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Erschienen in: Wireless Networks 4/2015

01.05.2015

Distributed multi chain compressive sensing based routing algorithm for wireless sensor networks

verfasst von: Ahmed Salim, Walid Osamy

Erschienen in: Wireless Networks | Ausgabe 4/2015

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Abstract

In wireless sensor networking applications, collecting sensed data and relaying it to the base station in an energy efficient manner is of paramount importance. It follows that power management and energy-efficient communication techniques become necessary to maximize network lifetime for wireless sensor networks (WSNs). In this paper, our objective is to distribute energy consumption evenly and maximize the network lifetime by utilizing data aggregation and in-network compression technique. We mainly focus on the combined problem of data routing with data aggregation during routing such that minimizing the number of packets to transmit and achieve our objective. We propose a multi chain routing algorithm executed with compressive sensing for data aggregating in WSNs. We show that by using our compressive sensing based routing algorithm significant reduction in data traffic can be achieved, resulting in power saving and thus prolong the network lifetime.

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Literatur
1.
Zurück zum Zitat Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002) Energy efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference, 2000, Vol. 2, pp. 3005–3014. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002) Energy efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference, 2000, Vol. 2, pp. 3005–3014.
2.
Zurück zum Zitat Chou, J., Petrovic, D., & Ramchandran, K. (2003). A distributed and adaptive signal processing approach toreducing energy consumption in sensor networks. In Proceedings of IEEE Infocom, pp. 1054–1062 Chou, J., Petrovic, D., & Ramchandran, K. (2003). A distributed and adaptive signal processing approach toreducing energy consumption in sensor networks. In Proceedings of IEEE Infocom, pp. 1054–1062
3.
Zurück zum Zitat Anastasi, Giuseppe, Conti, Marco, Di Francesco, Mario, & Passarella, Andrea. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Network, 7(3), 537–568.CrossRef Anastasi, Giuseppe, Conti, Marco, Di Francesco, Mario, & Passarella, Andrea. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Network, 7(3), 537–568.CrossRef
4.
Zurück zum Zitat Fasolo, E., Rossi, M., Widmer, J., & Zorzi, M. (2007). In-network aggregation techniques for wireless sensor networks: A survey. IEEE Wireless Communications, 14(2), 7087.CrossRef Fasolo, E., Rossi, M., Widmer, J., & Zorzi, M. (2007). In-network aggregation techniques for wireless sensor networks: A survey. IEEE Wireless Communications, 14(2), 7087.CrossRef
5.
Zurück zum Zitat Ahn, G., Hong, S. G., Miluzzo, E., Campbell, A. T., & Cuomo, F. (2006). Funneling-MAC: A localized, sink-oriented MAC for boosting fidelity in sensor networks. In Proceedings of the 4th international conference on embedded networked sensor systems, Vol. 293, p. 306. doi:10.1145/1182807.1182837. Ahn, G., Hong, S. G., Miluzzo, E., Campbell, A. T., & Cuomo, F. (2006). Funneling-MAC: A localized, sink-oriented MAC for boosting fidelity in sensor networks. In Proceedings of the 4th international conference on embedded networked sensor systems, Vol. 293, p. 306. doi:10.​1145/​1182807.​1182837.
6.
Zurück zum Zitat Boulis, A., Ganeriwal, S., & Srivastava, M. B. (2003). Aggregation in sensor networks: An energy-accuracy trade-off. In Proceedings of the 1st IEEE international workshop on sensor network protocols and applications, pp. 128-138. doi:10.1109/SNPA.2003.1203363. Boulis, A., Ganeriwal, S., & Srivastava, M. B. (2003). Aggregation in sensor networks: An energy-accuracy trade-off. In Proceedings of the 1st IEEE international workshop on sensor network protocols and applications, pp. 128-138. doi:10.​1109/​SNPA.​2003.​1203363.
7.
Zurück zum Zitat Intanagonwiwat, C., Estrin, D., Govindan, R., & Heidemann, J. (2002). Impact of network density on data aggregation in wireless sensor networks. In Proceedings of 22nd international conference on distributed computing systems, pp. 457–458. doi:10.1109/ICDCS.2002.1022289. Intanagonwiwat, C., Estrin, D., Govindan, R., & Heidemann, J. (2002). Impact of network density on data aggregation in wireless sensor networks. In Proceedings of 22nd international conference on distributed computing systems, pp. 457–458. doi:10.​1109/​ICDCS.​2002.​1022289.
8.
Zurück zum Zitat Galluccio, L., Campbell, A. T., & Palazzo, S. (2005). Concert: Aggregation- based congestion control for sensor networks. In Proceedings of the 3rd international conference on embedded networked sensor systems, pp. 274–275. doi:10.1145/1098918.1098951. Galluccio, L., Campbell, A. T., & Palazzo, S. (2005). Concert: Aggregation- based congestion control for sensor networks. In Proceedings of the 3rd international conference on embedded networked sensor systems, pp. 274–275. doi:10.​1145/​1098918.​1098951.
9.
Zurück zum Zitat Galluccio, L., Palazzo, S., & Campbell, A. T. (2009). Modeling and designing efficient data aggregation in wireless sensor networks under entropy and energy bounds. International Journal of Wireless Information Networks, 16(175), 183. Galluccio, L., Palazzo, S., & Campbell, A. T. (2009). Modeling and designing efficient data aggregation in wireless sensor networks under entropy and energy bounds. International Journal of Wireless Information Networks, 16(175), 183.
11.
Zurück zum Zitat Handy, M. J., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In 4th international workshop on mobile and wireless communications network, 2002, pp. 368–372. Handy, M. J., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In 4th international workshop on mobile and wireless communications network, 2002, pp. 368–372.
12.
Zurück zum Zitat Lindsey, S., & Raghavendra, C. (2002). PEGASIS: Power efficient gathering in sensor information systems. Aerospace Conference Proceedings, 3, 1125–1130. Lindsey, S., & Raghavendra, C. (2002). PEGASIS: Power efficient gathering in sensor information systems. Aerospace Conference Proceedings, 3, 1125–1130.
13.
Zurück zum Zitat Meenu, M., & Techand Vandana, A. P. (2012). Modified PEGASIS in WSN to increase network life time. International Journal of Computer Applications (0975–8887), 52(19), 7–10. Meenu, M., & Techand Vandana, A. P. (2012). Modified PEGASIS in WSN to increase network life time. International Journal of Computer Applications (0975–8887), 52(19), 7–10.
14.
Zurück zum Zitat Cui, J., & Valois, F. (2013). Data aggregation in wireless sensor networks: Compressing or Forecasting? Rapport de recherche, INRIA, RR-8362. Cui, J., & Valois, F. (2013). Data aggregation in wireless sensor networks: Compressing or Forecasting? Rapport de recherche, INRIA, RR-8362.
16.
Zurück zum Zitat Kolo, J. G., Shanmugam, S. A., Lim, D. W. G., Ang, L.-M., & Seng, K. P. (2012) An adaptive lossless data compression scheme for wireless sensor networks. Journal of Sensors, 2012, Article ID 539638, 20. doi:10.1155/2012/539638. Kolo, J. G., Shanmugam, S. A., Lim, D. W. G., Ang, L.-M., & Seng, K. P. (2012) An adaptive lossless data compression scheme for wireless sensor networks. Journal of Sensors, 2012, Article ID 539638, 20. doi:10.​1155/​2012/​539638.
17.
Zurück zum Zitat Chong, L., & Feng, W. (2009). Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th annual international conference on mobile computing and networking, Beijing, China, 2009. Chong, L., & Feng, W. (2009). Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th annual international conference on mobile computing and networking, Beijing, China, 2009.
18.
Zurück zum Zitat Luo, J., Xiang, L., & Rosenberg, C. (2010). Does compressed sensing improve the throughput of wireless sensor networks? In ICC, pp. 1–6. IEEE. Luo, J., Xiang, L., & Rosenberg, C. (2010). Does compressed sensing improve the throughput of wireless sensor networks? In ICC, pp. 1–6. IEEE.
19.
Zurück zum Zitat Xiang, L., Luo, J., & Vasilakos, A. V. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In SECON, pp. 46–54. IEEE. Xiang, L., Luo, J., & Vasilakos, A. V. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In SECON, pp. 46–54. IEEE.
20.
Zurück zum Zitat Haupt, J., Bajwa, W. U., Rabbat, M., & Nowak, R. (2008). Compressed sensing for networked data. IEEE on Signal Processing Magazine, 25(2), 92–101.CrossRef Haupt, J., Bajwa, W. U., Rabbat, M., & Nowak, R. (2008). Compressed sensing for networked data. IEEE on Signal Processing Magazine, 25(2), 92–101.CrossRef
21.
Zurück zum Zitat Duarte, M. F., Sarvotham, S., Wakin, M. B., Baron, D., & Baraniuk, R. G. (2005). Joint sparsity models for distributed compressed sensing. In Online Proceedings of the workshop on signal processing with adaptative sparse structured representations (SPARS), Rennes, France. Duarte, M. F., Sarvotham, S., Wakin, M. B., Baron, D., & Baraniuk, R. G. (2005). Joint sparsity models for distributed compressed sensing. In Online Proceedings of the workshop on signal processing with adaptative sparse structured representations (SPARS), Rennes, France.
22.
Zurück zum Zitat Wu, X., & Liu, M. (2012). In-situ soil moisture sensing: Measurement scheduling and estimation using compressive sensing. In F. Zhao, A. Terzis, K. Whitehouse, (Eds.), IPSN (pp. 1–12). New York: ACM. Wu, X., & Liu, M. (2012). In-situ soil moisture sensing: Measurement scheduling and estimation using compressive sensing. In F. Zhao, A. Terzis, K. Whitehouse, (Eds.), IPSN (pp. 1–12). New York: ACM.
23.
Zurück zum Zitat Wang, J., Tang, S., Yin, B., & Li, X.-Y. (2012). Data gathering in wireless sensor networks through intelligent compressive sensing. In A. G. Greenberg, K. Sohraby, (Eds.), INFOCOM, pp. 603–611. IEEE. Wang, J., Tang, S., Yin, B., & Li, X.-Y. (2012). Data gathering in wireless sensor networks through intelligent compressive sensing. In A. G. Greenberg, K. Sohraby, (Eds.), INFOCOM, pp. 603–611. IEEE.
24.
Zurück zum Zitat Zheng, H., Xiao, S., Wang, X., & Tian, X. (2012). Energy and latency analysis for in-network computation with compressive sensing in wireless sensor networks. In A. G. Greenberg, K. Sohraby, (Eds.), INFOCOM, pp. 2811–2815. IEEE. Zheng, H., Xiao, S., Wang, X., & Tian, X. (2012). Energy and latency analysis for in-network computation with compressive sensing in wireless sensor networks. In A. G. Greenberg, K. Sohraby, (Eds.), INFOCOM, pp. 2811–2815. IEEE.
25.
Zurück zum Zitat Baron, D., Duarte, M. F., Sarvotham, S., Wakin, M. B., & Baraniuk, R. G. (2005) An information theoretic approach to distributed compressed sensing. In Allerton conference on communication, control, and computing, Allerton, IL, September 2005. Baron, D., Duarte, M. F., Sarvotham, S., Wakin, M. B., & Baraniuk, R. G. (2005) An information theoretic approach to distributed compressed sensing. In Allerton conference on communication, control, and computing, Allerton, IL, September 2005.
26.
Zurück zum Zitat Choi, K., Wang, J., Zhu, L., Suh, T. S., Boyd, S., & Xing, L. (2010). Compressed sensing based cone-beam computed tomography reconstruction with a first-order method. Medical Physics, 37, 5113–5125.CrossRef Choi, K., Wang, J., Zhu, L., Suh, T. S., Boyd, S., & Xing, L. (2010). Compressed sensing based cone-beam computed tomography reconstruction with a first-order method. Medical Physics, 37, 5113–5125.CrossRef
27.
Zurück zum Zitat Cand‘es, E., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489–509.CrossRefMathSciNet Cand‘es, E., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489–509.CrossRefMathSciNet
29.
Zurück zum Zitat Tropp, J. A., & Gilbert, A. C. (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53(12), 4655–4666.CrossRefMATHMathSciNet Tropp, J. A., & Gilbert, A. C. (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53(12), 4655–4666.CrossRefMATHMathSciNet
30.
Zurück zum Zitat Ali, S. A. E.-F., Refaay, S. K. (2012). Chain based fault tolerant routing protocols. Network Protocols and Algorithms, 4(3) 79–103. Ali, S. A. E.-F., Refaay, S. K. (2012). Chain based fault tolerant routing protocols. Network Protocols and Algorithms, 4(3) 79–103.
32.
Zurück zum Zitat Werner-Allen, G., Lorincz, K., Ruiz, M., Marcillo, O., Johnson, J., Lees, J., et al. (2006). Deploying a wireless sensor network on an active volcano. In IEEE internet computing, special issue on data- driven applications in sensor networks, March/April 2006. Werner-Allen, G., Lorincz, K., Ruiz, M., Marcillo, O., Johnson, J., Lees, J., et al. (2006). Deploying a wireless sensor network on an active volcano. In IEEE internet computing, special issue on data- driven applications in sensor networks, March/April 2006.
33.
Zurück zum Zitat Li, G., He, J., & Fu, Y. (2008). Group-based intrusion detection system in wireless sensor networks. Computer Communications, 31, 4324–4332.CrossRef Li, G., He, J., & Fu, Y. (2008). Group-based intrusion detection system in wireless sensor networks. Computer Communications, 31, 4324–4332.CrossRef
Metadaten
Titel
Distributed multi chain compressive sensing based routing algorithm for wireless sensor networks
verfasst von
Ahmed Salim
Walid Osamy
Publikationsdatum
01.05.2015
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 4/2015
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-014-0852-5

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