Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-6bf8c574d5-j5c6p Total loading time: 0 Render date: 2025-03-09T16:35:34.150Z Has data issue: false hasContentIssue false

2 - Inside a wireless sensor node: structure and operations

Published online by Cambridge University Press:  05 December 2014

Mohammad S. Obaidat  and
Sudip Misra
Mohammad S. Obaidat
Affiliation:
Monmouth University, New Jersey
Sudip Misra
Affiliation:
Indian Institute of Technology
Get access

Summary

As indicated in Chapter 1, a wireless sensor network (WSN) is a wireless computer network that consists of many dispersed independent sensor nodes to coordinate physical or environmental conditions, such as vibration, pressure, temperature, sound, motion, and pollutants, at different settings. WSNs are being used in a wide variety of industrial and civilian applications, which may include industrial process monitoring and control, smart and digital homes, smart cities, environment and habitat monitoring, health care applications, and traffic control, among many others.

In terms of structure, WSNs are a blend of small sensors and the actuators along with some general-purpose small processors to perform some limited computational processing [1–12]. The WSN consists of numerous low-power, low-cost and self-organizing sensor nodes. Gathering data or physical information from the physical surroundings is one of the main aims of any WSN [1–20].

This chapter is aimed at investigating the structure of a wireless sensor node including its architecture and operation mechanism.

Limitations in wireless sensor networks

The unfriendly and remote environments at which wireless sensor nodes are usually positioned, and the restricted computational power and energy, as well as limited storage space in the nodes, are the major parameters that dictate what protocols should be employed. For example, because of such limited resources, the schemes and protocols usually used to secure WSNs are light-weight solutions, and those used for routing, are more energy-aware and should need as little execution time as possible.

Type
Chapter
Information
Principles of Wireless Sensor Networks , pp. 14 - 29
Publisher: Cambridge University Press
Print publication year: 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Zheng, J. and Jamalipour, A., Wireless Sensor Networks – A Networking Perceptive, Hoboken, NJ: Wiley, 2009.Google Scholar
Tilak, S., Abu-Ghazaleh, N. B. and Heinzelman, W., “A taxonomy of sensor network communication models,” Mobile Computing and Communication Review, Vol. 6, No. 2, pp. 28–36, April 2002.CrossRefGoogle Scholar
Zhao, F. and Guibas, L., Wireless Sensor Networks: An Information Processing Approach, San Francisco, CA: Morgan Kaufmann, 2004.Google Scholar
Pottie, G. J. and Kaiser, W. J., “Wireless integrated network sensors,” Communications of the ACM, Vol. 43, No. 5, pp. 51–58, May 2000.CrossRefGoogle Scholar
Hadim, S. and Mohamed, N., “Middleware challenges and approaches for wireless sensor networks,” IEEE Distributed Systems Online, Vol. 7, No. 3, pp. 1–13, March 2006.CrossRefGoogle Scholar
Barr, R., Bicket, J. C., Dantas, D. S. et al., “On the need for system-level support for ad hoc and sensor networks,” Operating System Review, Vol. 36, No. 2, pp. 1–5, 2002.CrossRefGoogle Scholar
Römer, K. and Mattern, F., “The design space of wireless sensor networks,” IEEE Wireless Communications, Vol. 11, No. 6, pp. 54–61, Dec. 2004.CrossRefGoogle Scholar
Perrig, A., Stankovic, J. and Wagner, D., “Security in wireless sensor networks,” Communications of the ACM, Vol. 47, No. 6, pp. 53–57, 2004.CrossRefGoogle Scholar
Pierret, R. F., Introduction to Microelectronics Fabrication, Menlo Park, CA: Addison-Wesley, 1990.Google Scholar
Senturia, S. D., Microsystems Design, Norwell, MA: Springer, 2001.Google Scholar
Franke, A. E., King, T.-J. and Howe, R. T., “Integrated MEMS technologies,” MRS Bulletin, Vol. 26, No. 4, pp. 291–295, 2001.CrossRefGoogle Scholar
Raghunathan, V., Schurgers, C., Park, S. and Srivastava, M., “Energy-aware wireless microsensor networks,” IEEE Signal Processing Magazine, Vol. 19, No. 2, pp. 40–50, March 2002.CrossRefGoogle Scholar
Benini, L. and DeMicheli, G., Dynamic Power Management: Design Techniques and CAD Tools, Norwell, MA: Springer, 1997.Google Scholar
Chandrakasan, A. and Brodersen, R., Low Power CMOS Digital Design, Norwell, MA: Springer, 1996.Google Scholar
Pering, T., Burd, T. and Brodersen, R., “The simulation and evaluation of dynamic voltage scaling algorithm,” in Proceedings of the 1998 International Symposium on Low Power Electronics and Design, ISLPED’98, pp. 76–81, Monterey, CA, 1998.Google Scholar
Savvides, A. and Srivastava, M., “A distributed computation platform for wireless embedded sensing,” in Proceedings of the 2002 International Conference on Computer Design, ICCD02, pp. 220–225, Sep. 2002.Google Scholar
Chandrakasan, A., Min, R., Bhardawi, M., Cho, S.-H. and Wang, A., “Power aware wireless microsensor systems,” in Proceedings of the 2002 International European Solid-State Device Research Conference, ESSDERC’02, pp. 47–54, Florence, Italy, 2002.Google Scholar
Yao, F., Demers, A. and Shenker, S., “A scheduling model for reduced CPU energy,” in Proceedings of the 1995 Annual Symposium on Foundations of Computer Science, FOCS’95, pp. 374–382, October 1995.Google Scholar
Rabaey, J., Ammer, J., da Silva, J., Patel, D. and Roundy, S., “Picoradio supports ad hoc ultra-low power wireless networking,” IEEE Computer Magazine, pp. 42–48, July 2002.Google Scholar
Levis, P. and Culler, D., “Mote: a tiny virtual machine for sensor networks,” in Proceedings of the 2002 International Conference on Architectural Support for Programming Languages and Operating Systems, ASLOS’2002, pp. 85–95, San Jose, CA, 2002.Google Scholar
Cheong, E., Liebman, J., Liu, J. and Zhao, F., “TinyGALS: a programming model for event-driven embedded systems,” in Proceedings of the 2003 Annual ACM Symposium on Applied Computing, SAC 2003, pp. 698–704, Melbourne, FL, March 2003.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×