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3 - Wireless sensor network applications: overview and case studies

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
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Summary

Wireless sensor networks (WSNs) have fascinated both the research and development communities. Applications of WSNs have mushroomed in both civilian and military domains. The growth of wireless sensor networks was originally motivated by military applications; however, WSNs are now used in all kinds of civilian and industrial applications [1–20]. Currently, the overwhelming majority of WSNs measure scalar tangible phenomena such as humidity, pressure, temperature, movement, and pollutants. Typically, most WSNs are built for delay-tolerant and low-bandwidth applications. Hence, most research efforts have concentrated on this latter paradigm, which is often called terrestrial sensor networks. In any WSN application the main challenge is the lifetime of the network. A WSN structure includes a gateway that offers wireless connectivity back to the wired/fixed network.

In nearly all applications of the WSNs, they are used to monitor certain physical processes. They are deployed for measuring the temperature, air pressure, radiation of the human body, chemical reactions, movement of objects, vitals of the body, and so on. By doing this we can recover some important information of the boundaries that are often called edges. To keep track of the edge of a physical development, the recognition of a boundary is imperative. The issue of discovering the boundary is considered to be the first step for resolving the edge detection. In digital processing there are many methods for identifying the edge; however, they are not easy to implement in the WSNs’ environment since the sensor nodes are not equally spaced like pixels, and because of limited computational power and memory [3–6].

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Chapter
Information
Principles of Wireless Sensor Networks , pp. 30 - 47
Publisher: Cambridge University Press
Print publication year: 2014

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References

Zheng, J. and Jamalipour, A. (Eds.), Wireless Sensor Networks: A Networking Perspective, Wiley, 2009.CrossRef
Ilyas, M. and Mahgoub, I., Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, Boca Raton, FL: CRC Press, 2004.CrossRefGoogle Scholar
Ahmed, N., Dong, Y., Bokareva, T. et al., “Detection and tracking using wireless sensor networks,” in Proceedings of the 2007 International Conference on Embedded Networked Sensor Systems, pp. 425–426, Sydney, Australia, November 2007.Google Scholar
Bokareva, T., Hu, W., Kanhere, S. et al., “Wireless sensor networks for battlefield surveillance,” in Proceedings of Land Warfare Conference, Brisbane, October 2006.Google Scholar
Arora, A., Dutta, P., Bapat, S. et al., “A Line in the Sand: a wireless sensor network for target detection, classification, and tracking,” Computer Networks, Vol. 46, No. 5, pp. 605–634, December 2004.CrossRefGoogle Scholar
Raghavendra, C. S., Meesookho, C. and Narayanan, S., “Collaborative classification applications in sensor networks,” in Proceedings of 2002 IEEE Sensor Array and Multichannel Signal Processing Workshop, pp. 370–374, August 2002.
Duarte, M. and Hu, Y., “Vehicle classification in distributed sensor networks,” Journal of Parallel and Distributed Computing, Vol. 64, No. 7, pp. 826–838, July 2004.CrossRefGoogle Scholar
Zhao, F., Shin, J. and Reich, J., “Information-driven dynamic sensor collaboration for tracking applications,” IEEE Signal Processing Magazine, Vol. 19, No. 2, pp. 61–72, March 2002.CrossRefGoogle Scholar
Dutta, P. K. and Arora, A. K., “Sensing Civilians, Soldiers, and Cars,” The Ohio State University Department of Computer and Information Science Technical Report OSU-CISRC-12/03-TR66, 2003.
Liao, P.-K., Chang, M.-K. and Kuo, C., “Contour line extraction with wireless sensor networks,” Proceedings of the 2005 IEEE International Conference on Communications, ICC 2005, Vol. 5, pp. 3202–3206, May 2005.CrossRefGoogle Scholar
Dhurandher, S., Khairwal, S., Obaidat, M. S. and Misra, S., “Efficient data acquisition in underwater wireless sensor ad hoc networks,” IEEE Wireless Communications, Vol. 16, No. 6, pp. 70–78, December 2009.CrossRefGoogle Scholar
Dhurandher, S. K., Misra, S., Obaidat, M. S. and Khairwal, S., “UWSim: a simulator for underwater wireless sensor networks,” Simulation: Transactions of the Society for Modeling and Simulation International, SCS, Vol. 84, No. 7, pp. 327–338, July 2008.CrossRefGoogle Scholar
Akyildiz, I. F., Pompili, D. and Melodia, T., “Underwater acoustic sensor networks: research challenges,” Journal of Ad Hoc Networks, Elsevier, Vol. 3, No. 3, pp. 257–279, March 2005.CrossRefGoogle Scholar
Shen, C.-C., Srisathapornphat, C. and Jaikaeo, C., “Sensor information networking architecture and applications,” IEEE Personal Communications, pp. 52–59, August 2001.CrossRefGoogle Scholar
Shi, E. and Perrig, A., “Designing secure sensor networks,” IEEE Wireless Communications, pp. 38–43, December 2004.Google Scholar
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y. and Cayirci, E., “A survey on sensor networks,” IEEE Communications Magazine, pp. 102–114, August 2002.CrossRefGoogle Scholar
García-Hernández, C. F., Ibargüengoytia-González, P. H., García-Hernández, J. and Pérez-Díaz, J. A., “Wireless sensor networks and applications: a survey,” International Journal of Computer Science and Network Security, Vol. 7, No. 3, pp. 264–273, March 2007.Google Scholar
Cui, J.-H., Kong, J., Gerla, M. and Zhou, S., “Challenges: building scalable mobile underwater wireless sensor networks for aquatic applications,” University of Connecticut, USA, UCONN CSE technical report UbiNET-TR05–02; last update, September 2005.
Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., and Anderson, J., “Wireless sensor networks for habitat monitoring,” in Proceedings of the 2nd Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, MobiQuitous’05, pp. 479–481, July 2005.
Chong, C.-Y. and Kumar, S. P., “Sensor networks: evolution, opportunities, and challenges,” Proceedings of the IEEE, Vol. 91, No. 8, pp. 1247–1256, August 2003.CrossRefGoogle Scholar
Caruso, M. J. and Withanawasam, L. S., “Vehicle detection and compass applications using AMR magnetic sensors, AMR sensor documentation,”
Simic, S. N. and Sastry, S., “Distributed environmental monitoring using random sensor networks,” in Proceedings of Information Processing in Sensor Networks, IPSN 2003, pp. 582–592, 2003.
Stojanovic, M., “Underwater acoustic communication,” Encyclopedia of Electrical and Electronics Engineering, Webster, J. G., Ed., Wiley, 1999, Vol. 22, pp. 688–98.Google Scholar
Michel, J., “Assessment and recovery of submerged oil: current state analysis,” Research & Development Center, U.S. Coast Guard, 2006; (accessed May 10, 2008).
Cui, J.-H. et al., “Challenges: building scalable mobile underwater wireless sensor networks for aquatic applications,” UCONN CSE technical report UbiNET-TR05–02; last update, Sept. 2005.
Kong, J. et al., “Building underwater ad-hoc networks and sensor networks for large scale real-time aquatic application,” Proceedings IEEE MILCOM, 2005.Google Scholar
Stojanovic, M., “Recent advances in high speed under water acoustic communication,” IEEE Journal of Oceanic Engineering, Vol. 21, No. 2, pp. 125–36, 1996.CrossRefGoogle Scholar
Heidemann, J. et al., “Underwater sensor networking: research challenges and potential applications,” USC/ISI technical report ISI-TR-2005–603.
Carbone, N. M. and Hodgkiss, W. S., “Effects of tidally driven temperature fluctuations on shallow-water acoustic communications at 18 kHz,” IEEE Journal of Oceanic Engineering, Vol. 25, No. 1, pp. 84–94, 2000.CrossRefGoogle Scholar

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