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Researches on the dynamic data routing and recharging schemes for rechargeable wireless sensor networks deployed in 3-dimensional spaces

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Abstract

Wireless sensor networks are bounded by their limited energy supply. Recharging batteries energy from distance by the wireless energy transferring technique will, to certain extent, solve the energy problem of the whole network. During this discussion, we studied the data routing and the recharging schemes for rechargeable wireless sensor nodes deployed in 3-dimensional spaces. According to our objective of maximizing the time ratio of recharging device staying at the service station, we formulate the continuous model, the simplified continuous model and the (T + 1)-phased discrete model. Simulations show that rechargeable wireless sensor networks will keep on working with the help of the amount of energy obtained from recharging devices. Our solution shows better performance than genetic algorithm both in time ratio and time complexity.

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References

  1. Villas, L., Guidoni, D. L., & Ueyama, J. (2013). 3d localization in wireless sensor networks using unmanned aerial vehicle. Network Computing and Applications (NCA) (pp. 135–142).

  2. Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (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 

  3. John, H., Stojanovic, M., & Zorzi, M. (2012). Underwater sensor networks: Applications, advances and challenges. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 370(1958), 158–175.

    Article  Google Scholar 

  4. Gao, J., Xiao, Y., Liu, J., et al. (2012). A survey of communication/networking in smart grids. Future Generation Computer Systems, 28(2), 391–404.

    Article  Google Scholar 

  5. Lynch, J. P., & Loh, K. J. (2006). A summary review of wireless sensors and sensor networks for structural health monitoring. Shock and Vibration Digest, 38(2), 91–130.

    Article  Google Scholar 

  6. Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., Mccann, J., & Leung, K. (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 

  7. Jing, Q., Vasilakos, A. V., Wan, J., Lu, J., & Qiu, D. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501.

    Article  Google Scholar 

  8. Chaurasiya, V. K., Jain, N., & Nandi, G. C. (2014). A novel distance estimation approach for 3D localization in wireless sensor network using multi-dimensional scaling. Information Fusion, 15, 5–18.

    Article  Google Scholar 

  9. Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  10. Liu, X. Y., Zhu, Y., Kong, L., Liu, C., Gu, Y., Vasilakos, A. V., et al. (2015). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26, 2188–2197.

    Article  Google Scholar 

  11. Acampora, G., Gaeta, M., Loia, V., & Vasilakos, A. V. (2010). Interoperable and adaptive fuzzy services for ambient intelligence applications. ACM Transactions on Autonomous and Adaptive Systems, 5(2), 737–744.

    Article  Google Scholar 

  12. Nikolaos, P., Nikolidakis, S., & Vergados, D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 15(2), 551–591.

    Article  Google Scholar 

  13. Sausen, P. S., Sousa, J. R. B., Spohn, M. A., et al. (2007). Exploring the switching energy effect in a dynamic power management technique for wireless sensor networks, Industrial Electronics Society (IECON) (pp. 2260–2265).

  14. Sudevalayam, S., & Kulkarni, P. (2011). Energy harvesting sensor nodes: Survey and implications. IEEE Communications Surveys & Tutorials, 13(3), 443–461.

    Article  Google Scholar 

  15. Liu, A., Ren, J., Li, X., et al. (2012). Design principles and improvement of cost function based energy aware routing algorithms for wireless sensor networks. Computer Networks, 56(7), 1951–1967.

    Article  Google Scholar 

  16. 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.

    Article  Google Scholar 

  17. Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In Sensor, mesh and ad hoc communications and networks (SECON), 2011 8th annual IEEE communications society conference on (pp. 46–54). IEEE.

  18. Liu, L., Song, Y., Zhang, H., Ma, H., & Vasilakos, A. V. (2015). Physarum optimization: a biology-inspired algorithm for the steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 818–831.

    Article  MathSciNet  Google Scholar 

  19. Sengupta, S., Das, S., Nasir, M., Vasilakos, A. V., & Pedrycz, W. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems Man & Cybernetics Part C, 42(6), 1093–1102.

    Article  Google Scholar 

  20. Yao, Y., Cao, Q., & Vasilakos, A. V. (2013). Edal: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. IEEE/ACM Transactions on Networking, 23(6), 182–190.

    Google Scholar 

  21. Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). Edal: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. Networking IEEE/ACM Transactions on, 23, 810–823.

    Article  Google Scholar 

  22. Ayub, K., & Zagurskis, V. (2013). Pilot signal assisted ultra wideband medium access control algorithm for wireless sensor networks. Telecommunications Forum (TELFOR) (pp. 184–187).

  23. Busch, C., Kannan, R., & Vasilakos, A. V. (2012). Approximating congestion + dilation in networks via “quality of routing” games. IEEE Transactions on Computers, 61(9), 1270–1283.

    Article  MathSciNet  Google Scholar 

  24. Fei, Qin, & Mitchell, John E. (2013). AS-MAC: Utilizing the adaptive spreading code length for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 10(1), 1.

    Google Scholar 

  25. Dash, S., Swain, A. R., & Ajay, A. (2012). Reliable energy aware multi-token based mac protocol for wsn. Advanced Information Networking and Applications (AINA) (pp. 144–151).

  26. Petcharat, Suriyachai, Roedig, Utz, & Scott, Andrew. (2012). A survey of MAC protocols for mission-critical applications in wireless sensor networks. IEEE Communications Surveys & Tutorials, 14(2), 240–264.

    Article  Google Scholar 

  27. Han, K., Luo, J., Liu, Y., & Vasilakos, A. V. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. Communications Magazine IEEE, 51(7), 107–113.

    Article  Google Scholar 

  28. Liu, Y., Xiong, N., Zhao, Y., Vasilakos, A. V., Gao, J., & Jia, Y. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.

    Article  Google Scholar 

  29. Li, P., Guo, S., Yu, S., & Vasilakos, A. V. (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 

  30. Dvir, A., & Vasilakos, A. V. (2010). Backpressure-based routing protocol for DTNS. Acm Sigcomm Computer Communication Review, 40(4), 405–406.

    Article  Google Scholar 

  31. Vasilakos, A. V., Zhang, Y., & Spyropoulos, T. (2011). Delay tolerant networks: Protocols and applications. Springer Protocols Handbooks (Vol. 11, pp. 1–6).

  32. Meng, T., Wu, F., Yang, Z., & Chen, G. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE Transactions on Computers, 65(1), 244–255.

    Article  MathSciNet  Google Scholar 

  33. Zhou, J., Dong, X., Cao, Z., & Vasilakos, A. V. (2015). Secure and privacy preserving protocol for cloud-based vehicular dtns. IEEE Transactions on Information Forensics and Security, 10, 1299–1314.

    Article  Google Scholar 

  34. Fadlullah, Z. M., Taleb, T., Vasilakos, A. V., Guizani, M., & Kato, N. (2010). Dtrab: combating against attacks on encrypted protocols through traffic-feature analysis. IEEE/ACM Transactions on Networking, 18(4), 1234–1247.

    Article  Google Scholar 

  35. Xiao, Y., Peng, M., Gibson, J. H., Xie, G. G., Du, D. Z., & Vasilakos, A. V. (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 

  36. Gokturk, S., Gurbuz, O., & Erkip, E. (2013). A cross-layer multi-hop cooperative network architecture for wireless ad hoc networks. Computer Networks, 57(18), 4010–4029.

    Article  Google Scholar 

  37. Wang, Y., & Garcia-Luna-Aceves, J. J. (2015). A distributed cross-layer routing protocol with channel assignment in multi-channel MANET. Computing, Networking and Communications (ICNC) (pp. 1050–1054).

  38. Singh, R., & Chouhan, S. (2015). A cross-layer MAC protocol for contention reduction and pipelined flow optimization in wireless sensor networks. Recent Trends in Information Systems (ReTIS) (pp. 58–63).

  39. Chilamkurti, N., Zeadally, S., Vasilakos, A., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors, 1(3), 357–361.

    Google Scholar 

  40. Tesla, N. (1901). Method of utilizing radiant energy. U.S. Patent No. 685,958.

  41. Tesla, N. (1901). Apparatus for the utilization of radiant energy. U.S. Patent No. 685,957.

  42. Tesla, N. (1900). Apparatus for transmission of electrical energy. U.S. Patent No. 649,621.

  43. Kurs, A., Karalis, A., & Moffatt, R. (2007). Wireless power transfer via strongly coupled magnetic resonances. Science, 317(5834), 83–86.

    Article  MathSciNet  Google Scholar 

  44. Shi, Y., Xie, L., Hou, Y. T., & Sherali, H. D. (2011). On renewable sensor networks with wireless energy transfer. Proceedings of IEEE INFOCOM, 2(3), 1350–1358.

    Google Scholar 

  45. Wang, C., Li, J., Ye, F., & Yang, Y. (2014). Recharging schedules for wireless sensor networks with vehicle movement costs and capacity constraints. In Sensing, communication, and networking (SECON), 2014 eleventh annual IEEE international conference on (pp. 468–476). IEEE.

  46. Xie, L., Shi, Y., Hou, Y. T., Lou, W., Sherali, H. D., & Midkiff, S. F. (2012). On renewable sensor networks with wireless energy transfer: The multi-node case. In Sensor, mesh and ad hoc communications and networks (SECON), 2012 9th annual IEEE communications society conference on (Vol. 2, pp. 10–18). IEEE.

  47. Li, J., Wang, C., Ye, F., & Yang, Y. (2014). Netwrap: An ndn based real time wireless recharging framework for wireless sensor networks. IEEE Transactions on Mobile Computing, 13(6), 173–181.

    Google Scholar 

  48. Peng, Y., Li, Z., Zhang, W., & Qiao, D. (2010). Prolonging sensor network lifetime through wireless charging. In Real-time systems symposium (RTSS), 2010 IEEE 31st (Vol. 41, pp. 129–139). IEEE.

  49. Wang, C., Li, J., Ye, F., & Yang, Y. (2013). Multi-vehicle coordination for wireless energy replenishment in sensor networks. In Parallel & distributed processing (IPDPS), 2013 IEEE 27th international symposium on (pp. 1101–1111). IEEE.

  50. Li, Z., Peng, Y., Zhang, W., & Qiao, D. (2011). J-RoC: A joint routing and charging scheme to prolong sensor network lifetime. In IEEE international conference on network protocols (pp. 373–382). IEEE Computer Society.

  51. Ding, X., Han, J., & Shi, L. (2015). The optimization based dynamic and cyclic working strategies for rechargeable wireless sensor networks with multiple base stations and wireless energy transfer devices. Sensors, 15(3), 6270–6305.

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank the anonymous reviewers and editors for their valuable comments. The material presented in this paper is based upon work funded by National Natural Science Foundation of China (61370088,61502142); International Science & Technology Cooperation Program of China (No. 2014DFB10060).

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, 230009, People’s Republic of China

    Junyi Xu, Zhenchun Wei, Jianghong Han & Chao Li

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  1. Junyi Xu
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Correspondence to Zhenchun Wei.

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Xu, J., Wei, Z., Han, J. et al. Researches on the dynamic data routing and recharging schemes for rechargeable wireless sensor networks deployed in 3-dimensional spaces. Wireless Netw 23, 1035–1044 (2017). https://doi.org/10.1007/s11276-016-1192-4

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