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Route Packets Uneven, Consume Energy Even: A Lifetime Expanding Routing Algorithm for WSNs

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Abstract

Wireless Sensor Networks (WSNs) are infrastructure-free networks consisting of tiny and simple environmental sensing devices. Sensor nodes collaborate through wireless and limited-range communication links to report environmental conditions to the network base-station (BS). These features need to establish efficient routing algorithms in WSNs to optimize various parameters of WSNs, including energy consumption, end-to-end delay, network lifetime, and network congestion. This paper proposes an efficient routing algorithm to evenly consume energy over the network area that eventually expands the network lifetime. The proposed algorithm directs an event occurrence report toward the BS in a multi-hop manner. Intermediate sensor nodes decide about the next receiving node of the report using a lightweight optimization process based on the network’s mean residual energy, sensors’ distance to BS, and the positions of neighboring sensors. Evaluations show that the proposed algorithm directs traffic toward the boundaries of the network, which leads to better balance in traffic and energy consumption network-wide. The proposed algorithm reduces the standard deviation of the sensor node residual energy up to 6x and, through this, expands the network lifetime by at least 36%.

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References

  1. Bhattacharjee, S., Roy, P., Ghosh, S., Misra, S., & Obaidat, M. S. (2012). Wireless sensor network-based fire detection, alarming, monitoring and prevention system for Bord-and-Pillar coal mines. The Journal of Systems and Software, 85, 571–581.

    Article  Google Scholar 

  2. Feng, J., Wang, Z., & Henkel, J. (2012). An adaptive data gathering strategy for target tracking in cluster-based wireless sensor networks, In IEEE Symposium on Computers and Communications (ISCC), Cappadocia, pp. 468–474.

  3. Chae, M. J., Yoo, H. S., Kim, J. Y., & Cho, M. Y. (2012). Development of a wireless sensor network system for suspension bridge health monitoring. Automation in Construction, 21, 237–252.

    Article  Google Scholar 

  4. Egbogah, E. E., & Fapojuwo, A. O. (2011). A survey of system architecture requirements for health care-based wireless sensor networks. Sensors, 11(5), 4875–4898.

    Article  Google Scholar 

  5. Wang, P., He, Y., & Huang, L. (2013). Near optimal scheduling of data aggregation in wireless sensor networks. Ad Hoc Networks, 11, 1287–1296.

    Article  Google Scholar 

  6. Keskin, M. E., Altınel, I. K., Aras, N., & Ersoy, C. (2014). Wireless sensor network lifetime maximization by optimal sensor deployment, activity scheduling, data routing and sink mobility. Ad Hoc Networks, 17, 18–36.

    Article  Google Scholar 

  7. Safa, H. (2014). A novel localization algorithm for large scale wireless sensor networks. Computer Communications, 45, 32–46.

    Article  MathSciNet  Google Scholar 

  8. Arakaki, R., & Luiz Usberti, F. (2018). Hybrid genetic algorithm for the open capacitated arc routing problem. Computers Operations Research, 90, 221–231.

    Article  MathSciNet  Google Scholar 

  9. Marinaki, M., & Marinakis, Y. (2016). A glowworm swarm optimization algorithm for the vehicle routing problem with stochastic demands. Expert Systems with Applications, 46, 145–163.

    Article  Google Scholar 

  10. Brandl, M., Kellner, K., & Fabian, Ch. (2012). Simulation and implementation of an attractiveness based on-demand routing algorithm for wireless sensor networks. Procedia Engineering, 47, 908–911.

    Article  Google Scholar 

  11. Sahin, D., Gungor, V. C., Kocak, T., & Tuna, G. (2014). Quality-of-service differentiation in single-path and multi-path routing for wireless sensor network-based smart grid applications. Ad Hoc Networks, 22, 43–60.

    Article  Google Scholar 

  12. Li, F., Xiong, M., Wang, L., Peng, H., Hua, J., & Liu, X. (2018). A novel energy-balanced routing algorithm in energy harvesting sensor networks. Physical Communication: In Press, Accepted Manuscript.

  13. Goe, R., & Maini, R. (2018). A hybrid of ant colony and firefly algorithms (HAFA) for solving vehicle routing problems. Journal of Computational Science, 25, 28–37.

    Article  MathSciNet  Google Scholar 

  14. Wang, X., Zhang, J., Huang, M., & Yang, Sh. (2017). A green intelligent routing algorithm supporting flexible QoS for many-to-many multicast. Computer Networks, 126, 229–245.

    Article  Google Scholar 

  15. Somasundara, A. A., et al. (2006). Controllably mobile infrastructure for low energy embedded networks. IEEE Transactions on Mobile Computing, 5(8), 958–973.

    Article  Google Scholar 

  16. Bi, Y. et al. (2007). HUMS: An autonomous moving strategy for mobile sinks in data-gathering sensor networks, EURASIP Journal on Wireless Communications and Networking.

  17. Wang, J., Yin, Y., Kim, J.U., Kim, S., & Lai, C. (2012). An mobile-sink based energy-efficient clustering algorithm for wireless sensor networks. In:IEEE 12th International Conference on Computer and Information Technology, pp. 678–683.

  18. Wang, W. P. Y. T. Y. (2010). Energy-balanced dispatch of mobile sensors in a hybrid wireless sensor network. IEEE Transactions on Parallel and Distributed Systems, 21(12), 1836–1850.

    Article  Google Scholar 

  19. Vupputuri, S., Rachuri, K. K., & Murthy, C. S. (2010). Using mobile data collectors to improve network lifetime of wireless sensor networks with reliability constraints. Journal of Parallel and Distributed Computing, 70(7), 767–778.

    Article  Google Scholar 

  20. Shah, S. R. S. J. W. B. R. (2003). Data MULEs: Modeling a three-tier architecture for sparse sensor networks, In IEEE International Workshop on Sensor Network Protocols and Applications, pp. 30–41.

  21. Luo, J., & Hubaux, J.P. (2005). Joint mobility and routing for lifetime elongation in wireless sensor networks. In Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 1735–1746.

  22. Kamarei, M., Hajimohammadi, M., Patooghy, A., & Fazeli, M. (2015). An efficient data aggregation method for event-driven WSNs: A modeling & evaluation approach. International Journal of Wireless Personal Communications, 84(1), 745–764.

    Article  Google Scholar 

  23. Ho, J. H., Shih, H. C., Liao, Y. B., & Chu, S. C. (2011). A ladder diffusion algorithm using ant colony optimization for wireless sensor networks. Information Sciences, 192(1), 204–212.

    Google Scholar 

  24. Cheng, S., & Chang, T. Y. (2012). An adaptive learning scheme for load balancing with zone partition in multi-sink wireless sensor network. Expert Systems with Applications, 39, 9427–9434.

    Article  Google Scholar 

  25. Sachan, R.S., Wazid, M., Katal, A., Singh, D.P., & Goudar, R.H. (2013). A cluster based intrusion detection and prevention technique for misdirection attack inside WSN. In International conference on Communication and Signal Processing, India, pp. 795–801.

  26. Tong, F., Xie, R., Shu, L., & Kim, Y. C. (2011). A cross-layer duty cycle mac protocol supporting a pipeline feature for wireless sensor networks. Sensors, 11(5), 5183–5201.

    Article  Google Scholar 

  27. Cheng, B. C., Liao, G. T., Tseng, R. Y., & Hsu, P. H. (2012). Network lifetime bounds for hierarchical wireless sensor networks in the presence of energy constraints. Computer Networks, 56(2), 820–831.

    Article  Google Scholar 

  28. Yoo, H., Shim, M., & Kim, D. (2011). A scalable multi-sink gradient-based routing protocol for traffic load balancing. Journal on Wireless Communications and Networking EURASIP, 85, 1–16.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Applied Science and Technology, Tehran, Iran

    Meisam Kamarei

  2. Department of Computer Science, Islamic Azad University, Aligoudarz Branch, Aligudarz, Iran

    Fatemeh Tavakoli

  3. Department of Computer System Technology, North Carolina A&T State University, Greensboro, NC, 27411, USA

    Ahmad Patooghy

Authors
  1. Meisam Kamarei
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  2. Fatemeh Tavakoli
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  3. Ahmad Patooghy
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Correspondence to Meisam Kamarei.

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Kamarei, M., Tavakoli, F. & Patooghy, A. Route Packets Uneven, Consume Energy Even: A Lifetime Expanding Routing Algorithm for WSNs. Wireless Pers Commun 125, 3133–3151 (2022). https://doi.org/10.1007/s11277-022-09702-1

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  • DOI: https://doi.org/10.1007/s11277-022-09702-1

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