LIP: an efficient lightweight iterative positioning algorithm for wireless sensor networks

详细信息    查看全文

• 作者：Anahit Martirosyan ; Azzedine Boukerche
• 关键词：Wireless network ; Sensor networks ; Localization ; Iterative positioning ; Target tracking
• 刊名：Wireless Networks
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：22
• 期：3
• 页码：825-838
• 全文大小：905 KB
• 参考文献：1.Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38, 393–422.CrossRef
  2.Dey, A. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 1–12.CrossRef
  3.Xu, E., Ding, Z., & Dasgupta, S. (2013). Target tracking and mobile sensor navigation in wireless sensor networks. IEEE Transactions on Mobile Computing, 12(1), 177–186.CrossRef
  4.Bhuiyan, M., Wang, G., & Vasilakos, A. (2015) Local area prediction-based mobile target tracking in wireless sensor networks. IEEE Transactions on Computers, 64(7), 1968–1982.MathSciNet CrossRef
  5.Boukerche, A., Oliveira, H., Nakamura, E., & Loureiro, A. F. (2007). Localization systems for wireless sensor networks. IEEE Wireless Communications, 14(6), 6–12.CrossRef
  6.Oliveira, H., Boukerche, A., Nakamura, E. F., & Loureiro, A. F. (2009) . An efficient directed localization recursion protocol for wireless sensor networks. IEEE Transactions on Computers, 58(5), 677–691.MathSciNet CrossRef
  7.Li, M., Li, Z., & Vasilakos, A. V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE.
  8.Wang, X., Vasilakos, A. V., Chen, M., Liu, Y., & Kwon, T. (2012). A survey of green mobile networks: Opportunities and challenges. Mobile Networks and Applications, 17(1), 4–20.CrossRef
  9.Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. (2011). Body area networks: A survey. Mobile Networks and Applications, 16(2), 171–193.CrossRef
  10.Zeng, Y., Xiang, K., Li, D., & Vasilakos, A. V. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.CrossRef
  11.Chilamkurti, N., Zeadally, S., Vasilakos, A. V., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors, 4, 122–130.
  12.Whitehouse, K., & Culler, D. (2002). Calibration as parameter estimation in sensor networks. In Proceedings of ACM international workshop on wireless sensor networks and applications (pp. 59–67).
  13.Gribben, J., & Boukerche, A. (2014). Location error estimation in wireless ad hoc networks. Ad Hoc Networks, 13, 504–515.CrossRef
  14.Chen, H., Deng, K. P., & So, H. C. (2008). An improved DV-Hop localization algorithm with reduced node location error for wireless sensor networks. IECE Transactions on Fundamentals, E91-A(8), 2232–2236.
  15.Hightower, J., & Borriello, G. (2001). Location systems for ubiquitous computing. Computer, 34(8), 57–66.CrossRef
  16.Albowitz, J., Chen, A., & Zhang, L. (2001). Recursive position estimation in sensor networks. In Proceedings of IEEE ICNP (pp. 35–41).
  17.Savarese, C., Rabaey, J. M., & Beutel, J. (2001). Locationing in distributed ad-hoc wireless sensor networks. In ICASSP (pp. 2037–2040), May.
  18.Ding, Y., Yang, D., & Han, G. (2014) Multidimensional scaling-based localization algorithm for wireless sensor network with geometric correction. Journal of Networks, 9(3), 582–587.CrossRef
  19.Stoleru, R., Stankovic, J. A., & Son, S. H. (2007). Robust node localization for wireless sensor networks. In Proceedings of the 4th workshop on embedded networked sensors, Cork, Ireland.
  20.Chan, H., Luk, M., & Perrig, A. (2005) Using clustering information for sensor network localization. In Proceedings of the international conference on distributed computing in sensor systems, Marina del Rey, June.
  21.Boukerche, A., Oliveira, H., Nakamura, E., & Loureiro, A. F. (2008). Vehicular ad hoc networks: A new challenge for localization-based systems. Computer Communications, 31(12), 2838–2849.CrossRef
  22.Sichitiu, M. L. & Ramadurai, V. (2003). Localization of wireless sensor networks with a mobile beacon: Center for Advances Computing Communications, North Carolina State University, Technical report TR-03/06, Jul.
  23.Xiao, B., Chen, H., & Zhou,S. (2007). A walking beacon-assisted localization in wireless sensor networks. In Proceedings IEEE international conference on communication (ICC ’07) (pp. 3070–3075), June.
  24.Priyantha, N. B., Balakrishnan, H., Demaine, E., & Teller, S. (2005). Mobile-assisted localization in wireless sensor networks. In IEEE INFOCOM. Miami.
  25.Ssu, K.-F., Ou, C.-H., & Jiau, H. C. (2005). Localization with mobile anchor points in wireless sensor networks. IEEE Transactions on Vehicular Technology, 1187–1197.
  26.Koutsonikolas, D., Das, S. M., & Hu, Y. C. (2007). Path planning of mobile landmarks for localization in wireless sensor networks. Computer Communications, 30(13), 2577–2592.CrossRef
  27.Nongpiur, R. C. (2014). Improved robust node position estimation in wireless sensor networks. arXiv preprint arXiv:​1401.​7377
  28.Saric, Z. M., Kukolj, D. D., & Teslic, N. D. (2010). Acoustic source localization in wireless sensor network. Circuits Systems and Signal Processing, 29(5), 837–856. doi:10.​1007/​s00034-010-9187-3 .CrossRef MATH
  29.Zhang, J., Yang, R., & Li, J. (2013). An enhanced DV-hop localization algorithm using RSSI. International Journal of Future Generation Communication and Networking, 6(6).
  30.Savvides, A., Han, C., & Strivastava, M. B. (2001). Dynamic fine-grained localization in ad-hoc networks of sensors. In Proceedings of the 7th annual international conference on mobile computing and networking (pp. 166–179), July 2001, Rome, Italy.
  31.Savvides, A., Park, H., & Srivastava, M. (2003). The n-Hop multilateration primitive for node localization problems. Mobile Networks and Applications, 8, 443–451.CrossRef
  32.Boukerche, A. (2008). Algorithms and protocols for wireless sensor networks: Wiley series on parallel and distributed computing.
  33.Langendoen, K., & Reijers, N. (2003). Distributed localization in wireless sensor networks: A quantitative comparison. Computer Networks, 43(4), 499–518.CrossRef MATH
  34.Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocols for wireless sensor networks. In Proceedings of the 33rd annual international conference on system sciences (pp. 3005–3014), USA, January.
  35.Estrin, D., et al. http://​nesl.​ee.​ucla.​edu/​tutorials/​mobicom02
  36.Crossbow, MicaZ Wireless Measurement System: http://​www.​xbow.​com
  37.Feeney, L. M. (2001). An energy-consumption model for performance analysis of routing protocols for mobile ad hoc networks. Mobile Networks and Applications, 3(6), 239–249.CrossRef MATH
  38.Parker, T., & Langendoen, K. (2004). Refined statistic-based localization for ad-hoc sensor networks. In 47th IEEE global telecommunications conference, November.
  
• 作者单位：Anahit Martirosyan (1)
  Azzedine Boukerche (1)
  
  1. PARADISE Research Laboratory, University of Ottawa, 800 King Edward, Ottawa, ON, Canada
  
• 刊物类别：Computer Science
• 刊物主题：Computer Communication Networks
  Electronic and Computer Engineering
  Business Information Systems
  
• 出版者：Springer Netherlands
• ISSN：1572-8196

文摘

Wireless sensor networks (WSNs) are increasingly being used in remote environment monitoring, security surveillance, military applications, and health monitoring systems among many other applications. Designing efficient localization techniques have been a major obstacle towards the deployment of WSN for these applications. In this paper, we present a novel lightweight iterative positioning (LIP) algorithm for next generation of wireless sensor networks, where we propose to resolve the localization problem through the following two phases: (1) initial position estimation and (2) iterative refinement. In the initial position estimation phase, instead of flooding the network with beacon messages, we propose to limit the propagation of the messages by using a random time-to-live for the majority of the beacon nodes. In the second phase of the algorithm, the nodes select random waiting periods for correcting their position estimates based on the information received from neighbouring nodes. We propose the use of Weighted Moving Average when the nodes have received multiple position corrections from a neighbouring node in order to emphasize the corrections with a high confidence. In addition, in the refinement phase, the algorithm employs low duty-cycling for the nodes that have low confidence in their position estimates, with the goal of reducing their impact on localization of neighbouring nodes and preserving their energy. Our simulation results indicate that LIP is not only scalable, but it is also capable of providing localization accuracy comparable to the Robust Positioning Algorithm, while significantly reducing the number of messages exchanged, and achieving energy savings.