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A MARKOV-MODULATED DIFFUSION MODEL FOR ENERGY HARVESTING SENSOR NODES

Published online by Cambridge University Press:  19 June 2017

Omer H. Abdelrahman Open the ORCID record for Omer H. Abdelrahman [Opens in a new window]
Omer H. Abdelrahman*
Affiliation:
Department of Electrical and Electronic Engineering, Intelligent Systems and Networks, Imperial College, London SW7 2BT, UK E-mail: [email protected]
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Abstract

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This paper presents a probability model of an energy-harvesting wireless sensor node, with the objective of linking quality of sensed data to energy consumption and self-sustainability. The model departs from the common energy discretization framework used in the literature, and instead uses a diffusion process modulated by discrete packet arrival and transmission processes for the detailed representation of renewable energy supply, consumption and storage. An analytical–numerical method is developed to compute the average time until the node experiences an outage, due to lack of energy, for a given workload and ambient energy characteristics, battery capacity and initial charge. The results are illustrated with numerical examples.

Keywords

energy depletionenergy harvestingdiffusion processfirst-passage timeinternet of thingsself-sustainability
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 31 , Special Issue 4: G-Networks and their Applications , October 2017 , pp. 505 - 515
Copyright
Copyright © Cambridge University Press 2017 

References

1. Abdelrahman, O.H. & Gelenbe, E. (2009). Queueing performance under network coding. In Proceedings of the IEEE Information Theory Workshop on Networking and Information Theory (ITW), Volos, Greece, pp. 135–139.Google Scholar
2. Abdelrahman, O.H. & Gelenbe, E. (2012). Packet delay and energy consumption in non-homogeneous networks. The Computer Journal 55(8): 950–964.CrossRefGoogle Scholar
3. Abdelrahman, O.H. & Gelenbe, E. (2013). Time and energy in team-based search. Physical Review E 87(3): 032125.CrossRefGoogle Scholar
4. Abdelrahman, O.H. & Gelenbe, E. (2016). A diffusion model for energy harvesting sensor nodes. In Proceedings of the IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS’16), London, UK, pp. 154–158.Google Scholar
5. Asmussen, S. (1995). Stationary distributions for fluid flow models with or without Brownian noise. Communications in Statistics. Stochastic Models 11(1): 21–49.CrossRefGoogle Scholar
6. Cai, L.X., Liu, Y., Luan, T.H., Shen, X.S., Mark, J.W. & Poor, H.V. (2014). Sustainability analysis and resource management for wireless mesh networks with renewable energy supplies. IEEE Journal on Selected Areas in Communications 32(2): 345–355.Google Scholar
7. Dufresne, F. & Gerber, H.U. (1991). Risk theory for the compound Poisson process that is perturbed by diffusion. Insurance: Mathematics and Economics 10(1): 51–59.Google Scholar
8. Galinina, O., Mikhaylov, K., Andreev, S., Turlikov, A. & Koucheryavy, Y. (2015). Smart home gateway system over Bluetooth low energy with wireless energy transfer capability. EURASIP Journal on Wireless Communications and Networking 2015(1): 1–18.Google Scholar
9. Gautam, N. & Mohapatra, A. (2015). Efficiently operating wireless nodes powered by renewable energy sources. IEEE Journal on Selected Areas in Communications 33(8): 1706–1716.Google Scholar
10. Gelenbe, E. & Ceran, E.T. (2016). Energy packet networks with energy harvesting. IEEE Access 4: 1321–1331.CrossRefGoogle Scholar
11. Gelenbe, E., Gesbert, D., Gunduz, D., Kulah, H. & Uysal-Biyikoglu, E. (2013). Energy harvesting communication networks: Optimization and demonstration (the E-CROPS project). In Proceedings of the 24th Tyrrhenian International Workshop on Digital Communications – Green ICT (TIWDC), Genoa, Italy, pp. 1–6.Google Scholar
12. Gelenbe, E. & Kadioglu, Y.M. (2015). Energy loss through standby and leakage in energy harvesting wireless sensors. In Proceedings of the CAMAD’15, Guildford, UK, pp. 231–236.Google Scholar
13. Gelenbe, E. (1975) On approximate computer system models. Journal of the ACM 22: 261–269.Google Scholar
14. Gelenbe, E. (2010). Search in unknown random environments. Physical Review E 82(6): 061112.CrossRefGoogle ScholarPubMed
15. Gelenbe, E. (2012). Energy packet networks: ICT based energy allocation and storage. In Proceedings of the 1st International Conference on Green Communications and Networking (GreeNets), volume 51 of LNICST, Colmar, France, pp. 186–195. Springer.Google Scholar
16. Gelenbe, E. (2012) Energy packet networks: smart electricity storage to meet surges in demand. In Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques (SIMUTOOLS), Desenzano del Garda, Italy, pp. 1–7. ICST/ACM.Google Scholar
17. Gelenbe, E. (2015). Synchronising energy harvesting and data packets in a wireless sensor. Energies 8(1): 356–369.Google Scholar
18. Gelenbe, E. & Abdelrahman, O.H. (2014). Search in the universe of big networks and data. IEEE Network 28(4): 20–25.Google Scholar
19. Gelenbe, E. & Marin, A. (2015). Interconnected wireless sensors with energy harvesting. In Proceedings of the 22nd International Conference on Analytical and Stochastic Modelling Techniques and Applications (ASMTA), volume 9081 of LNCS, Albena, Bulgaria, pp. 87–99. Springer.CrossRefGoogle Scholar
20. Gubbi, J., Buyya, R., Marusic, S. & Palaniswami, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems 29(7): 1645–1660.Google Scholar
21. Hanson, F. (2007). Applied stochastic processes and control for jump-diffusions: modeling, analysis, and computation , volume 13 of Advances in design and control. Philadelphia, PA: SIAM.Google Scholar
22. Jones, G.L., Harrison, P.G., Harder, U. & Field, T. (2011). Fluid queue models of battery life. In Proceedings of the IEEE 19th International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), Singapore, pp. 278–285.Google Scholar
23. Jornet, J.M. & Akyildiz, I.F. (2012). Joint energy harvesting and communication analysis for perpetual wireless nanosensor networks in the terahertz band. IEEE Transactions on Nanotechnology 11(3): 570–580.Google Scholar
24. Kansal, A., Hsu, J., Zahedi, S. & Srivastava, M.B. (2007). Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems 6(4), Article 32, 38 pp.CrossRefGoogle Scholar
25. Karandikar, R.L. & Kulkarni, V.G. (1995). Second-order fluid flow models: Reflected Brownian motion in a random environment. Operations Research 43(1): 77–88.Google Scholar
26. Kendall, D.G. (1951). Some problems in the theory of queues. Journal of the Royal Statistics Society, Series B: Statistical Methodology 13(2): 151–185.Google Scholar
27. Naderi, M.Y., Basagni, S. & Chowdhury, K.R. (2012). Modeling the residual energy and lifetime of energy harvesting sensor nodes. In Proceedings of the IEEE Global Communications Conference (GLOBECOM), Anaheim, CA, pp. 3394–3400.Google Scholar
28. Raghunathan, V., Ganeriwal, S. & Srivastava, M. (2006). Emerging techniques for long lived wireless sensor networks. IEEE Communications Magazine 44(4): 108–114.Google Scholar
29. Seyedi, A. & Sikdar, B. (2008). Modeling and analysis of energy harvesting nodes in wireless sensor networks. In Proceedings of the 46th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Urbana-Champaign, IL, pp. 67–71.Google Scholar
30. Sharma, V., Mukherji, U., Joseph, V. & Gupta, S. (2010). Optimal energy management policies for energy harvesting sensor nodes. IEEE Transactions on Wireless Communications 9(4): 1326–1336.Google Scholar
31. Tandon, A. & Motani, M. (2014). Has green energy arrived? delay analysis for energy harvesting communication systems. In Proceedings of the 11th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Singapore, pp. 582–590.Google Scholar
32. Tunc, C. & Akar, N. (2017). Markov fluid queue model of an energy harvesting IOT device with adaptive sensing. Performance Evaluation 111: 1–16.Google Scholar