria in networked systems,” in Proceedings of ACM SIGCOMM Workshop on Practice and Theory of Incentives in Networked Systems, Portland, OR, Sept. 2004.Google Scholar
Meriaux, F., Perlaza, S. M., Lasaulce, S., Han, Z., and Poor, H. V., “Achievability of efficient satisfaction equilibria in self-configuring networks,” in Proceedings of the International Conference on Game Theory for Networks (GameNets), Vancouver, Canada, May 2012.Google Scholar
Southwell, R., Chen, X., and Huang, J., “Quality of service games for spectrum sharing,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 3, pp. 589–600, Mar. 2014.Google Scholar
Rose, L., Perlaza, S. M., Debbah, M., and Le Martret, C. J., “Distributed power allocation with SINR constraints using trial and error learning,” in Proceedings of the IEEE Wireless Communications and Networking Conference, Shanghai, China, Apr. 2012.Google Scholar
Shen, S., Hu, K., Huang, L., Li, H., Han, R., and Cao, Q., “Quantal response equilibrium-based strategies for intrusion detection in WSNs,” Mobile Information Systems, vol. 2015, July 2015.Google Scholar
Ross, S. and Chaib-draa, B., “Learning to play a satisfaction equilibrium,” in Workshop on Evolutionary Models of Collaboration, Hynderanad, India, Jan. 2007.Google Scholar
Harper, D. G., “Competitive foraging in mallards: ‘Ideal free’ ducks,” Animal Behavior, vol. 30, no. 2, pp. 575–584, May 1982.Google Scholar
Rose, L., Lasaulce, S., Perlaza, S. M., and Debbah, M., “Learning equilibria with partial information in decentralized wireless networks,” IEEE Communications Magazine, vol. 49, no. 8, pp. 136–142, Aug. 2011.Google Scholar
Golub, G. H. and Van Loan, C. F., Matrix computations, Johns Hopkins University Press, Baltimore, MD, 3rd edition, 1996.Google Scholar
Lloyd, S., “Least squares quantization in PCM,” IEEE Transactions on Information Theory, vol. 28, no. 2, pp. 129–137, Mar. 1982.Google Scholar
Larrousse, B., Beaude, O., and Lasaulce, S., “Crawford-Sobel meet Lloyd-Max on the grid,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, May 2014, pp. 6127–6131.Google Scholar
Niyato, D. and Hossain, E., “Competitive spectrum sharing in cognitive radio networks: A dynamic game approach,” IEEE Transactions on Wireless Commun., vol. 7, no. 7, pp. 2651–2660, July 2008.Google Scholar
Lasaulce, S. and Tembine, H., Game theory and learning for wireless networks: Fundamentals and applications, Academic Press, Waltham, MA, 2011.Google Scholar
Fudenberg, D. and Levine, D., The theory of learning in games, MIT Press, Cambridge, MA, 1998.Google Scholar
Young, H. P., Strategic learning and its limits, Oxford University Press, London, UK, 2005.Google Scholar
Scutari, G., Palomar, D., and Barbarossa, S., “The MIMO iterative waterfilling algorithm,” IEEE Transactions on Signal Processing, vol. 57, no. 5, pp. 1917–1935, May 2009.Google Scholar
Han, Z., Niyato, D., Saad, W., Başar, T., and Hjørungnes, A., Game theory in wireless and communication networks: Theory, models and applications, Cambridge University Press, Cambridge, UK, Oct. 2011.Google Scholar
Yates, R. D., “A framework for uplink power control in cellular radio systems,” IEEE Journal on Selected Areas in Communications, vol. 13, no. 9, pp. 1341–1347, Sept. 1995.Google Scholar
Gan, L., Topcu, U., and Low, S. H., “Optimal decentralized protocol for electric vehicle charging,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 940–951, May 2013.Google Scholar
Scutari, G., Palomar, D., and Barbarossa, S., “Optimal linear precoding strategies for wideband noncooperative systems based on game theory part I: Nash equilibria,” IEEE Transactions on Signal Processing, vol. 56, no. 3, pp. 1230–1249, Mar. 2008.Google Scholar
Mertikopoulos, P., Belmega, E. V., Moustakas, A., and Lasaulce, S., “Distributed learning policies for power allocation in multiple access channels,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 1, pp. 96–106, Jan. 2012.Google Scholar
Yu, W., Ginis, G., and Cioffi, J., “Distributed multiuser power control for digital subscriber lines,” IEEE Journal on Selected Areas in Communications, vol. 20, no. 5, pp. 1105–1115, June 2002.Google Scholar
Brown, G. W., “Iterative solutions of games by fictitious play,” in Activity Analysis of Production and Allocation, Koopmans, T. C., Ed., pp. 374–376. Wiley, New York, 1951.Google Scholar
Hart, S. and Mas-Colell, A., “A simple adaptive procedure leading to correlated equilibrium,” Econometrica, vol. 68, no. 5, pp. 1127–1150, Sept. 2000.Google Scholar
Bennis, M., Perlaza, S. M., and Debbah, M., “Learning coarse correlated equilibrium in two-tier wireless networks,” in Proceedings of the IEEE International Conference on Communications (ICC), Ottawa, Canada, June 2012.Google Scholar
Sutton, R. S. and Barto, A. G., Reinforcement learning: An introduction (adaptive computation and machine learning), A Bradford Book, Mar. 1998.Google Scholar
Szita, I., Gyenes, V., and Lorincz, A., “Reinforcement learning with echo state networks,” in Proceedings of the International Conference on Artificial Neural Networks, Berlin, Germany, Sept. 2006.Google Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M., “Playing Atari with deep reinforcement learning,” in Proceedings of Annual Conference on Neural Information Processing System (NIPS), Deep Learning Workshop, Lake Tahoe, CA, Dec. 2013.Google Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D., “Human-level control through deep reinforcement learning,” Nature, vol. 528, pp. 529–533, Feb. 2015.Google Scholar
Lin, L. J., “Reinforcement learning for robots using neural networks,” Technical Report Carnegie-Mellon Univ Pittsburgh PA School of Computer Science, 1993.Google Scholar
Bush, R. R. and Mosteller, F., Stochastic models of learning, John Wiley & Sons, New York, 1st edition, 1955.Google Scholar
Leslie, S. D. and Collins, E. J., “Convergent multiple-timescales reinforcement learning algorithms in normal form games,” Annals of Applied Probability, vol. 13, no. 4, pp. 1231– 1251, 2003.Google Scholar
Zhu, Q., Tembine, H., and Başar, T., “Hybrid learning in stochastic games and its applications in network security,” in Reinforcement learning and approximate dynamic programming for feedback control, series on computational intelligence, Lewis, F. L. and Liu, D., Eds., pp. 305–329, Wiley, Hoboken, NJ, 2013.Google Scholar
Bennis, M., Perlaza, S. M., Blasco, P., Han, Z., and Poor, H. V., “Self-organization in small cell networks: A reinforcement learning approach,” IEEE Transactions on Wireless Communications, vol. 12, no. 7, pp. 3202–3212, July 2013.Google Scholar
Chen, M., Challita, U., Saad, W., Yin, C., and Debbah, M., “Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks,” arXiv:1710.02913, 2017.Google Scholar
Mandic, D. P. and Chambers, J. A., Recurrent neural networks for prediction: Learning algorithms, architectures and stability, Wiley Online Library, New York, 2001.Google Scholar
Lukosevicius, M., A practical guide to applying echo state networks, Springer, Berlin, Germany, 2012.Google Scholar
Jaeger, H. and Haas, H., “Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication,” Science, vol. 304, no. 5667, pp. 78–80, 2004.Google Scholar
Ozturk, M. C., Xu, D., and Principe, J. C., “Analysis and design of echo state networks,” Neural Computation, vol. 19, no. 1, pp. 111–138, Jan. 2007.Google Scholar
Scardapane, S., Wang, D., and Panella, M., “A decentralized training algorithm for echo state networks in distributed big data applications,” Neural Networks, vol. 78, pp. 65–74, June 2016.Google Scholar
Jaeger, H., Lukosevicius, M., Popovici, D., and Siewert, U., “Optimization and applications of echo state networks with leaky-integrator neurons,” Neural Networks, vol. 20, no. 3, pp. 335–352, May 2007.Google Scholar
Chen, M., Saad, W., and Yin, C., “Echo state networks for self-organizing resource allocation in LTE-U with uplink-downlink decoupling,” IEEE Transaction on Wireless Communications, vol. 16, no. 1, pp. 3–16, Jan. 2017.Google Scholar
Chen, M., Saad, W., and Yin, C., “Virtual reality over wireless networks: Quality-of-service model and learning-based resource management,” arXiv:1703.04209, 2017.Google Scholar
Chen, M., Saad, W., and Yin, C., “Liquid state machine learning for resource allocation in a network of cache-enabled LTE-U UAVs,” in Proceedings of IEEE Global Communication Conference, Singapore, Dec. 2017.Google Scholar
Challita, U., Saad, W. and Bettstetter, C., “Deep reinforcement learning for interference-aware path planning of cellular-connected UAVs,” in Proceedings of the International Conference on Communications, Kansas City, MO, May 2018.Google Scholar
Facchinei, F. and Pang, J., Finite-dimensional variational inequalities and complementarity problems, Springer-Verlag, New York, 2003.Google Scholar
Boyd, S. and Vandenberghe, L., Convex optimization, Cambridge University Press, New York, 2004.Google Scholar
Han, Z., Niyato, D., Saad, W., Başar, T., and Hjørungnes, A., Game theory in wireless and communication networks, Cambridge University Press, Cambridge, UK, 2011.Google Scholar
Solodov, M. V., “Constraint qualifications,” Wiley encyclopedia of operations research and management science, Cochran, J. J., Cox, L. A., Keskinocak, P., Kharoufeh, J. P., and Smith, J. C., Eds., Wiley, Hoboken, NJ, 2010.Google Scholar
Luo, Z., Pang, J., and Ralph, D., Mathematical programs with equilibrium constraints, Cambridge University Press, Cambridge, UK, 1996.Google Scholar
Facchinei, F., Jiang, H., and Qi, L., “A smoothing method for mathematical programs with equilibrium constraints,” Mathematical Programming, vol. 85, no. 1, pp. 107–134, May 1999.Google Scholar
Su, C.-L., Equilibrium problems with equilibrium constraints: Stationarities, algorithms, and applications, Ph.D. thesis, Stanford University, Stanford, CA, Sep. 2005.Google Scholar
Leyffer, S. and Munson, T., “Solving multi-leader–common-follower games,” Optimization Methods and Software, vol. 25, no. 4, pp. 601–623, 2010.Google Scholar
Tang, X., Ren, P., and Han, Z., “Hierarchical competition as equilibrium program with equilibrium constraints towards security-enhanced wireless networks,” IEEE Journal on Selected Areas in Communications, 2018.Google Scholar
Basar, T., “Equilibrium strategies in dynamic games with multi-levels of hierarchy,” Automatica, vol. 17, no. 5, pp. 749–754, 1981.Google Scholar
Basar, T., “Performance bounds for hierarchical systems under partial dynamic information,” Journal of Optimization Theory and Applications, vol. 39, no. 1, pp. 67–87, January 1983.Google Scholar
Shen, H. and Basar, T., “Optimal nonlinear pricing for a monopolistic network service provider with complete and incomplete information,” IEEE Journal on Selected Areas in Communications (JSAC) Special Issue: Non-Cooperative Behavior in Networking, vol. 25, no. 6, pp. 1216–1223, June 2007.Google Scholar
Zhang, L., Jiang, T., and Luo, K., “Dynamic spectrum allocation for the downlink of OFDMA-based hybrid-access cognitive femtocell networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 3, pp. 1772–1781, 2016.Google Scholar
Bajić, J., Milosavljević, V., Rajs, V., Slankamenac, M., and Živanov, M., “Universal wireless communication detector (UD-100)-preventing of high-tech cheating methods,” in MIPRO, 2012 Proceedings of the 35th International Convention. IEEE, 2012, pp. 237–240.Google Scholar
Mahmoud, M. E. and Shen, X., “Stimulating cooperation in multi-hop wireless networks using cheating detection system,” in INFOCOM, 2010 Proceedings IEEE. IEEE, 2010, pp. 1–9.Google Scholar
Wu, T.-C. and Wu, T.-S., “Cheating detection and cheater identification in secret sharing schemes,” IEE Proceedings-Computers and Digital Techniques, vol. 142, no. 5, pp. 367– 369, 1995.Google Scholar
Zhong, S., Chen, J. and Yang, Y. R., “Sprite: A simple, cheat-proof, credit-based system for mobile ad-hoc networks,” in INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies. IEEE, 2003, vol. 3, pp. 1987– 1997.Google Scholar
Press, W. H. and Dyson, F. J., “Iterated prisoners dilemma contains strategies that dominate any evolutionary opponent,” Proceedings of the National Academy of Sciences, vol. 109, no. 26, pp. 10409–10413, 2012.Google Scholar
Roemheld, L., “Evolutionary extortion and mischief: Zero determinant strategies in iterated 2x2 games,” arXiv preprint arXiv:1308.2576, 2013.Google Scholar
Pan, L., Hao, D., Rong, Z., and Zhou, T., “Zero-determinant strategies in the iterated public goods game,” arXiv preprint arXiv:1402.3542, 2014.Google Scholar
Al Daoud, A., Kesidis, G., and Liebeherr, J., “Zero-determinant strategies: A game-theoretic approach for sharing licensed spectrum bands,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 11, pp. 2297–2308, 2014.Google Scholar
Adami, C. and Hintze, A., “Evolutionary instability of zero-determinant strategies demonstrates that winning is not everything,” Nature Communications, vol. 4, p. 2193, 2013.Google Scholar
Zhang, H., Li, F., Niyato, D., Song, L., Jiang, T., and Han, Z., “Zero-determinant strategy for power control of small cell network,” in International Conference on Communication Systems (ICCS). IEEE, 2014, pp. 41–45.Google Scholar
Boyd, S. and Vandenberghe, L., Convex optimization, Cambridge University Press, Cambridge, UK, 2004.Google Scholar
Imhof, L. A., Fudenberg, D., and Nowak, M. A., “Tit-for-tat or win-stay, lose-shift?” Journal of Theoretical Biology, vol. 247, no. 3, pp. 574–580, 2007.Google Scholar
Golbeck, J., “Evolving strategies for the prisoners dilemma,” Advances in Intelligent Systems, Fuzzy Systems, and Evolutionary Computation, vol. 2002, pp. 299–306, 2002.Google Scholar
Chandrasekhar, V., Andrews, J. G., Muharemovic, T., Shen, Z., and Gatherer, A., “Power control in two-tier femtocell networks,” IEEE Transactions on Wireless Communications, vol. 8, no. 8, pp. 4316–4328, 2009.Google Scholar
Jo, H.-S., Mun, C., Moon, J., and Yook, J.-G., “Interference mitigation using uplink power control for two-tier femtocell networks,” IEEE Transactions on Wireless Communications, vol. 8, no. 10, pp. 4906–4910, 2009.Google Scholar
Chen, Y., Zhang, J., and Zhang, Q., “Utility-aware refunding framework for hybrid access femtocell network,” IEEE Transactions on Wireless Communications, vol. 11, no. 5, pp. 1688–1697, 2012.Google Scholar
Yi, Y., Zhang, J., Zhang, Q., and Jiang, T., “Spectrum leasing to femto service provider with hybrid access,” in INFOCOM, 2012 Proceedings IEEE. IEEE, 2012, pp. 1215–1223.Google Scholar
Deng, Z., Xu, Y., and Wang, N., “Power control game via improved utility functions of primary-secondary user in cognitive radio networks,” in Computer Science and Network Technology (ICCSNT), 2011 International Conference on. IEEE, 2011, vol. 3, pp. 1460– 1463.Google Scholar
Tang, Y., Wang, L., Grace, D., and Wei, J., “Utility based cooperative spectrum leasing in cognitive radio networks,” in Wireless Communication Systems (ISWCS), 2012 International Symposium on. IEEE, 2012, pp. 71–75.Google Scholar
Yi, Y., Zhang, J., Zhang, Q., Jiang, T., and Zhang, J., “Cooperative communication-aware spectrum leasing in cognitive radio networks,” in New frontiers in dynamic spectrum, 2010 IEEE Symposium on. IEEE, 2010, pp. 1–11.Google Scholar
Bloem, M., Alpcan, T., and Başar, T., “A Stackelberg game for power control and channel allocation in cognitive radio networks,” in Proceedings of the 2nd International Conference on Performance Evaluation Methodologies and Tools. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2007, p. 4.Google Scholar
Islam, H., Liang, Y.-C., and Hoang, A. T., “Joint power control and beamforming for cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 7, pp. 2415–2419, 2008.Google Scholar
Hoang, A. T., Liang, Y.-C., and Islam, M. H., “Power control and channel allocation in cognitive radio networks with primary users’ cooperation,” IEEE Transactions on Mobile Computing, vol. 9, no. 3, pp. 348–360, 2010.Google Scholar
Li, H., Cheng, X., Li, K., Xing, X., and Jing, T., “Utility-based cooperative spectrum sensing scheduling in cognitive radio networks,” in INFOCOM, 2013 Proceedings IEEE. IEEE, 2013, pp. 165–169.Google Scholar
Yu, C.-H., Tirkkonen, O., Doppler, K., and Ribeiro, C., “On the performance of device-to-device underlay communication with simple power control,” in Vehicular Technology Conference, 2009. VTC Spring 2009. IEEE 69th. IEEE, 2009.Google Scholar
Yu, C.-H., Tirkkonen, O., Doppler, K., and Ribeiro, C., “Power optimization of device-to-device communication underlaying cellular communication,” in Communications, 2009. ICC’09. IEEE International Conference on. IEEE, 2009.Google Scholar
Wang, F., Song, L., Han, Z., Zhao, Q., and Wang, X., “Joint scheduling and resource allocation for device-to-device underlay communication,” in Wireless Communications and Networking Conference (WCNC), 2013 IEEE. IEEE, 2013, pp. 134–139.Google Scholar
He, Y., Luan, X., Wang, J., Feng, M., and Wu, J., “Power allocation for D2D communications in heterogeneous networks,” in Advanced Communication Technology (ICACT), 2014 16th International Conference on. IEEE, 2014, pp. 1041–1044.Google Scholar
Maschler, M., Solan, E., and Zamir, S., Game theory, Cambridge University Press, Cambridge, UK, 2013.Google Scholar
Aziz, H., Brandt, F., Elkind, E., and Skowron, P., “Computational social choice: The first ten years and beyond,” Computer Science Today, vol. 10000, 2017.Google Scholar
Gehrlein, W. V., “Condorcet’s paradox,” Theory and Decision, vol. 15, no. 2, pp. 161–197, 1983.Google Scholar
S. University, “Social choice theory,” https://web.stanford.edu/class/symbsys150/social-choice-theory-5-8.html.Google Scholar
[240]Feiwel, G. R., Arrow and the ascent of modern economic theory, New York University Press, New York, 1987.Google Scholar
Eckert, D. and Herzberg, F., “The birth of social choice theory from the spirit of mathematical logic: Arrows theorem in the framework of model theory,” www100.uni-graz.at/vwlwww/forschung/RePEc/wpaper/2016-04.pdf.Google Scholar
Sen, A., “Social choice theory: A re-examination,” Econometrica: Journal of the Econometric Society, pp. 53–89, 1977.Google Scholar
Li, W., Fu, X., Huang, Q., and Liu, L., “Evaluating on online services based on social choice theory,” in Control and Decision Conference (CCDC), 2016 Chinese. IEEE, 2016, pp. 6512–6517.Google Scholar
Geist, C. and Peters, D., “Computer-aided methods for social choice theory,” Trends in Computational Social Choice, p. 249, 2017.Google Scholar
Anshelevich, E. and Postl, J., “Randomized social choice functions under metric preferences,” in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. AAAI Press, 2016, pp. 46–52.Google Scholar
Aziz, H., “Maximal recursive rule: A new social decision scheme,” in Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. AAAI Press, 2013, pp. 34–40.Google Scholar
Minerva, R., Biru, A., and Rotondi, D., “Towards a definition of the Internet of Things (IoT),” Internet Initiative, vol. 1, 2015.Google Scholar
Gubbi, J., Buyya, R., Marusic, S., and Palaniswami, M., “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.Google Scholar
Sundmaeker, H., Guillemin, P., Friess, P., and Woelfflé, S., “Vision and challenges for realising the Internet of Things,” Cluster of European Research Projects on the Internet of Things, European Commision, vol. 3, no. 3, pp. 34–36, 2010.Google Scholar
Chui, M., Löffler, M., and Roberts, R., “The Internet of Things.” McKinsey Quarterly, 2011.Google Scholar
Stankovic, J. A., “Research directions for the Internet of Things,” Internet of Things Journal, vol. 1, no. 1, pp. 3–9, 2014.Google Scholar
Borgia, E., “The Internet of Things vision: Key features, applications and open issues,” Computer Communications, vol. 54, pp. 1–31, 2014.Google Scholar
Atzori, L., Iera, A., and Morabito, G., “The Internet of Things: A survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010.Google Scholar
Uckelmann, D., Harrison, M., and Michahelles, F., “An architectural approach towards the future internet of things,” in Architecting the Internet of Things, pp. 1–24. Springer, Heidelberg, 2011.Google Scholar
Gluhak, A., Krco, S., Nati, M., Pfisterer, D., Mitton, N., and Razafindralambo, T., “A survey on facilities for experimental internet of things research,” Communications Magazine, vol. 49, no. 11, 2011.Google Scholar
Luong, N. C., Hoang, D. T., Wang, P., Niyato, D., Kim, D. I., and Han, Z., “Data collection and wireless communication in Internet of Things (IoT) using economic analysis and pricing models: A survey,” Communications Surveys & Tutorials, vol. 18, no. 4, pp. 2546–2590, 2016.Google Scholar
Lee, J.-S. and Hoh, B., “Sell your experiences: A market mechanism based incentive for participatory sensing,” in International Conference on Pervasive Computing and Communications (PerCom). IEEE, 2010, pp. 60–68.Google Scholar
Mobile, W., “Real-time maps and traffic information based on the wisdom of the crowd,” http://solsie.com/2009/09/real-time-maps-and-traffic-information-based-on-the-wisdom-of-the-crowd/Google Scholar
Lee, J.-S. and Hoh, B., “Dynamic pricing incentive for participatory sensing,” Pervasive and Mobile Computing, vol. 6, no. 6, pp. 693–708, 2010.Google Scholar
Adeel, U., Yang, S., and McCann, J. A., “Self-optimizing citizen-centric mobile urban sensing systems,” in ICAC, 2014, pp. 161–167.Google Scholar
Al-Fagih, A. E., Al-Turjman, F. M., and Hassanein, H. S., “Online heuristics for monetary-based courier relaying in rfid-sensor networks,” in International Conference on Communications (ICC). IEEE, 2013, pp. 1700–1704.Google Scholar
Broch, J., Maltz, D. A., Johnson, D. B., Hu, Y.-C., and Jetcheva, J., “A performance comparison of multi-hop wireless ad hoc network routing protocols,” in International Conference on Mobile Computing and Networking. ACM, 1998, pp. 85–97.Google Scholar
Liu, Q., Xian, X., and Wu, T., “Game theoretic approach in routing protocol for cooperative wireless sensor networks,” in International Conference in Swarm Intelligence. Springer, New York, 2011, pp. 207–217.Google Scholar
Heinzelman, W. R., Chandrakasan, A., and Balakrishnan, H., “Energy-efficient communication protocol for wireless microsensor networks,” in International Conference on System Sciences. IEEE, 2000, pp. 1–10.Google Scholar
Ee, C. T. and Bajcsy, R., “Congestion control and fairness for many-to-one routing in sensor networks,” in International Conference on Embedded Networked Sensor Systems. ACM, 2004, pp. 148–161.Google Scholar
Qiu, X., Liu, H., Li, D., Yick, J., Ghosal, D., and Mukherjee, B., “Efficient aggregation of multiple classes of information in wireless sensor networks,” Sensors, vol. 9, no. 10, pp. 8083–8108, 2009.Google Scholar
Jain, R., The art of computer systems performance analysis: Techniques for experimental design, measurement, simulation, and modeling, John Wiley & Sons, New York, 1990.Google Scholar
Zhou, Y., Lyu, M. R., Liu, J., and Wang, H., “Port: A price-oriented reliable transport protocol for wireless sensor networks,” in International Symposium on Software Reliability Engineering (ISSRE). IEEE, 2005, pp. 1–10.Google Scholar
Zhang, Y., Lee, C., Niyato, D., and Wang, P., “Auction approaches for resource allocation in wireless systems: A survey,” Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1020–1041, 2013.Google Scholar
Charlish, A., Woodbridge, K., and Griffiths, H., “Multi-target tracking control using continuous double auction parameter selection,” in International Conference on Information Fusion (FUSION). IEEE, 2012, pp. 1269–1276.Google Scholar
Blackman, S. and van Keuk, G., “On phased-array radar tracking and parameter control,” Transactions on Aerospace and Electronic Systems, vol. 29, no. 1, 1993.Google Scholar
Masazade, E. and Varshney, P. K., “A market based dynamic bit allocation scheme for target tracking in wireless sensor networks,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013, pp. 4207–4211.Google Scholar
Edalat, N., Xiao, W., Tham, C.-K., Keikha, E., and Ong, L.-L., “A price-based adaptive task allocation for wireless sensor network,” in International Conference on Mobile Adhoc and Sensor Systems (MASS). IEEE, 2009, pp. 888–893.Google Scholar
Edalat, N., Tham, C.-K., and Xiao, W., “An auction-based strategy for distributed task allocation in wireless sensor networks,” Computer Communications, vol. 35, no. 8, pp. 916–928, 2012.Google Scholar
Nan, W., Guo, B., Huangfu, S., Yu, Z., Chen, H., and Zhou, X., “A cross-space, multi-interaction-based dynamic incentive mechanism for mobile crowd sensing,” in International Conference on Ubiquitous Intelligence and Computing, International Conference on Autonomic and Trusted Computing, International Conference on Scalable Computing and Communications, and Its Associated Workshops (UTC-ATC-ScalCom). IEEE, 2014, pp. 179–186.Google Scholar
Tian, Y., Gu, Y., Ekici, E., and Ozguner, F., “Dynamic critical-path task mapping and scheduling for collaborative in-network processing in multi-hop wireless sensor networks,” in International Conference on Parallel Processing (ICPP) Workshops. IEEE, 2006, pp. 1–8.Google Scholar
Shah-Mansouri, H. and Wong, V. W., “Profit maximization in mobile crowdsourcing: A truthful auction mechanism,” in International Conference on Communications (ICC). IEEE, 2015, pp. 3216–3221.Google Scholar
Wang, G., Cao, G., and LaPorta, T., “A bidding protocol for deploying mobile sensors,” in International Conference on Network Protocols. IEEE, 2003, pp. 315–324.Google Scholar
Wang, G., Cao, G., and La Porta, T. F., “Movement-assisted sensor deployment,” Transactions on Mobile Computing, vol. 5, no. 6, pp. 640–652, 2006.Google Scholar
Wang, G., Cao, G., Berman, P., and La Porta, T. F., “Bidding protocols for deploying mobile sensors,” Transactions on Mobile Computing, vol. 6, no. 5, pp. 563–576, 2007.Google Scholar
Wang, G., Cao, G., and La Porta, T., “Proxy-based sensor deployment for mobile sensor networks,” in International Conference on Mobile Ad-hoc and Sensor Systems. IEEE, 2004, pp. 493–502.Google Scholar
Kelly, F. P., Maulloo, A. K., and Tan, D. K., “Rate control for communication networks: Shadow prices, proportional fairness and stability,” Journal of the Operational Research Society, vol. 49, no. 3, pp. 237–252, 1998.Google Scholar
Naderan, M., Dehghan, M., and Pedram, H., “A distributed dual-based algorithm for multi-target coverage in wireless sensor networks,” in International Symposium on Computer Networks and Distributed Systems (CNDS). IEEE, 2011, pp. 204–209.Google Scholar
Chen, J., Zang, C., Liang, W., and Yu, H., “Auction-based dynamic coalition for single target tracking in wireless sensor networks,” in World Congress on Intelligent Control and Automation (WCICA). IEEE, 2006, vol. 1, pp. 94–98.Google Scholar
Soh, L.-K. and Tsatsoulis, C., “Reflective negotiating agents for real-time multisensor target tracking,” in International Joint Conference on Artificial Intelligence. Lawrence Erlbaum, Mahwah, NJ, 2001, vol. 17, pp. 1121–1127, 2001.Google Scholar
Liang, S., Bhuiyan, M. Z. A., and Wang, G., “Auction-based adaptive sensor activation algorithm for target tracking in WSNs,” in International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2011, pp. 1217–1223.Google Scholar
Zhou, Y., “An efficient least-squares trilateration algorithm for mobile robot localization,” in International Conference on Intelligent Robots and Systems (IROS). IEEE, 2009, pp. 3474–3479.Google Scholar
Zheng, J., Bhuiyan, M. Z. A., Liang, S., Xing, X., and Wang, G., “Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks,” Future Generation Computer Systems, vol. 39, pp. 88–99, 2014.Google Scholar
Wu, W., Wang, G.-h., Li, Z.-x., and Liu, B., “Airborne sensor management and target tracking based on market theory,” in International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2014, pp. 350–354.Google Scholar
Barr, S., Liu, B., and Wang, J., “Underwater sensor barriers with auction algorithms,” in International Conference on Computer Communications and Networks (ICCCN). IEEE, 2009, pp. 1–6.Google Scholar
Kuhn, H. W., “The Hungarian method for the assignment problem,” Naval Research Logistics (NRL), vol. 2, no. 1-2, pp. 83–97, 1955.Google Scholar
Barr, S., Liu, B., and Wang, J., “Constructing underwater sensor based barriers using distributed auctions,” in Military Communications Conference (MILCOM). IEEE, 2009, pp. 1–7.Google Scholar
Kapadia, A., Kotz, D., and Triandopoulos, N., “Opportunistic sensing: Security challenges for the new paradigm,” in International Conference on Communication Systems and Networks (COMSNETS) Workshops. IEEE, 2009, pp. 1–10.Google Scholar
Singla, A. and Krause, A., “Incentives for privacy tradeoff in community sensing,” in Conference on Human Computation and Crowdsourcing. 2013, AAAI.Google Scholar
Sun, J. and Ma, H., “Privacy-preserving verifiable incentive mechanism for online crowdsourcing markets,” in International Conference on Computer Communication and Networks (ICCCN). IEEE, 2014, pp. 1–8.Google Scholar
Rabin, M. O. and Thorpe, C., “Time-lapse cryptography,” Digital Access to Scholarship at Harvard, 2006.Google Scholar
Camenisch, J. L., Piveteau, J.-M., and Stadler, M. A., “Blind signatures based on the discrete logarithm problem,” in Workshop on the Theory and Application of of Cryptographic Techniques. Springer, Heidelberg, 1994, pp. 428–432.Google Scholar
Aggarwal, C. C. and Philip, S. Y., “A general survey of privacy-preserving data mining models and algorithms,” in Privacy-preserving data mining, pp. 11–52. Springer, Heidelberg, 2008.Google Scholar
Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D., Amde, M., Owen, S. et al., “MLlib: Machine learning in Apache Spark,” The Journal of Machine Learning Research, vol. 17, no. 1, pp. 1235–1241, 2016.Google Scholar
Strutz, T., Data fitting and uncertainty: A practical introduction to weighted least squares and beyond, Vieweg and Teubner, Weisbaden, 2010.Google Scholar
Sweeney, L., “k-anonymity: A model for protecting privacy,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 05, pp. 557–570, 2002.Google Scholar
Machanavajjhala, A., Gehrke, J., Kifer, D., and Venkitasubramaniam, M., “l-diversity: Privacy beyond k-anonymity,” in International Conference on Data Engineering (ICDE). IEEE, 2006, pp. 24–24.Google Scholar
Dwork, C., “Differential privacy: A survey of results,” in International Conference on Theory and Applications of Models of Computation. Springer, Heidelberg, 2008, pp. 1–19.Google Scholar
Go, A., Bhayani, R., and Huang, L., “Twitter sentiment classification using distant supervision,” CS224N Project Report, Stanford, vol. 1, no. 12, 2009.Google Scholar
Kwapisz, J. R., Weiss, G. M., and Moore, S. A., “Activity recognition using cell phone accelerometers,” ACM SigKDD Explorations Newsletter, vol. 12, no. 2, pp. 74–82, 2011.Google Scholar
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y., Deep learning, vol. 1, MIT Press, Cambridge, MA, 2016.Google Scholar
Polikar, R., “Ensemble based systems in decision making,” Circuits and Systems Magazine, vol. 6, no. 3, pp. 21–45, 2006.Google Scholar
Chong, E. and Żak, S., “Nonlinear constrained optimization,” An Introduction to Optimization, pp. 423–477, Wiley, Hoboken, NJ, 2008.Google Scholar
Venkatesh, R. and Kamakura, W., “Optimal bundling and pricing under a monopoly: Contrasting complements and substitutes from independently valued products,” The Journal of Business, vol. 76, no. 2, pp. 211–231, 2003.Google Scholar
Zhang, Y. and van der Schaar, M., “Reputation-based incentive protocols in crowdsourcing applications,” in International Conference on Computer Communications (INFOCOM). IEEE, 2012, pp. 2140–2148.Google Scholar
”Google knows nearly every Wi-Fi password in the world,” www.computerworld.com/article/2474851/android-google-knows-nearly-every-wi-fi-password-in-the-world.html.Google Scholar
”Location based services market to reach $43.3bn by 2019, driven by context aware mobile services,” www.juniperresearch.com/press-release/context-and-location-based-services-pr2.Google Scholar
Zhang, X., Yang, Z., Zhou, Z., Cai, H., Chen, L., and Li, X., “Free market of crowdsourcing: Incentive mechanism design for mobile sensing,” Transactions on Parallel and Distributed Systems, vol. 25, no. 12, pp. 3190–3200, 2014.Google Scholar
Feng, Z., Zhu, Y., Zhang, Q., Zhu, H., Yu, J., Cao, J., and Ni, L. M., “Towards truthful mechanisms for mobile crowdsourcing with dynamic smartphones,” in International Conference on Distributed Computing Systems (ICDCS). IEEE, 2014, pp. 11–20.Google Scholar
Zhao, D., Li, X.-Y., and Ma, H., “How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint,” in International Conference on Computer Communications(INFOCOM). IEEE, 2014, pp. 1213–1221.Google Scholar
Kong, Q., Yu, J., Lu, R., and Zhang, Q., “Incentive mechanism design for crowdsourcing-based cooperative transmission,” in Global Communications Conference (GLOBECOM). IEEE, 2014, pp. 4904–4909.Google Scholar
Luo, T., Kanhere, S. S., and Tan, H.-P., “Optimal prizes for all-pay contests in heterogeneous crowdsourcing,” in International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2014, pp. 136–144.Google Scholar
Sánchez-Charles, D., Nin, J., Solé, M., and Muntés-Mulero, V., “Worker ranking determination in crowdsourcing platforms using aggregation functions,” in International Conference on Fuzzy Systems (FUZZ). IEEE, 2014, pp. 1801–1808.Google Scholar
Hossfeld, T., Keimel, C., and Timmerer, C., “Crowdsourcing quality-of-experience assessments,” Computer, vol. 47, no. 9, pp. 98–102, 2014.Google Scholar
Zhang, X., Xue, G., Yu, R., Yang, D., and Tang, J., “Keep your promise: Mechanism design against free-riding and false-reporting in crowdsourcing,” Internet of Things Journal, vol. 2, no. 6, pp. 562–572, 2015.Google Scholar
Xiao, Y., Zhang, Y. and van der Schaar, M., “Socially-optimal design of crowdsourcing platforms with reputation update errors,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013, pp. 5263–5267.Google Scholar
Yang, D., Xue, G., Fang, X., and Tang, J., “Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones,” Transactions on Networking (TON), vol. 24, no. 3, pp. 1732–1744, 2016.Google Scholar
Pulla, V. S., Jammi, C. S., Tiwari, P., Gjoka, M., and Markopoulou, A., “Questcrowd: A location-based question answering system with participation incentives,” in International Conference on Computer Communications (INFOCOM) Workshops. IEEE, 2013, pp. 75–76.Google Scholar
Xie, H., Lui, J. C., Jiang, J. W., and Chen, W., “Incentive mechanism and protocol design for crowdsourcing systems,” in Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2014, pp. 140–147.Google Scholar
Chawla, S., Hartline, J. D., and Sivan, B., “Optimal crowdsourcing contests,” Games and Economic Behavior, 2015.Google Scholar
Luo, T., Tan, H.-P., and Xia, L., “Profit-maximizing incentive for participatory sensing,” in International Conference on Computer Communications (INFOCOM). IEEE, 2014, pp. 127–135.Google Scholar
Duan, L., Kubo, T., Sugiyama, K., Huang, J., Hasegawa, T., and Walrand, J., “Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing,” in International Conference on Computer Communications (INFOCOM). IEEE, 2012, pp. 1701–1709.Google Scholar
Zhang, Y. and Han, Z., “Incentive mechanism in crowdsourcing with moral hazard,” in Contract theory for wireless networks, pp. 43–56. Springer, Heidelberg, 2017.Google Scholar
Chen, H., Ham, S. H., and Lim, N., “Designing multiperson tournaments with asymmetric contestants: An experimental study,” Management Science, vol. 57, no. 5, pp. 864–883, 2011.Google Scholar
Archak, N. and Sundararajan, A., “Optimal design of crowdsourcing contests,” International Conference on Information Systems (ICIS), p. 200, 2009.Google Scholar
Green, J. R. and Stokey, N. L., “A comparison of tournaments and contracts,” Journal of Political Economy, vol. 91, no. 3, pp. 349–364, 1983.Google Scholar
Murphy, W. H., Dacin, P. A., and Ford, N. M., “Sales contest effectiveness: An examination of sales contest design preferences of field sales forces,” Journal of the Academy of Marketing Science, vol. 32, no. 2, pp. 127–143, 2004.Google Scholar
Poujol, F. J. and Tanner Jr, J. F., “The impact of contests on salespeople’s customer orientation: An application of tournament theory,” Journal of Personal Selling & Sales Management, vol. 30, no. 1, pp. 33–46, 2010.Google Scholar
Budde, J., “Information in tournaments under limited liability,” Journal of Mathematical Economics, vol. 45, no. 1–2, pp. 59–72, 2009.Google Scholar
Nalebuff, B. J. and Stiglitz, J. E., “Prizes and incentives: Towards a general theory of compensation and competition,” The Bell Journal of Economics, pp. 21–43, 1983.Google Scholar
Harbring, C. and Irlenbusch, B., “An experimental study on tournament design,” Labour Economics, vol. 10, no. 4, pp. 443–464, 2003.Google Scholar
Ryvkin, D. and Ortmann, A., “The predictive power of three prominent tournament formats,” Management Science, vol. 54, no. 3, pp. 492–504, 2008.Google Scholar
Bhattacharya, S. and Guasch, J. L., “Heterogeneity, tournaments, and hierarchies,” Journal of Political Economy, vol. 96, no. 4, pp. 867–881, 1988.Google Scholar
Lazear, E. P. and Rosen, S., “Rank-order tournaments as optimum labor contracts,” Journal of Political Economy, vol. 89, no. 5, pp. 841–864, 1981.Google Scholar
Kalra, A. and Shi, M., “Designing optimal sales contests: A theoretical perspective,” Marketing Science, vol. 20, no. 2, pp. 170–193, 2001.Google Scholar
Liang, C. and Yu, F. R., “Wireless virtualization for next generation mobile cellular networks,” Wireless Communications, vol. 22, no. 1, pp. 61–69, 2015.Google Scholar
Liang, C. and Yu, F. R., “Wireless network virtualization: A survey, some research issues and challenges,” Communications Surveys & Tutorials, vol. 17, no. 1, pp. 358–380, 2015.Google Scholar
Bari, M. F., Boutaba, R., Esteves, R., Granville, L. Z., Podlesny, M., Rabbani, M. G., Zhang, Q., and Zhani, M. F., “Data center network virtualization: A survey,” Communications Surveys & Tutorials, vol. 15, no. 2, pp. 909–928, 2013.Google Scholar
Celentano, J., “US wireless capex looking up,” www.aglmediagroup.com/u-s-wireless-capital-expenditures-looking-up/, 2015.Google Scholar
Chase, J. and Niyato, D., “Joint optimization of resource provisioning in cloud computing,” Transactions on Services Computing, vol. 10, no. 3, pp. 396–409, 2017.Google Scholar
Hart, O. and Moore, J., “Property rights and the nature of the firm,” Journal of Political Economy, vol. 98, no. 6, pp. 1119–1158, 1990.Google Scholar
Shapley, L. S., “Stochastic games,” Proceedings of the National Academy of Sciences, vol. 39, no. 10, pp. 1095–1100, 1953.Google Scholar
Grossman, S. J. and Hart, O. D., “The costs and benefits of ownership: A theory of vertical and lateral integration,” Journal of Political Economy, vol. 94, no. 4, pp. 691–719, 1986.Google Scholar
Bolton, P. and Whinston, M. D., “Incomplete contracts, vertical integration, and supply assurance,” The Review of Economic Studies, vol. 60, no. 1, pp. 121–148, 1993.Google Scholar
Luong, N. C., Wang, P., Niyato, D., Wen, Y., and Han, Z., “Resource management in cloud networking using economic analysis and pricing models: A survey,” Communications Surveys & Tutorials, vol. 19, no. 2, pp. 954–1001, 2017.Google Scholar
Murray, P., Sefidcon, A., Steinert, R., Fusenig, V., and Carapinha, J., “Cloud networking: An infrastructure service architecture for the wide area,” in Future Network & Mobile Summit (FutureNetw). IEEE, 2012, pp. 1–8.Google Scholar
Mouftah, H. T., Communication Infrastructures for Cloud Computing, IGI Global, Hershey, PA, 2013.Google Scholar
Duan, Q., Yan, Y., and Vasilakos, A. V., “A survey on service-oriented network virtualization toward convergence of networking and cloud computing,” Transactions on Network and Service Management, vol. 9, no. 4, pp. 373–392, 2012.Google Scholar
Xiang, X., Lin, C., Chen, F., and Chen, X., “Greening geo-distributed data centers by joint optimization of request routing and virtual machine scheduling,” in International Conference on Utility and Cloud Computing (UCC). IEEE, 2014, pp. 1–10.Google Scholar
Bitar, N., Gringeri, S., and Xia, T. J., “Technologies and protocols for data center and cloud networking,” Communications Magazine, vol. 51, no. 9, pp. 24–31, 2013.Google Scholar
Levin, A. and Massonet, P., “Enabling federated cloud networking,” in International Systems and Storage Conference (SYSTOR), ACM, 2015, p. 23.Google Scholar
Jamakovic, A., Bohnert, T. M., and Karagiannis, G., “Mobile cloud networking: Mobile network, compute, and storage as one service on-demand,” in The Future Internet Assembly. Springer, Heidelberg, 2013, pp. 356–358.Google Scholar
Karagiannis, G., Jamakovic, A., Edmonds, A., Parada, C., Metsch, T., Pichon, D., Corici, M., Ruffino, S., Gomes, A., Crosta, P. S. et al., “Mobile cloud networking: Virtualisation of cellular networks,” in International Conference on Telecommunications (ICT). IEEE, 2014, pp. 410–415.Google Scholar
Lewis, G., Echeverría, S., Simanta, S., Bradshaw, B., and Root, J., “Tactical cloudlets: Moving cloud computing to the edge,” in Military Communications Conference (MILCOM). IEEE, 2014, pp. 1440–1446.Google Scholar
Ahlgren, B., Aranda, P. A., Chemouil, P., Oueslati, S., Correia, L. M., Karl, H., Söllner, M., and Welin, A., “Content, connectivity, and cloud: Ingredients for the network of the future,” Communications Magazine, vol. 49, no. 7, pp. 62–70, 2011.Google Scholar
Beck, M. T., Werner, M., Feld, S., and Schimper, S., “Mobile edge computing: A taxonomy,” in International Conference on Advances in Future Internet. CiteSeer, University Park, PA, 2014, pp. 48–55.Google Scholar
Carella, G., Edmonds, A., Dudouet, F., Corici, M., Sousa, B., and Yousaf, Z., “Mobile cloud networking: From cloud, through NFV and beyond,” in Conference on Network Function Virtualization and Software Defined Network (NFV-SDN). IEEE, 2015, pp. 7–8.Google Scholar
Rost, P., Bernardos, C. J., De Domenico, A., Di Girolamo, M., Lalam, M., Maeder, A., Sabella, D., and Wübben, D., “Cloud technologies for flexible 5g radio access networks,” Communications Magazine, vol. 52, no. 5, pp. 68–76, 2014.Google Scholar
Peng, M., Wang, C., Lau, V., and Poor, H. V., “Fronthaul-constrained cloud radio access networks: Insights and challenges,” Wireless Communications, vol. 22, no. 2, pp. 152–160, 2015.Google Scholar
Sabella, D., Rost, P., Sheng, Y., Pateromichelakis, E., Salim, U., Guitton-Ouhamou, P., Di Girolamo, M., and Giuliani, G., “RAN as a service: Challenges of designing a flexible RAN architecture in a cloud-based heterogeneous mobile network,” in Future Network and Mobile Summit (FutureNetworkSummit). IEEE, 2013, pp. 1–8.Google Scholar
Ahmed, A. and Ahmed, E., “A survey on mobile edge computing,” in International Conference on Intelligent Systems and Control (ISCO), 01 2016, pp. 1–8.Google Scholar
Gui, Y., Zheng, Z., Wu, F., Gao, X., and Chen, G., “Soar: Strategy-proof auction mechanisms for distributed cloud bandwidth reservation,” in International Conference on Communication Systems (ICCS). IEEE, 2014, pp. 162–166.Google Scholar
Shi, W., Wu, C., and Li, Z., “A Shapley-value mechanism for bandwidth on demand between datacenters,” Transactions on Cloud Computing, 2015.Google Scholar
Tan, W. K., Divakaran, D. M., and Gurusamy, M., “Uniform price auction for allocation of dynamic cloud bandwidth,” in International Conference on Communications (ICC). IEEE, 2014, pp. 2944–2949.Google Scholar
Guo, J., Liu, F., Zeng, D., Lui, J. C. and Jin, H., “A cooperative game based allocation for sharing data center networks,” in International Conference on Computer Communications (INFOCOM). IEEE, 2013, pp. 2139–2147.Google Scholar
Li, W., Guo, D., Li, K., Qi, H., and Zhang, J., “iDaaD: Inter-datacenter network as a service,” Transactions on Parallel and Distributed Systems, 2015.Google Scholar
Nikaein, N., Schiller, E., Favraud, R., Katsalis, K., Stavropoulos, D., Alyafawi, I., Zhao, Z., Braun, T., and Korakis, T., “Network store: Exploring slicing in future 5g networks,” in International Workshop on Mobility in the Evolving Internet Architecture. ACM, 2015, pp. 8–13.Google Scholar
Wang, C., Yuan, Y., and Wan, C., “Lease data center in the light of network resources: An economic model,” in International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC). IEEE, pp. 606–610, 2014.Google Scholar
Zhang, Q., Zhani, M. F., Zhang, S., Zhu, Q., Boutaba, R., and Hellerstein, J. L., “Dynamic energy-aware capacity provisioning for cloud computing environments,” in International Conference on Autonomic Computing. ACM, 2012, pp. 145–154.Google Scholar
Zhang, Q., Zhu, Q., Zhani, M. F., Boutaba, R., and Hellerstein, J. L., “Dynamic service placement in geographically distributed clouds,” Journal on Selected Areas in Communications (JSAC), vol. 31, no. 12, pp. 762–772, 2013.Google Scholar
Zheng, Z., Gui, Y., Wu, F., and Chen, G., “Star: strategy-proof double auctions for multi-cloud, multi-tenant bandwidth reservation,” Transactions on Computers, vol. 64, no. 7, pp. 2071–2083, 2015.Google Scholar
Forde, T. K., Macaluso, I., and Doyle, L. E., “Exclusive sharing & virtualization of the cellular network,” in International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). IEEE, 2011, pp. 337–348.Google Scholar
Misra, S., Das, S., Khatua, M., and Obaidat, M. S., “QoS-guaranteed bandwidth shifting and redistribution in mobile cloud environment,” Transactions on Cloud Computing, vol. 2, no. 2, pp. 181–193, 2014.Google Scholar
Das, S., Khatua, M., Misra, S., and Obaidat, M., “Quality-assured secured load sharing in mobile cloud networking environment,” Transactions on Cloud Computing, 2015.Google Scholar
Zhang, J., Xiong, T., and Lou, W., “Community clinic: Economizing mobile cloud service cost via cloudlet group,” in International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, pp. 208–216, 2014.Google Scholar
Jin, A.-L., Song, W., Wang, P., Niyato, D., and Ju, P., “Auction mechanisms toward efficient resource sharing for cloudlets in mobile cloud computing,” Transactions on Services Computing, vol. 9, no. 6, pp. 895–909, 2016.Google Scholar
Jin, A.-L., Song, W., and Zhuang, W., “Auction-based resource allocation for sharing cloudlets in mobile cloud computing,” Transactions on Emerging Topics in Computing, 2015.Google Scholar
Di, S., Wang, C.-L., Cheng, L., and Chen, L., “Social-optimized win-win resource allocation for self-organizing cloud,” in International Conference on Cloud and Service Computing (CSC). IEEE, 2011, pp. 251–258.Google Scholar
Teng, F. and Magoules, F., “Resource pricing and equilibrium allocation policy in cloud computing,” in International Conference on Computer and information technology (CIT). IEEE, 2010, pp. 195–202.Google Scholar
Khan, A. M., Vilaça, X., Rodrigues, L., and Freitag, F., “Towards incentive-compatible pricing for bandwidth reservation in community network clouds,” in International Conference on Grid Economics and Business Models. Springer, Heidelberg, 2015, pp. 251–264.Google Scholar
Zhao, J., Chu, X., Liu, H., Leung, Y.-W., and Li, Z., “Online procurement auctions for resource pooling in client-assisted cloud storage systems,” in International Conference on Computer Communications (INFOCOM). IEEE, 2015, pp. 576–584.Google Scholar
Wu, X., Liu, M., Dou, W., Gao, L., and Yu, S., “A scalable and automatic mechanism for resource allocation in self-organizing cloud,” Peer-to-peer networking and applications, vol. 9, no. 1, pp. 28–41, 2016.Google Scholar
Khethavath, P., Thomas, J., Chan-Tin, E., and Liu, H., “Introducing a distributed cloud architecture with efficient resource discovery and optimal resource allocation,” in World Congress on Services (SERVICES). IEEE, 2013, pp. 386–392.Google Scholar
Chard, K., Bubendorfer, K., Caton, S., and Rana, O. F., “Social cloud computing: A vision for socially motivated resource sharing,” Transactions on Services Computing, vol. 5, no. 4, pp. 551–563, 2012.Google Scholar
Khan, M. A., “A service framework for emerging markets,” in International Conference on Telecommunications (ICT). IEEE, 2014, pp. 272–276.Google Scholar
Cong, X., Shuang, K., Su, S., Yang, F., and Zi, L., “LBAs: An effective pricing mechanism towards video migration in cloud-assisted VoD system,” Computer Networks, vol. 64, pp. 15–25, 2014.Google Scholar
Niu, D., Feng, C., and Li, B., “Pricing cloud bandwidth reservations under demand uncertainty,” in SIGMETRICS performance evaluation review. ACM, 2012, vol. 40, pp. 151–162.Google Scholar
Lin, Y. and Shen, H., “Autotune: Game-based adaptive bitrate streaming in P2P-assisted cloud-based VoD systems,” in International Conference on Peer-to-Peer Computing (P2P). IEEE, 2015, pp. 1–10.Google Scholar
Feng, Y., Li, B., and Li, B., “Peer-assisted VoD prefetching in double auction markets,” in International Conference on Network Protocols (ICNP). IEEE, 2010, pp. 275–284.Google Scholar
Chakareski, J., “Cost and profit driven cloud-P2P interaction,” Peer-to-Peer Networking and Applications, vol. 8, no. 2, pp. 244–259, 2015.Google Scholar
Nan, G., Mao, Z., Yu, M., Li, M., Wang, H., and Zhang, Y., “Stackelberg game for bandwidth allocation in cloud-based wireless live-streaming social networks,” Systems Journal, vol. 8, no. 1, pp. 256–267, 2014.Google Scholar
Nan, G., Mao, Z., Li, M., Zhang, Y., Gjessing, S., Wang, H., and Guizani, M., “Distributed resource allocation in cloud-based wireless multimedia social networks,” Network, vol. 28, no. 4, pp. 74–80, 2014.Google Scholar
Ding, J., Yu, R., Zhang, Y., Gjessing, S., and Tsang, D. H., “Service provider competition and cooperation in cloud-based software defined wireless networks,” Communications Magazine, vol. 53, no. 11, pp. 134–140, 2015.Google Scholar
Krishnan, S. S. and Sitaraman, R. K., “Video stream quality impacts viewer behavior: inferring causality using quasi-experimental designs,” Transactions on Networking, vol. 21, no. 6, pp. 2001–2014, 2013.Google Scholar
Birge, J. R. and Louveaux, F., Introduction to stochastic programming, Springer Science & Business Media, New York, 2011.Google Scholar
Bertsimas, D. and Sim, M., “The price of robustness,” Operations Research, vol. 52, no. 1, pp. 35–53, 2004.Google Scholar
Goyal, S. and Vega-Redondo, F., “Network formation and social coordination,” Games and Economic Behavior, vol. 50, no. 2, pp. 178–207, 2005.Google Scholar
Gandhi, A., “The stochastic response dynamic: A new approach to learning and computing equilibrium in continuous games,” Technical Report, 2012.Google Scholar
Index, C. V. N., “Cisco visual networking index: global mobile data traffic forecast update, 2014–2019,” Technical Report, 2015.Google Scholar
Luo, M., Zhang, L.-J., and Lei, F., “An insurance model for guaranteeing service assurance, integrity and QoS in cloud computing,” in International Conference on Web Services (ICWS). IEEE, 2010, pp. 584–591.Google Scholar
Dasgupta, D. and Rahman, M. M., “Estimating security coverage for cloud services,” in International Conference on Privacy, Security, Risk and Trust (PASSAT) and Inernational Conference on Social Computing (SocialCom). IEEE, 2011, pp. 1064–1071.Google Scholar
Zhang, C. and Yan, M., “Insurance-based cloud computing-architecture, risk analysis and experiment,” in International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE, 2010, pp. 1–4.Google Scholar
Tribhuwan, M., Bhuyar, V., and Pirzade, S., “Ensuring data storage security in cloud computing through two-way handshake based on token management,” in International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom). IEEE, 2010, pp. 386–389.Google Scholar
Pattuk, E., Kantarcioglu, M., Khadilkar, V., Ulusoy, H., and Mehrotra, S., “BigSecret: A secure data management framework for key-value stores,” in International Conference on Cloud Computing (CLOUD). IEEE, 2013, pp. 147–154.Google Scholar
Chaisiri, S., Ko, R. K., and Niyato, D., “A joint optimization approach to security-as-a-service allocation and cyber insurance management,” in Trustcom/BigDataSE/ISPA. IEEE, 2015, vol. 1, pp. 426–433.Google Scholar
Schaper, J., “Cloud services,” in International Conference on Digital Ecosystems and Technologies (DEST). IEEE, 2010, pp. 91–91.Google Scholar
Rothschild, M. and Stiglitz, J., “Equilibrium in competitive insurance markets: An essay on the economics of imperfect information,” in Uncertainty in economics, pp. 257–280. Elsevier, Amsterdam, 1978.Google Scholar
Cho, S., Qiu, L., and Bandyopadhyay, S., “Should online content providers be allowed to subsidize content? An economic analysis,” Information Systems Research, vol. 27, no. 3, pp. 580–595, 2016.Google Scholar
Brake, D., “Mobile zero rating: The economics and innovation behind free data,” in Net neutrality reloaded: Zero rating, specialised service, ad blocking and traffic management, Belli, L., ed., FGV Direito Rio, Rio de Janeiro, p. 132, 2016.Google Scholar
Eisenach, J. A., “The economics of zero rating,” NERA Economic Consulting, March, 2015.Google Scholar
Andrews, M., Özen, U., Reiman, M. I., and Wang, Q., “Economic models of sponsored content in wireless networks with uncertain demand,” in International Conference on Computer Communications (INFOCOM) Workshops. IEEE, 2013, pp. 345–350.Google Scholar
Ma, R. T., “Subsidization competition: Vitalizing the neutral Internet,” in International Conference on Emerging Networking Experiments and Technologies. ACM, 2014, pp. 283– 294.Google Scholar
Zhang, L. and Wang, D., “Sponsoring content: Motivation and pitfalls for content service providers,” in International Conference on Computer Communications (INFOCOM) Workshops. IEEE, 2014, pp. 577–582.Google Scholar
Joe-Wong, C., Ha, S., and Chiang, M., “Sponsoring mobile data: An economic analysis of the impact on users and content providers,” in International Conference on Computer Communications (INFOCOM). IEEE, 2015, pp. 1499–1507.Google Scholar
Wang, W., Xiong, Z., Niyato, D., and Wang, P., “A hierarchical game with strategy evolution for mobile sponsored content/service markets,” in Global Communications Conference (GLOBECOM). IEEE, 2017, pp. 1–6.Google Scholar
Zhang, L., Wu, W., and Wang, D., “Sponsored data plan: A two-class service model in wireless data networks,” in SIGMETRICS Performance Evaluation Review. ACM, 2015, vol. 43, pp. 85–96.Google Scholar
Lotfi, M. H., Sundaresan, K., Sarkar, S., and Khojastepour, M. A., “Economics of quality sponsored data in non-neutral networks,” Transactions on Networking (TON), vol. 25, no. 4, pp. 2068–2081, 2017.Google Scholar
Zhang, L., Wu, W., and Wang, D., “TDS: Time-dependent sponsored data plan for wireless data traffic market,” in International Conference on Computer Communications (INFO-COM). IEEE, 2016, pp. 1–9.Google Scholar
Xiong, Z., Feng, S., Niyato, D., Wang, P., Leshem, A., and Han, Z., “Joint sponsored and edge caching content service market: A game-theoretic approach,” in IEEE Transactions on Wireless Communications, Jan. 2019.Google Scholar
Andrews, M., Jin, Y., and Reiman, M. I., “A truthful pricing mechanism for sponsored content in wireless networks,” in International Conference on Computer Communications (INFOCOM). IEEE, 2016, pp. 1–9.Google Scholar
Zhang, Y., Xiong, Z., Niyato, D., Wang, P., and Jin, J., “A game-theoretic analysis of complementarity, substitutability and externalities in cloud services,” in Global Communications Conference (GLOBECOM). IEEE, 2017, pp. 1–6.Google Scholar
Xiong, Z., Feng, S., Niyato, D., Wang, P., and Han, Z., “Network effect-based sequential dynamic pricing for mobile social data market,” in Global Communications Conference (GLOBECOM). IEEE, 2017, pp. 1–6.Google Scholar
Zhang, Y., Xiong, Z., Niyato, D., Wang, P., and Jin, J., “Joint optimization of information trading in Internet of Things (IoT) market with externalities,” in Wireless Communications and Networking Conference (WCNC). IEEE, 2018.Google Scholar
Candogan, O., Bimpikis, K., and Ozdaglar, A., “Optimal pricing in networks with externalities,” Operations Research, vol. 60, no. 4, pp. 883–905, 2012.Google Scholar
Gong, X., Duan, L., Chen, X., and Zhang, J., “When social network effect meets congestion effect in wireless networks: Data usage equilibrium and optimal pricing,” Journal on Selected Areas in Communications (JSAC), vol. 35, no. 2, pp. 449–462, 2017.Google Scholar
Kempe, D., Kleinberg, J., and Tardos, É., “Maximizing the spread of influence through a social network,” in 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2003, pp. 137–146.Google Scholar
Nie, J., Xiong, Z., Niyato, D., Wang, P., and Luo, J., “A socially-aware incentive mechanism for mobile crowdsensing service market,” in Global Communications Conference (GLOBECOM). IEEE, 2018, pp. 1–7.Google Scholar
Han, Z., Game theory in wireless and communication networks: Theory, models, and applications, Cambridge University Press, Cambridge, UK, 2012.Google Scholar
Zhang, H., Xiao, Y., Cai, L. X., Niyato, D., Song, L., and Han, Z., “A multi-leader multi-follower Stackelberg game for resource management in LTE unlicensed,” Transactions on Wireless Communications (TWC), vol. 16, no. 1, pp. 348–361, 2017.Google Scholar
Rosen, J. B., “Existence and uniqueness of equilibrium points for concave n-person games,” Econometrica: Journal of the Econometric Society, pp. 520–534, 1965.Google Scholar
Zhang, X., Guo, L., Li, M., and Fang, Y., “Motivating human-enabled mobile participation for data offloading,” Transactions on Mobile Computing, 2017.Google Scholar
Xiong, Z., Feng, S., Niyato, D., Wang, P., and Zhang, Y., “Economic analysis of network effects on sponsored content: A hierarchical game theoretic approach,” in Global Communications Conference (GLOBECOM). IEEE, 2017, pp. 1–6.Google Scholar
Xiong, Z., Feng, S., Niyato, D., Wang, P., and Zhang, Y., “Competition and cooperation analysis for data sponsored market: A network effects model,” in Wireless Communications and Networking Conference (WCNC). IEEE, 2018.Google Scholar
Huang, Q., Birman, K., van Renesse, R., Lloyd, W., Kumar, S., and Li, H. C., “An analysis of Facebook photo caching,” in Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. ACM, 2013, pp. 167–181.Google Scholar
Huston, G. et al., “Web caching,” The Internet Protocol Journal, vol. 2, no. 3, pp. 2–20, 1999.Google Scholar
O’Hanlon, C., “Infoblox DNS caching appliance helps speed web experiences,” Technical Report, 2012.Google Scholar
I. Analytics, “Cplex optimizer,” www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer, 2012.Google Scholar
Manlove, D. F., Algorithmics of matching under preferences, vol. 2, World Scientific, Singapore, 2013.Google Scholar
Wittie, M. P., Pejovic, V., Deek, L., Almeroth, K. C., and Zhao, B. Y., “Exploiting locality of interest in online social networks,” in International Conference on Emerging Networking EXperiments and Technologies (CoNEXT). ACM, 2010, p. 25.Google Scholar
Jaho, E. and Stavrakakis, I., “Joint interest and locality-aware content dissemination in social networks,” in International Conference on Wireless On-Demand Network Systems and Services (WONS). IEEE, 2009, pp. 173–180.Google Scholar
Bertsimas, D. and Tsitsiklis, J. N., Introduction to linear optimization, vol. 6, Athena Scientific, Belmont, MA, 1997.Google Scholar
Gale, D. and Shapley, L. S., “College admissions and the stability of marriage,” The American Mathematical Monthly, vol. 69, no. 1, pp. 9–15, 1962.Google Scholar
Edler, T. and Lundberg, S., “Energy efficiency enhancements in radio access networks,” Ericsson Review, vol. 81, no. 1, pp. 42–51, 2004.Google Scholar
Irmer, R., “Evolution of LTE-operator requirements and some potential solutions,” in International FOKUS IMS Workshop, 2009.Google Scholar
Niyato, D., Hossain, E., Kim, D. I., Bhargava, V., and Shafai, L., Wireless-Powered Communication Networks, Cambridge University Press, Cambridge, UK, 2016.Google Scholar
Jadidian, J. and Katabi, D., “Magnetic MIMO: How to charge your phone in your pocket,” in International Conference on Mobile Computing and Networking. ACM, 2014, pp. 495– 506.Google Scholar
Kurs, A., Karalis, A., Moffatt, R., Joannopoulos, J. D., Fisher, P., and Soljačić, M., “Wireless power transfer via strongly coupled magnetic resonances,” Science, vol. 317, no. 5834, pp. 83–86, 2007.Google Scholar
Kurs, A., Moffatt, R., and Soljačić, M., “Simultaneous mid-range power transfer to multiple devices,” Applied Physics Letters, vol. 96, no. 4, pp. 044102-1-044102-3, 2010.Google Scholar
Lu, X., Niyato, D., Wang, P., Kim, D. I., and Han, Z., “Wireless charger networking for mobile devices: Fundamentals, standards, and applications,” Wireless Communications, vol. 22, no. 2, pp. 126–135, 2015.Google Scholar
Stockman, H., “Communication by means of reflected power,” The IRE, vol. 36, no. 10, pp. 1196–1204, 1948.Google Scholar
Vannucci, G., Bletsas, A., and Leigh, D., “A software-defined radio system for backscatter sensor networks,” Transactions on Wireless Communications (TWC), vol. 7, no. 6, 2008.Google Scholar
Bletsas, A., Siachalou, S., and Sahalos, J. N., “Anti-collision tags for backscatter sensor networks,” in European Microwave Conference (EuMC). IEEE, 2008, pp. 179–182.Google Scholar
Kimionis, J., Bletsas, A., and Sahalos, J. N., “Bistatic backscatter radio for power-limited sensor networks,” in Global Communications Conference (GLOBECOM). IEEE, 2013, pp. 353–358.Google Scholar
Bletsas, A., Siachalou, S., and Sahalos, J. N., “Anti-collision backscatter sensor networks,” Transactions on Wireless Communications (TWC), vol. 8, no. 10, 2009.Google Scholar
Griffin, J. D. and Durgin, G. D., “Complete link budgets for backscatter-radio and RFID systems,” Antennas and Propagation Magazine, vol. 51, no. 2, 2009.Google Scholar
Juels, A., “RFID security and privacy: A research survey,” Journal on Selected Areas in Communications (JSAC), vol. 24, no. 2, pp. 381–394, 2006.Google Scholar
Klair, D. K., Chin, K.-W., and Raad, R., “A survey and tutorial of RFID anti-collision protocols,” Communications Surveys & Tutorials, vol. 12, no. 3, pp. 400–421, 2010.Google Scholar
Zhang, P., Gummeson, J., and Ganesan, D., “Blink: A high throughput link layer for backscatter communication,” in International Conference on Mobile Systems, Applications, and Services. ACM, 2012, pp. 99–112.Google Scholar
Liu, V., Parks, A., Talla, V., Gollakota, S., Wetherall, D., and Smith, J. R., “Ambient backscatter: wireless communication out of thin air,” in SIGCOMM Computer Communication Review. ACM, 2013, vol. 43, pp. 39–50.Google Scholar
Kimionis, J., Bletsas, A., and Sahalos, J. N., “Increased range bistatic scatter radio,” Transactions on Communications (TCOM), vol. 62, no. 3, pp. 1091–1104, 2014.Google Scholar
Choi, S. H. and Kim, D. I., “Backscatter radio communication for wireless powered communication networks,” in Asia-Pacific Conference on Communications (APCC). IEEE, 2015, pp. 370–374.Google Scholar
Lu, X., Niyato, D., Jiang, H., Kim, D. I., Xiao, Y., and Han, Z., “Ambient backscatter networking: A novel paradigm to assist wireless powered communications,” arXiv preprint arXiv:1709.09615, 2017.Google Scholar
Tsuo, F.-Y., Tan, H.-P., Chew, Y. H., and Wei, H.-Y., “Energy-aware transmission control for wireless sensor networks powered by ambient energy harvesting: A game-theoretic approach,” in International Conference on Communications (ICC). IEEE, 2011, pp. 1–5.Google Scholar
Michelusi, N. and Zorzi, M., “Optimal adaptive random multiaccess in energy harvesting wireless sensor networks,” Transactions on Communications (TCOM), vol. 63, no. 4, pp. 1355–1372, 2015.Google Scholar
Niyato, D. and Wang, P., “Competitive wireless energy transfer bidding: A game theoretic approach,” in International Conference on Communications (ICC). IEEE, 2014, pp. 1–6.Google Scholar
Xiao, Y., Niyato, D., Han, Z., and DaSilva, L. A., “Dynamic energy trading for energy harvesting communication networks: A stochastic energy trading game,” Journal on Selected Areas in Communications (JSAC), vol. 33, no. 12, pp. 2718–2734, 2015.Google Scholar
Ding, Z., Perlaza, S. M., Esnaola, I., and Poor, H. V., “Power allocation strategies in energy harvesting wireless cooperative networks,” Transactions on Wireless Communications (TWC), vol. 13, no. 2, pp. 846–860, 2014.Google Scholar
Chen, H. H., Li, Y., Jiang, Y., Ma, Y., and Vucetic, B., “Distributed power splitting for SWIPT in relay interference channels using game theory,” Transactions on Wireless Communications (TWC), vol. 14, no. 1, pp. 410–420, 2015.Google Scholar
Hoang, D. T., Niyato, D., Wang, P., Kim, D. I., and Le, L. B., “Overlay RF-powered backscatter cognitive radio networks: A game theoretic approach,” in International Conference on Communications (ICC). IEEE, 2017, pp. 1–6.Google Scholar
Hong, S. G., Hwang, Y. M., Lee, S. Y., Shin, Y., Kim, D. I., and Kim, J. Y., “Game-theoretic modeling of backscatter wireless sensor networks under smart interference,” Communications Letters, 2017.Google Scholar
Liang, Y.-C., Zeng, Y., Peh, E. C., and Hoang, A. T., “Sensing-throughput tradeoff for cognitive radio networks,” Transactions on Wireless Communications (TWC), vol. 7, no. 4, pp. 1326–1337, 2008.Google Scholar
Luo, L. and Roy, S., “Efficient spectrum sensing for cognitive radio networks via joint optimization of sensing threshold and duration,” Transactions on Communications (TCOM), vol. 60, no. 10, pp. 2851–2860, 2012.Google Scholar
Facchinei, F. and Kanzow, C., “Generalized Nash equilibrium problems,” Annals of Operations Research, vol. 175, no. 1, pp. 177–211, 2010.Google Scholar
Dempe, S., Foundations of bilevel programming, Springer Science & Business Media, New York, 2002.Google Scholar
Wang, W., Hoang, D. T., Niyato, D., Wang, P., and Kim, D. I., “Stackelberg game for distributed time scheduling in RF-powered backscatter cognitive radio networks,” Transactions on Wireless Communications (TWC), vol. 17, no. 8, pp. 5606–5622, 2018.Google Scholar
Scutari, G., Palomar, D. P., Facchinei, F., and Pang, J.-S., “Convex optimization, game theory, and variational inequality theory,” Signal Processing Magazine, vol. 27, no. 3, pp. 35–49, 2010.Google Scholar
Boyd, S. and Vandenberghe, L., Convex optimization, Cambridge University Press, Cambridge, UK, 2004.Google Scholar
Monderer, D. and Shapley, L. S., “Potential games,” Games and Economic Behavior, vol. 14, no. 1, pp. 124–143, 1996.Google Scholar
Sundaram, R. K., A first course in optimization theory, Cambridge University Press, Cambridge, UK, 1996.Google Scholar
Sinha, A., Malo, P. and Deb, K., “A review on bilevel optimization: from classical to evolutionary approaches and applications,” Transactions on Evolutionary Computation, 2017.Google Scholar
Dempe, S. and Schmidt, H., “On an algorithm solving two-level programming problems with nonunique lower level solutions,” Computational Optimization and Applications, vol. 6, no. 3, pp. 227–249, 1996.Google Scholar
Facchinei, F. and Pang, J.-S., Finite-dimensional variational inequalities and complementarity problems, Springer Science & Business Media, New York, 2007.Google Scholar
Xu, C., Song, L., and Han, Z., Resource management for device-to-device underlay communication, Springer, New York, 2013.Google Scholar
Song, L., Niyato, D., Han, Z., and Hossain, E., Wireless device-to-device communications and networks, Cambridge University Press, Cambridge, UK, 2014.Google Scholar
Kaufman, B. and Aazhang, B., “Cellular networks with an overlaid device to device network,” in 42nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Oct. 2008.Google Scholar
Belleschi, M., Fodor, G., and Abrardo, A., “Performance analysis of a distributed resource allocation scheme for D2D communications,” in IEEE Global Communications Conference (GLOBECOM) Workshops, Houston, TX, Dec. 2011.Google Scholar
Janis, P., Koivunen, V., Ribeiro, C., Doppler, K., and Hugl, K., “Interference-avoiding MIMO schemes for device-to-device radio underlaying cellular networks,” in IEEE 20th International Symposium on Indoor and Mobile Radio Communications, Tokyo, Japan, Sep. 2009.Google Scholar
Doppler, K., Manssour, J., Osseiran, A., and Xiao, M., “Innovative concepts in peer-to-peer and network coding,” Project: Wireless World Initiative New Radio, WINNER+, 2008.Google Scholar
Wang, F., Song, L., Han, Z., Zhao, Q., and Wang, X., “Joint scheduling and resource allocation for device-to-device underlay communication,” in IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, Apr. 2013.Google Scholar
Xu, C., Song, L., Han, Z., Li, D., and Jiao, B., “Resource allocation using a reverse iterative combinatorial auction for device-to-device underlay cellular networks,” in IEEE Global Communications Conference (GLOBECOM), Anaheim, CA, Dec. 2012.Google Scholar
Zhang, Y., Song, L., Saad, W., Dawy, Z., and Han, Z., “Exploring social ties for enhanced device-to-device communications in wireless networks,” in IEEE Globe Communication Conference (Globecom), Atlanta, GA, Dec. 2013.Google Scholar
Feng, D., Lu, L., Yuan-Wu, Y., Li, G., Feng, G., and Li, S., “Device-to-device communications underlaying cellular networks,” IEEE Transactions on Communications, vol. 61, no. 8, pp. 3541–3551, Aug. 2013.Google Scholar
Bayat, S., Louie, R. H. Y., Li, Y., and Vucetic, B., “Cognitive radio relay networks with multiple primary and secondary users: Distributed stable matching algorithms for spectrum access,” in 2011 IEEE International Conference on Communications (ICC), Tokyo, Japan, Jun. 2011.Google Scholar
Bayat, S., Louie, R. H. Y., Han, Z., Li, Y., and Vucetic, B., “Distributed stable matching algorithm for physical layer security with multiple source-destination pairs and jammer nodes,” in 2012 IEEE Wireless Communications and Networking Conference (WCNC), Paris, France, Apr. 2012.Google Scholar
Bayat, S., Louie, R. H. Y., Han, Z., Vucetic, B., and Li, Y., “Physical-layer security in distributed wireless networks using matching theory,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 5, pp. 717–732, May 2013.Google Scholar
Bayat, S., Louie, R. H. Y., Han, Z., Li, Y., and Vucetic, B., “Multiple operator and multiple femtocell networks: Distributed stable matching,” in 2012 IEEE International Conference on Communications (ICC), Ottawa, Canada, Jun. 2012.Google Scholar
El-Hajj, A., Dawy, Z., and Saad, W., “A stable matching game for joint uplink/downlink resource allocation in OFDMA wireless networks,” in 2012 IEEE International Conference on Communications (ICC), Ottawa, Canada, Jun. 2012.Google Scholar
Pantisano, F., Bennis, M., Saad, W., Valentin, S., and Debbah, M., “Matching with externalities for context-aware user-cell association in small cell networks,” in IEEE Global Communications Conference, Atlanta, GA, Dec. 2013.Google Scholar
Saad, W., Han, Z., Zheng, R., Debbah, M., and Poor, H. V., “A college admissions game for uplink user association in wireless small cell networks,” in The 33rd Annual IEEE International Conference on Computer Communications, Toronto, Canada, Apr.-May 2014.Google Scholar
Hamidouche, K., Saad, W., and Debbah, M., “Many-to-many matching games for proactive social-caching in wireless small cell networks,” in Proceedings of 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Hammamet, Tunisia, May 2014.Google Scholar
Leshem, A., Zehavi, E., and Yaffe, Y., “Multichannel opportunistic carrier sensing for stable channel access control in cognitive radio systems,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 1, pp. 82–95, Jan. 2012.Google Scholar
Naparstek, O. and Leshem, A., “A fast matching algorithm for asymptotically optimal distributed channel assignment,” in 18th International Conference on Digital Signal Processing (DSP), Santorini, Greece, Jul. 2013.Google Scholar
Naparstek, O., Leshem, A., and Jorswieck, E. A., “Distributed medium access control for energy efficient transmission in cognitive radios,” Computing Research Repository (CoRR), vol. abs/1401.1671, 2014.Google Scholar
Holfeld, B., Mochaourab, R., and Wirth, T., “Stable Matching for Adaptive Cross-Layer Scheduling in the LTE Downlink,” in IEEE 77th Vehicular Technology Conference (VTC Spring), Dresden, Germany, Jun. 2013.Google Scholar
Mochaourab, R., Holfeld, B., and Wirth, T., “Distributed channel assignment in cognitive radio networks: Stable matching and Walrasian equilibrium,” IEEE Transactions on Wireless Communications, vol. 14, no. 7, pp. 3924–3936, Jul. 2015.Google Scholar
Zhang, Y., Gu, Y., Pan, M., and Han, Z., “Distributed matching based spectrum allocation in cognitive radio networks,” in IEEE Globe Communication Conference, Austin, TX, Dec. 2014.Google Scholar
Jorswieck, E. A., “Stable matchings for resource allocation in wireless networks,” in 2011 17th International Conference on Digital Signal Processing (DSP), Corfu, Greece, Jul. 2011.Google Scholar
Jorswieck, E. A. and Cao, P., “Matching and exchange market based resource allocation in MIMO cognitive radio networks,” in Proceedings of the 21st European Signal Processing Conference (EUSIPCO), Marrakech, Morocco, Sep. 2013.Google Scholar
Huang, L., Zhu, G., Du, X., and Bian, K., “Stable multiuser channel allocations in opportunistic spectrum access,” in IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, Apr. 2013.Google Scholar
Guo, L., Cui, Q., Liu, Y., Li, X., Fu, T., and Chen, Z., “Graph theory based channel reallocation technique in channel borrowing in mobile satellite communication,” in IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, Apr. 2013.Google Scholar
Han, J., Cui, Q., Yang, C., and Tao, X., “Bipartite matching approach to optimal resource allocation in device to device underlaying cellular network,” Electronics Letters, vol. 50, no. 3, Jan. 2014.Google Scholar
Gjendemsjo, A., Gesbert, D., Oien, G. E., and Kiani, S. G., “Optimal power allocation and scheduling for two-cell capacity maximization,” in 4th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, Boston, MA, Apr. 2006.Google Scholar
Gusfield, D., “Three fast algorithms for four problems in stable marriage,” SIAM Journal on Computing, vol. 16, no. 1, pp. 111–128, Feb. 1987.Google Scholar
Irving, R. W., Leather, P., and Gusfield, D., “An efficient algorithm for the optimal stable marriage,” Journal of the ACM, vol. 34, no. 3, pp. 532–543, Jul. 1987.Google Scholar
Feder, T., “Network flow and 2-satisfiability,” Algorithmica, vol. 11, no. 3, pp. 291–319, Mar. 1994.Google Scholar
Kuhn, H. W., “The Hungarian method for the assignment problem,” Naval Research Logistics Quarterly, vol. 2, pp. 83–97, 1955.Google Scholar
Huang, C., “Cheating by men in the Gale-Shapley stable matching algorithm,” in Algorithms-ESA, vol. 4168, pp. 418–431. 2006.Google Scholar
Cisco, “Cisco visual networking index: Global mobile data traffic forecast update, 2013-2018,” White Paper c11–520862, Cisco Cooperation, 2014.Google Scholar
Shanmugam, K., Golrezaei, N., Dimakis, A., Molisch, A., and Caire, G., “Femtocaching: Wireless content delivery through distributed caching helpers,” IEEE Transactions on Information Theory, vol. 59, no. 12, pp. 8402–8413, Dec. 2013.Google Scholar
Golrezaei, N., Mansourifard, P., Molisch, A. F., and Dimakis, A. G., “Base-station assisted device-to-device communications for high-throughput wireless video networks,” IEEE Transactions on Wireless Communications, vol. 13, no. 7, pp. 3665–3676, Jul. 2014.Google Scholar
Hamidouche, K., Saad, W., and Debbah, M., “Many-to-many matching games for proactive social-caching in wireless small cell networks,” in International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2014 12th Hammamet, Tunisia, May 2014, pp. 569–574.Google Scholar
Song, L., Niyato, D., Han, Z., and Hossain, E., “Game-theoretic resource allocation methods for device-to-device communication,” IEEE Wireless Communications, vol. 21, no. 3, pp. 136–144, Jun. 2014.Google Scholar
Wang, Q., Wang, W., Jin, S., Zhu, H., and Zhang, N. T., “Quality-optimized joint source selection and power control for wireless multimedia D2D communication using Stackelberg game,” IEEE Transactions on Vehicular Technology, vol. 64, no. 8, pp. 3755–3769, Aug. 2015.Google Scholar
Wang, Q., Wang, W., Jin, S., Zhu, H., and Zhang, N. T., “Game-theoretic source selection and power control for quality-optimized wireless multimedia device-to-device communications,” in 2014 IEEE Global Communications Conference, Austin, TX, Dec. 2014, pp. 4568–4573.Google Scholar
Wang, B., Han, Z., and Liu, K. J. R., “Distributed relay selection and power control for multiuser cooperative communication networks using Stackelberg game,” IEEE Transactions on Mobile Computing, vol. 8, no. 7, pp. 975–990, Jul. 2009.Google Scholar
Li, J., Chen, W., Xiao, M., Shu, F., and Liu, X., “Efficient video pricing and caching in heterogeneous networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 10, pp. 8744–8751, Oct 2016.Google Scholar
Li, J., Sun, J., Qian, Y., Shu, F., Xiao, M., and Xiang, W., “A commercial video-caching system for small-cell cellular networks using game theory,” IEEE Access, vol. 4, pp. 7519–7531, 2016.Google Scholar
Gao, L., Wang, X., Xu, Y., and Zhang, Q., “Spectrum trading in cognitive radio networks: A contract-theoretic modeling approach,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 4, pp. 843–855, Apr. 2011.Google Scholar
Luo, Y., Gao, L., and Huang, J., “Spectrum reservation contract design in TV white space networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 2, pp. 147–160, Jun. 2015.Google Scholar
Kordali, A. V. and Cottis, P. G., “A contract-based spectrum trading scheme for cognitive radio networks enabling hybrid access,” IEEE Access, vol. 3, pp. 1531–1540, Jul. 2015.Google Scholar
Sheng, S. P. and Liu, M., “Profit incentive in trading nonexclusive access on a secondary spectrum market through contract design,” IEEE/ACM Transactions on Networking, vol. 22, no. 4, pp. 1190–1203, Aug. 2014.Google Scholar
Li, Y., Zhang, J., Gan, X., Fu, L., Yu, H., and Wang, X., “A contract-based incentive mechanism for delayed traffic offloading in cellular networks,” IEEE Transactions on Wireless Communications, vol. 15, no. 8, pp. 5314–5327, Aug. 2016.Google Scholar
Hasan, Z. and Bhargava, V. K., “Relay selection for OFDM wireless systems under asymmetric information: A contract-theory based approach,” IEEE Transactions on Wireless Communications, vol. 12, no. 8, pp. 3824–3837, Aug. 2013.Google Scholar
Zhang, Y., Song, L., Saad, W., Dawy, Z., and Han, Z., “Contract-based incentive mechanisms for device-to-device communications in cellular networks,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 10, pp. 2144–2155, Oct. 2015.Google Scholar
Hamidouche, K., Saad, W., and Debbah, M., “Breaking the economic barrier of caching in cellular networks: Incentives and contracts,” in IEEE Global Communications Conference (GLOBECOM), Washington, DC, Dec. 2016.Google Scholar
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y. Y., and Moon, S., “I tube, you tube, everybody tubes: Analyzing the world’s largest user generated content video system,” in ACM SIGCOMM Conference on Internet Measurement, San Diego, CA, Aug. 2007, pp. 1–14.Google Scholar
Li, J., Chen, H., Chen, Y., Lin, Z., Vucetic, B., and Hanzo, L., “Pricing and resource allocation via game theory for a small-cell video caching system,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 8, pp. 2115–2129, Aug. 2016.Google Scholar
Cisco, “Cisco visual networking index: Global mobile data traffic forecast update, 2016,” Technical Report, 2017.Google Scholar
Liu, F., Bala, E., Erkip, E., Beluri, M. C., and Yang, R., “Small-cell traffic balancing over licensed and unlicensed bands,” IEEE Transactions on Vehicular Technology, vol. 64, no. 12, pp. 5850–5865, 2015.Google Scholar
Fu, J., Zhang, X., Cheng, L., Shen, Z., Chen, L., and Yang, D., “Utility-based flexible resource allocation for integrated LTE-U and LTE wireless systems,” in Vehicular Technology Conference (VTC Spring), 2015 IEEE 81st. IEEE, 2015.Google Scholar
Xu, Y., Yin, R., Chen, Q., and Yu, G., “Joint licensed and unlicensed spectrum allocation for unlicensed LTE,” in Personal, Indoor, and Mobile Radio Communications (PIMRC), 2015 IEEE 26th Annual International Symposium on. IEEE, 2015, pp. 1912–1917.Google Scholar
Sallent, O., Pérez-Romero, J., Ferrús, R., and Agustí, R., “Learning-based coexistence for LTE operation in unlicensed bands,” in Communication Workshop (ICCW), 2015 IEEE International Conference on. IEEE, 2015, pp. 2307–2313.Google Scholar
Gu, Y., Zhang, Y., Cai, L., Pan, M., Song, L., and Han, Z., “Student-project allocation matching for spectrum sharing in LTE-unlicensed,” in Global Communications Conference, IEEE, 2015.Google Scholar
Zhang, H., Xiao, Y., Cai, L. X., Niyato, D., Song, L., and Han, Z., “A hierarchical game approach for multi-operator spectrum sharing in LTE unlicensed,” in Global Communications Conference (GLOBECOM), 2015 IEEE. IEEE, 2015.Google Scholar
Xiao, Y., Han, Z., Yuen, C., and DaSilva, L. A., “Carrier aggregation between operators in next generation cellular networks: A stable roommate market,” IEEE Transactions on Wireless Communications, vol. 15, no. 1, pp. 633–650, 2016.Google Scholar
Yuan, P., Xiao, Y., Bi, G., and Zhang, L., “Toward cooperation by carrier aggregation in heterogeneous networks: A hierarchical game approach,” IEEE Transactions on Vehicular Technology, vol. 66, no. 2, pp. 1670–1683, 2017.Google Scholar
Xiao, Y., Niyato, D., Han, Z., and Chen, K.-C., “Secondary users entering the pool: A joint optimization framework for spectrum pooling,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 3, pp. 572–588, 2014.Google Scholar
Shen, Y. and Martinez, E., “Channel estimation in OFDM systems,” Freescale semiconductor application note, 2006.Google Scholar
Gale, D. and Shapley, L. S., “College admissions and the stability of marriage,” The American Mathematical Monthly, vol. 69, no. 1, pp. 9–15, 1962.Google Scholar
McVitie, D. G. and Wilson, L. B., “The stable marriage problem,” Communications of the ACM, vol. 14, no. 7, pp. 486–490, 1971.Google Scholar
Boyd, S. and Vandenberghe, L., Convex optimization, Cambridge University Press, Cambridge, UK, 2004.Google Scholar
Xiao, Y., Bi, G., and Niyato, D. YXiao01, “A simple distributed power control algorithm for cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 10, no. 11, pp. 3594–3600, 2011.Google Scholar
McFarland, M., “Google drones will deliver chipotle burritos at Virginia Tech,” CNN Money, September 2016.Google Scholar
Xiang, G., Hardy, A., Rajeh, M., and Venuthurupalli, L., “Design of the life-ring drone delivery system for rip current rescue,” in IEEE Systems and Information Engineering Design Symposium (SIEDS), Apr. 2016, pp. 181–186.Google Scholar
Gatteschi, V., Lamberti, F., Paravati, G., Sanna, A., Demartini, C., Lisanti, A., and Venezia, G., “New frontiers of delivery services using drones: A prototype system exploiting a quadcopter for autonomous drug shipments,” in 39th IEEE Annual Computer Software and Applications Conference (COMPSAC), July 2015, vol. 2, pp. 920–927.Google Scholar
Pagliery, J., “Sniper attack on California power grid may have been an insider, DHS says,” CNN. com, Oct. 2015.Google Scholar
Javaid, A. Y., Sun, W., Devabhaktuni, V. K., and Alam, M., “Cyber security threat analysis and modeling of an unmanned aerial vehicle system,” in IEEE Conference on Technologies for Homeland Security (HST), Nov. 2012, pp. 585–590.Google Scholar
Mansfield, K., Eveleigh, T., Holzer, T. H., and Sarkani, S., “Unmanned aerial vehicle smart device ground control station cyber security threat model,” in IEEE International Conference on Technologies for Homeland Security (HST), Nov. 2013, pp. 722–728.Google Scholar
Rodday, N. M., Schmidt, R. d. O., and Pras, A., “Exploring security vulnerabilities of unmanned aerial vehicles,” in IEEE/IFIP Network Operations and Management Symposium (NOMS), Apr. 2016, pp. 993–994.Google Scholar
Wood, R. K., “Deterministic network interdiction,” Mathematical and Computer Modeling, vol. 17, no. 2, pp. 1–18, 1993.Google Scholar
Başar, T. and Olsder, G. J., Dynamic Noncooperative Game Theory, SIAM Series in Classics in Applied Mathematics, Society for Industrial and Applied Mathematics, Philadelphia, PA, Jan. 1999.Google Scholar
Okhravi, H., Hobson, T., Bigelow, D., and Streilein, W., “Finding focus in the blur of moving-target techniques,” IEEE Security & Privacy,, vol. 12, no. 2, pp. 16–26, 2014.Google Scholar
Xu, J., Guo, P., Zhao, M., Erbacher, R. F., Zhu, M., and Liu, P., “Comparing different moving target defense techniques,” in Proceedings of the First ACM Workshop on Moving Target Defense, Scottsdale, AZ, Nov. 2014, pp. 97–107.Google Scholar
McDaniel, P., Jaeger, T., Porta, T. F. L., Papernot, N., Walls, R. J., Kott, A., Marvel, L., Swami, A., Mohapatra, P., Krishnamurthy, S. V. et al., “Security and science of agility,” in Proceedings of the First ACM Workshop on Moving Target Defense, Scottsdale, AZ, Nov. 2014, pp. 13–19.Google Scholar
Zhuang, R., DeLoach, S. A., and Ou, X., “Towards a theory of moving target defense,” in Proceedings of the First ACM Workshop on Moving Target Defense, Scottsdale, AZ, Nov. 2014, pp. 31–40.Google Scholar
Casola, V., Benedictis, A. D., and Albanese, M., “A moving target defense approach for protecting resource-constrained distributed devices,” in IEEE 14th International Conference on Information Reuse and Integration (IRI), San Francisco, CA, August 2013, pp. 22–29.Google Scholar
Marttinen, A., Wyglinski, A. M., and Jantti, R., “Moving-target defense mechanisms against source-selective jamming attacks in tactical cognitive radio MANETs,” in IEEE Conference on Communications and Network Security (CNS), San Francisco, CA, October 2014, pp. 14–20.Google Scholar
Jafarian, J. H., Al-Shaer, E., and Duan, Q., “Openflow random host mutation: Transparent moving target defense using software defined networking,” in Proceedings of the First Workshop on Hot Topics in Software Defined Networks, Helsinki, Finland, August 2012, pp. 127–132.Google Scholar
Jajodia, S., Ghosh, A. K., Subrahmanian, V., Swarup, V., Wang, C., and Wang, X. S., Moving Target Defense II, Springer, New York, 2013.Google Scholar
Zhu, Q. and Başar, T., “Game-theoretic approach to feedback-driven multi-stage moving target defense,” in Decision and Game Theory for Security, pp. 246–263. Springer, New York, 2013.Google Scholar
Carter, K. M., Riordan, J. F., and Okhravi, H., “A game theoretic approach to strategy determination for dynamic platform defenses,” in Proceedings of the First ACM Workshop on Moving Target Defense, Scottsdale, AZ, Nov. 2014, pp. 21–30.Google Scholar
El-Dosouky, A., Saad, W., and Niyato, D., “Single controller stochastic games for optimized moving target defense,” in Proceedings of the International Conference on Communications, Kuala lumpur, Malaysia, May 2016.Google Scholar
Lee, J., Kapitanova, K., and Son, S. H., “The price of security in wireless sensor networks,” Computer Networks, vol. 54, no. 17, pp. 2967–2978, 2010.Google Scholar
Manadhata, P. K. and Wing, J. M., “An attack surface metric,” IEEE Transactions on Software Engineering, vol. 37, no. 3, pp. 371–386, 2011.Google Scholar
Filar, J. A. and Raghavan, T., “A matrix game solution of the single-controller stochastic game,” Mathematics of Operations Research, vol. 9, no. 3, pp. 356–362, 1984.Google Scholar
Mertens, J.-F., “Stochastic games,” Handbook of game theory with economic applications, Aumann, R. and Hart, S., eds., vol. 3, pp. 1809–1832, 2002.Google Scholar
Nowak, A. and Raghavan, T., “A finite step algorithm via a bimatrix game to a single controller non-zero sum stochastic game,” Mathematical Programming, vol. 59, no. 1-3, pp. 249–259, 1993.Google Scholar
Lemke, C. E. and Howson, J. T., Jr, “Equilibrium points of bimatrix games,” Journal of the Society for Industrial & Applied Mathematics, vol. 12, no. 2, pp. 413–423, 1964.Google Scholar
Department of Homeland Security, “Critical infrastructure sectors,” Technical Report, 2014.Google Scholar
Baalbaki, B. A., Al-Nashif, Y., Hariri, S., and Kelly, D., “Autonomic critical infrastructure protection (ACIP) system,” in ACS International Conference on Computer Systems and Applications (AICCSA), 2013, pp. 1–4.Google Scholar
Sandaruwan, G., Ranaweera, P., and Oleshchuk, V., “PLC security and critical infrastructure protection,” in IEEE International Conference on Industrial and Information Systems (ICIIS), Peradeniya, Sri Lanka, Dec. 2013, pp. 81–85.Google Scholar
McCausland, J., Nardo, G. D., Falcon, R., Abielmona, R., Groza, V., and Petriu, E., “A proactive risk-aware robotic sensor network for critical infrastructure protection,” in IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2013, pp. 132–137.Google Scholar
Beltran, L. P., Merabti, M., and Shi, Q., “Multiplayer game technology to manage critical infrastructure protection,” in IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2012, pp. 549–556.Google Scholar
Bier, V. M., Haphuriwat, N., Menoyo, J., Zimmerman, R., and Culpen, A. M., “Optimal resource allocation for defense of targets based on differing measures of attractiveness,” Risk Analysis, vol. 28, no. 3, pp. 763–770, 2008.Google Scholar
Huang, Y., Fan, Y., and Cheu, R. L., “Optimal allocation of multiple emergency service resources for protection of critical transportation infrastructure,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2022, no. 1, pp. 1–8, 2007.Google Scholar
El-Dosouky, A., Saad, W., Kamhoua, C., and Kwiat, K., “Contract-theoretic resource allocation for critical infrastructure protection,” in Proceedings of IEEE Global Communication Conference, San Diego, CA, Dec. 2015.Google Scholar
Stole, L., “Lectures on the theory of contracts and organizations,” Unpublished monograph, 2001.Google Scholar