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Hybrid routing and load balancing protocol for wireless sensor network

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Abstract

In wireless sensor network, when the nodes are mobile, the network structure keeps on changing dynamically, that is, new nodes enter the network and old members exit the network. As a result, the path from one node to the other varies from time to time. In addition, if the load on a particular part of the network is high, then the nodes will not be capable of transmitting the data. Thus, data delivery at the destination will be unsuccessful. Moreover, the part of the network involved in transmitting the data should not be overloaded. To overcome these issues, a hybrid routing protocol and load balancing technique is discussed in this paper for the mobile data collectors in which the path from source to destination is ensured before data transmission. The hybrid routing protocol that combines the reactive and proactive approach is used to enhance gradient based routing protocol for low power and lossy networks. This protocol can efficiently handle the movement of multiple sinks. Finally, load balancing is applied over the multiple mobile elements to balance the load of sensor nodes. Simulation results show that this protocol can increase the packet delivery ratio and residual energy with reduced delay and packet drop.

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References

  1. Anisi, M. H., Abdullah, A. H., & Razak, S. A. (2011). Energy-efficient data collection in wireless sensor networks. Wireless Sensor Network, 3, 329–333.

    Article  Google Scholar 

  2. Truong, T. T., Brown, K. N., & Sreenan, C. J. (2010). Using mobile sinks in wireless sensor networks to improve building emergency response. Ireland: Mobile and Internet Systems Laboratory and Cork Constraint Computation Centre, Department of Computer Science, University College Cork,

    Google Scholar 

  3. Sha, Z., Lu, J. L., Li, X., & Wu, M. Y. (2010). An anti-detection moving strategy for mobile sink. In Proceedings of IEEE, Globecom.

  4. Kinalis, A., Nikoletseas, S., Patroumpa, D., & Rolim, J. (2014). Biased sink mobility with adaptive stop times for low latency data collection in sensor networks. Information Fusion, 15, 56–63.

    Article  Google Scholar 

  5. Li, M., et al. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.

    Article  Google Scholar 

  6. Han, K., et al. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.

    Article  Google Scholar 

  7. Sheng, Z., et al. (2013). A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities. Wireless Communications, IEEE, 20(6), 91–98.

    Article  Google Scholar 

  8. Zhou, L., et al. (2010). Context-aware middleware for multimedia services in heterogeneous networks. IEEE Intelligent Systems, 25(2), 40–47.

    Article  Google Scholar 

  9. Cheng, L., Chen, Y., Chen, C., & Ma, J. (2009). Query-based data collection in wireless sensor networks with mobile sinks. In Proceedings of the International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly (pp 1157–1162). ACM.

  10. Acampora, G., et al. (2010). Interoperable and adaptive fuzzy services for ambient intelligence applications ACM transactions on autonomous and adaptive systems (TAAS), 5(2), 8.

    Google Scholar 

  11. Zhou, J., et al. (2015). Secure and privacy preserving protocol for cloud-based vehicular DTNs. IEEE Transactions on Information Forensics and Security, 10(6), 1299–1314.

    Article  Google Scholar 

  12. Fadlullah, Z. M., et al. (2010). DTRAB: Combating against attacks on encrypted protocols through traffic-feature analysis. IEEE/ACM Transactions on Networking, 18(4), 1234–1247.

    Article  Google Scholar 

  13. Jing, Q., et al. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501.

    Article  Google Scholar 

  14. Park, T., Kim, D., Jang, S., Yoo, S. E., & Lee, Y. (2009). Energy efficient and seamless data collection with mobile sinks in massive sensor networks. In Parallel & Distributed Processing, IPDPS, IEEE International Symposium, Rome.

  15. Wang, X., Wang, S., Bi, D. W., & Ma, J. J. (2007). Distributed peer-to-peer target tracking in wireless sensor networks. Sensors, 7, 1001–1027.

    Article  Google Scholar 

  16. Yan, Z., et al. (2014). A survey on trust management for internet of things. Journal of Network and Computer Applications, 42, 120–134.

    Article  Google Scholar 

  17. Vasilakos, A., et al. (2012). Delay tolerant networks: Protocols and applications. Boca Raton: CRC Press.

    Google Scholar 

  18. Song, Y., et al. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

    Article  Google Scholar 

  19. Liu, L., et al. (2015). Physarum optimization: A biology-inspired algorithm for the steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 819–832.

    MathSciNet  Google Scholar 

  20. Du, J., Liu, H., Shangguan, L., Mai, L., Wang, K., & Li, S. (2012). Rendezvous data collection using a mobile element in heterogeneous sensor networks. International Journal of Distributed Sensor Networks, 2012, 686172. doi:10.1155/2012/686172.

    Google Scholar 

  21. Anisi, M. H., Abdullah, A. H., & Razak, S. A. (2011). Efficient data aggregation in wireless sensor networks. In International Conference on Future Information Technology IPCSIT (Vol. 13).

  22. Wei, G., et al. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  23. Liu, X. Y., et al. (2015). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2188–2197.

    Article  Google Scholar 

  24. Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks (TOSN), 11(3), 45.

    Article  Google Scholar 

  25. Liu X, et al. (2011) Compressed data aggregation for energy efficient wireless sensor networks. In 8th Annual IEEE Conference SECON (pp. 46–54).

  26. Chilamkurti, N,. et al. (2009) Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors, 2009, 134165. doi:10.1155/2009/134165.

  27. Safdar, V., Bashir, F., Hamid, Z., Afzal, H., & Pyun, J. Y. (2012). A hybrid routing protocol for wireless sensor networks with mobile sinks. In Wireless and Pervasive Computing (ISWPC), 7th International Symposium on Dalian (pp. 1–5).

  28. Xiao, Y., et al. (2012). Tight performance bounds of multihop fair access for MAC protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions on Mobile Computing, 11(10), 1538–1554.

    Article  Google Scholar 

  29. Zeng, Y., et al. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  30. Liu, Y., et al. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.

    Article  Google Scholar 

  31. Bhuiyan, M. Z. A., Wang, G., Vasilakos, A. V., et al. (2015). Local area prediction-based mobile target tracking in wireless sensor networks. IEEE Transactions Computers, 64(7), 1968–1982.

    Article  MathSciNet  Google Scholar 

  32. Busch, C., et al. (2012). Approximating congestion + dilation in networks via “Quality of Routing” games. IEEE Transactions Computers, 61(9), 1270–1283.

    Article  MathSciNet  Google Scholar 

  33. Tzevelekas, L., & Stavrakakis, I. (2010) Sink mobility schemes for data extraction in large scale WSNs under single or zero hop data forwarding. In Wireless Conference (EW), 2010 European. IEEE.

  34. Sengupta, S., et al. (2012). An evolutionary multi objective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 42(6), 1093–1102.

    Article  Google Scholar 

  35. Li, P., et al. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3264–3273.

    Article  Google Scholar 

  36. Dvir, A., et al. (2011). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 41(4), 405–406.

    MathSciNet  Google Scholar 

  37. Meng, T., et al. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE Transactions on Computers. doi:10.1109/TC.2015.2417543.

    Google Scholar 

  38. Yao, Y., et al. (2013) EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks (MASS) (pp. 182–190).

    Google Scholar 

  39. Yao, Y., et al. (2015) EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23(3).

  40. Palani, U., Alamelu Mangai, V., & Nachiappan, A. (2014). Compressive network coding based mobile data gathering technique for wireless sensor networks. In IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) (pp. 951–957), Ramanathapuram.

  41. Arshad, M., Armi, N., Kamel, N., & Saad, N. M. (2011). Mobile data collector based routing protocol for wireless sensor networks. Scientific Research and Essays, 6(29), 6162–6175.

    Google Scholar 

  42. Kim, J. W., In, J. S., Hur, K., Kim, J. W., & Eom, D. S. (2010). An intelligent agent-based routing structure for mobile sinks in WSNs. IEEE Transactions on Consumer Electronics, 56, 4.

    Google Scholar 

  43. Jea, D., Somasundara, A., & Srivastava, M. (2005). Multiple controlled mobile elements (data mules) for data collection in sensor networks (pp. 244-257).

    Google Scholar 

  44. Network Simulator. http://www.isi.edu/nsnam/ns

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, Annamalai University, Chidambaram, India

    U. Palani & V. Alamelumangai

  2. Department of Electrical and Electronics Engineering, Pondicherry Engineering College, Puducherry, India

    Alamelu Nachiappan

Authors
  1. U. Palani
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  2. V. Alamelumangai
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  3. Alamelu Nachiappan
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Correspondence to U. Palani.

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Palani, U., Alamelumangai, V. & Nachiappan, A. Hybrid routing and load balancing protocol for wireless sensor network. Wireless Netw 22, 2659–2666 (2016). https://doi.org/10.1007/s11276-015-1110-1

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  • DOI: https://doi.org/10.1007/s11276-015-1110-1

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