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A cluster-tree-based energy-efficient routing protocol for wireless sensor networks with a mobile sink

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Abstract

With the rapid development of the Internet of Things technology, wireless sensor networks as the core technique have been applied in military, precision agriculture, security monitoring, intelligent traffic, instrument monitoring and so on. In wireless sensor networks with mobile sinks, the sensor nodes have limited energy supply and the sink is removable. To improve the network efficiency and the sink utilization, we present a cluster tree-based energy-efficient routing protocol (CTEER). The protocol is based on rendezvous which is capable of reducing the latency. CTEER firstly plans the mobile path for the sink and creates a cross-communication region. Then, a cross-routing tree with the mobile sink as the center is constructed within the region. The routing tree changes with the location of the sink in each round so that the utilization rate of sensor nodes can be improved. Meanwhile, we divide the ordinary nodes outside the cross-region into multiple clusters. The ordinary nodes can send data to the routing tree directly through the cluster heads. In this way, transmission hops from ordinary nodes to the routing tree can be decreased. Thus, the data latency can be reduced. Experimental results show that CTEER protocol keeps competitive in terms of energy saving, network lifetime and data latency reduction. In four experimental environments, compared with RRP, the total energy consumption of nodes in each round in CTEER is decreased by 77.58%, 62.03%, 57.01% and 50.95%, respectively. In three experimental environments, compared with FRM, the total energy consumption of nodes in each round in CTEER is reduced by 35.04%, 29.93% and 4.08%, respectively. Compared with RRP, the total hops of sensor nodes in each round in CTEER are reduced by 82.44%, 75.56%, 74.16% and 78.04%, respectively. Compared with FRM, the hops are decreased by 43.25%, 40.19%, 39.10% and 39.64%, respectively. Considering the low delay advantage, CTTEER is suitable for time-sensitive applications. Some examples of the applications would be target tracking, telemonitoring of human health status and artificial intelligence applications.

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References

  1. Sheth A, Jaimini U, Yip HY (2018) How will the internet of things enable augmented personalized health? IEEE Intell Syst 33(1):89–97

    Article  Google Scholar 

  2. Ahmadreza V, Gongxuan Z, Junlong Z et al (2018) A new path-constrained rendezvous planning approach for large-scale event-driven wireless sensor networks. Sensors (Basel) 18(5):1434

    Article  Google Scholar 

  3. Hou YT, Shi Y, Sherali HD et al (2005) On energy provisioning and relay node placement for wireless sensor networks. IEEE Trans Wireless Commun 4(5):2579–2590

    Article  Google Scholar 

  4. Li J, Mohapatra P (2006) An analytical model for the energy hole problem in many-to-one sensor networks. In: IEEE Vehicular Technology Conference

  5. Song L, Hatzinakos D (2007) Architecture of wireless sensor networks with mobile sinks: sparsely deployed sensors. IEEE Trans Veh Technol 56(4):1826–1836

    Article  Google Scholar 

  6. Liu Z, Qiu Z (2007) A local non uniform data collection scheme for mobile user in wireless sensor networks. In: IET Conference on Wireless, Mobile and Sensor Networks 2007 (CCWMSN07), pp 612–615

  7. Gatzianas M, Georgiadis L (2008) A distributed algorithm for maximum lifetime routing in sensor networks with mobile sink. IEEE Trans Wireless Commun 7(3):984–994

    Article  Google Scholar 

  8. Gu Y, Ji Y, Li J et al (2013) ESWC: efficient scheduling for the mobile sink in wireless sensor networks with delay constraint. IEEE Trans Parallel Distrib Syst 24(7):1310–1320

    Article  Google Scholar 

  9. Zhu C, Shuai WU, Han G et al (2015) A tree-cluster-based data-gathering algorithm for industrial WSNS with a mobile sink. IEEE Access 3:381–396

    Article  Google Scholar 

  10. Chang JY, Shen TH (2016) An efficient tree-based power saving scheme for wireless sensor networks with mobile sink. IEEE Sens J 16(20):7545–7557

    Article  Google Scholar 

  11. Sharma S, Puthal D, Jena SK et al (2016) Rendezvous based routing protocol for wireless sensor networks with mobile sink. J Supercomput 73(3):1168–1188

    Article  Google Scholar 

  12. Li C, Bai J, Gu J et al (2018) Clustering routing based on mixed integer programming for heterogeneous wireless sensor networks. Ad Hoc Netw 72:81–90

    Article  Google Scholar 

  13. Darabkh KA, Al-Maaitah NJ, Jafar IF et al (2018) EA-CRP: a novel energy-aware clustering and routing protocol in wireless sensor networks. Comput Electr Eng 72:702–718

    Article  Google Scholar 

  14. Manfredi S, Natalizio E, Pascariello C et al (2020) Stability and convergence of a message-loss-tolerant rendezvous algorithm for wireless networked robot systems. IEEE Trans Control Netw Syst 7(3):1103–1114

    Article  MathSciNet  MATH  Google Scholar 

  15. Dinh NT, Gu T, Kim Y (2019) Rendezvous cost-aware opportunistic routing in heterogeneous duty-cycled wireless sensor network systems. IEEE Access 7:121825–121840

    Article  Google Scholar 

  16. Hamerly G, Elkan C (2003) Learning the k in k-means. Adv Neural Inf Process Syst 16:281–288

    Google Scholar 

  17. Lu YM, Wong VWS (2006) An energy-efficient multipath routing protocol for wireless sensor networks. In: IEEE Vehicular Technology Conference

  18. Liao Y, Qi H, Li W (2013) Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks. IEEE Sens J 13(5):1498–1506

    Article  Google Scholar 

  19. Baccour N, Youssef H (2015) Reliable link quality estimation in low-power wireless networks and its impact on tree-routing. Ad Hoc Netw 27:1–25

    Article  Google Scholar 

  20. Xie S, Wang Y (2014) Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wireless Pers Commun 78(1):231–246

    Article  Google Scholar 

  21. Suganthi K, Vinayagasundaram B, Aarthi J (2015) Randomized fault-tolerant virtual backbone tree to improve the lifetime of wireless sensor networks. Comput Electr Eng 48:286–297

    Article  Google Scholar 

  22. Han Z, Wu J, Zhang J et al (2014) A general self-organized tree-based energy-balance routing protocol for wireless sensor network. IEEE Trans Nucl Sci 61(2):732–740

    Article  Google Scholar 

  23. Villas LA, Boukerche A, Ramos HS et al (2013) DRINA: A lightweight and reliable routing approach for in-network aggregation in wireless sensor networks. IEEE Trans Comput 62(4):676–689

    Article  MathSciNet  MATH  Google Scholar 

  24. Karaboga D, Okdem S, Ozturk C (2012) Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Netw 18(7):847–860

    Article  Google Scholar 

  25. Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379

    Article  Google Scholar 

  26. Sohn I, Lee J, Lee SH (2016) Low-energy adaptive clustering hierarchy using affinity propagation for wireless sensor networks. IEEE Commun Lett 20(3):558–561

    Article  Google Scholar 

  27. Heinzelman W R, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences

  28. Wang T, Zhang G, Yang X et al (2018) Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks. J Syst Softw 146:196–214

    Article  Google Scholar 

  29. Yun YS, Xia Y (2010) Maximizing the lifetime of wireless sensor networks with mobile sink in delay-tolerant applications. IEEE Trans Mob Comput 9(9):1308–1318

    Article  Google Scholar 

  30. Kleerekoper A, Filer NP (2014) DECOR: Distributed construction of load balanced routing trees for many to one sensor networks. Ad Hoc Netw 16(2):225–236

    Article  Google Scholar 

  31. Han SW, Jeong IS, Kang SH (2013) Low latency and energy efficient routing tree for wireless sensor networks with multiple mobile sinks. J Netw Comput Appl 36(1):156–166

    Article  Google Scholar 

  32. Liu WJ, Fan JX, Zhang SK et al (2013) Clustering-based data gathering in wireless sensor network with mobile collector. Appl Mech Mater 336–338(1):261–264

    Google Scholar 

  33. Saranya V, Shankar S, Kanagachidambaresan GR (2018) Energy efficient clustering scheme (EECS) for wireless sensor network with mobile sink. Wireless Pers Commun 100(4):1553–1567

    Article  Google Scholar 

  34. Tang J, Yang W, Zhu L et al (2017) An adaptive clustering approach based on minimum travel route planning for wireless sensor networks with a mobile sink. Sensors (Basel) 17(5):964

    Article  Google Scholar 

  35. Nayak SP, Rai SC, Pradhan S (2017) A multi-clustering approach to achieve energy efficiency using mobile sink in WSN. In: Behera H, Mohapatra D (eds) Computational intelligence in data mining. Advances in Intelligent Systems and Computing, vol 556. Springer, Singapore

  36. Zhang R, Pan J, Xie D et al (2016) NDCMC: A hybrid data collection approach for large-scale WSNS using mobile element and hierarchical clustering. IEEE Internet Things J 3(4):533–543

    Article  Google Scholar 

  37. Wang J, Cao Y, Li B et al (2016) Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Gener Comput Syst 76:452–457

    Article  Google Scholar 

  38. Sha C, Qiu JM, Lu TY et al (2017) Virtual region based data gathering method with mobile sink for sensor networks. Wireless Netw 24(8):1793–1807

    Google Scholar 

  39. Pottie GJ (1999) Wireless integrated network sensors. Wireless Integr Netw Sens Next Gener 43(5):51–58

    Google Scholar 

Download references

Acknowledgements

The research in the paper is supported by the National Natural Science Foundation of China (81674099,81804219); National Key Research and Development Program of China (2017YFC1703500); Jiangsu Province Science Foundation (BK20180822).

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Correspondence to TianShu Wang.

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Lu, J., Hu, K., Yang, X. et al. A cluster-tree-based energy-efficient routing protocol for wireless sensor networks with a mobile sink. J Supercomput 77, 6078–6104 (2021). https://doi.org/10.1007/s11227-020-03501-w

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