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Target-aware distributed coverage and connectivity algorithm for wireless sensor networks

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Abstract

One of the wireless sensor networks applications is to sense a discrete set of targets lying on the field and maintain connectivity with the sink for data transmission. In addition, it needs to minimize energy consumption to maximize the coverage lifetime. One such solution for coverage maximization is to group sensor nodes into cover sets. Each cover set remains active at a time to keep track of all the targets in the field until one of its active nodes depletes energy completely. Therefore, maximizing the number of cover sets and enhancing each set’s coverage lifetime is a challenging issue. In this paper, we propose a new energy-aware algorithm for the coverage and connectivity of the sensor nodes. In the algorithm, we devise an energy-efficient strategy to maximize the number of cover sets and energy-aware connectivity. Extensive simulation runs show that the proposed algorithm outperforms the existing ones.

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References

  1. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer networks, 52(12), 2292–2330.

    Article  Google Scholar 

  2. Praveen Kumar, D., Amgoth, T., & Annavarapu, C. S. R. (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49, 1–25.

    Article  Google Scholar 

  3. Farsi, M., Elhosseini, M. A., Badawy, M., Ali, H. A., & Eldin, H. Z. (2019). Deployment techniques in wireless sensor networks, coverage and connectivity: A survey. IEEE Access, 7, 28940–28954.

    Article  Google Scholar 

  4. Zorbas, D., & Razafindralambo, T. (2013). Prolonging network lifetime under probabilistic target coverage in wireless mobile sensor networks. Computer Communications, 36(9), 1039–1053.

    Article  Google Scholar 

  5. Sohal, A. K., Sharma, A. K., Sood, N. (2020). Energy-efficient heterogeneous WCEP for enhancing coverage lifetime in WSNs. In Ambient communications and computer systems, (pp. 25–36). Springer

  6. Boukerche, A., & Sun, P. (2018). Connectivity and coverage based protocols for wireless sensor networks. Ad Hoc Networks, 80, 54–69.

    Article  Google Scholar 

  7. Pei, X., Jianguo, W., Shang, C., & Chang, C.-Y. (2019). GSMS: A barrier coverage algorithm for joint surveillance quality and network lifetime in WSNS. IEEE Access, 7, 159608–159621.

    Article  Google Scholar 

  8. Roselin, J., Latha, P., & Benitta, S. (2017). Maximizing the wireless sensor networks lifetime through energy efficient connected coverage. Ad Hoc Networks, 62, 1–10.

    Article  Google Scholar 

  9. Tripathi, A., Gupta, H. P., Dutta, T., Mishra, R., Shukla, K. K., & Jit, S. (2018). Coverage and connectivity in WSNs: A survey, research issues and challenges. IEEE Access, 6, 26971–26992.

    Article  Google Scholar 

  10. Kabakulak, B. (2019). Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor networks. Ad Hoc Networks, 86, 83–102.

    Article  Google Scholar 

  11. Le Nguyen, P., Hanh, N. T., Khuong, N. T., Binh, H. T. T., & Ji, Y. (2019). Node placement for connected target coverage in wireless sensor networks with dynamic sinks. Pervasive and Mobile Computing, 59, 101070.

    Article  Google Scholar 

  12. Huang, C., Huang, G., Liu, W., Wang, R., & Xie, M. (2021). A parallel joint optimized relay selection protocol for wake-up radio enabled WSNs. Physical Communication, 47, 101320.

    Article  Google Scholar 

  13. Donta, P. K., Amgoth, T., & Annavarapu, C. S. R. (2022). Delay-aware data fusion in duty-cycled wireless sensor networks: A Q-learning approach. Sustainable Computing: Informatics and Systems, 33, 100642.

    Google Scholar 

  14. Sharma, A., & Chauhan, S. (2020). A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network. Wireless Networks, 26(6), 4411–4429.

    Article  Google Scholar 

  15. Chaya, S., & Jayasree, P. V. Y. (2021). Hybrid gravitational search algorithm based model for optimizing coverage and connectivity in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2835–2848.

    Article  Google Scholar 

  16. Fan, Y., Lei, S., Yuli, Y., Ye, L., and Timothy, G. (2021). Improved coverage and connectivity via weighted node deployment in solar insecticidal lamp internet of things. IEEE Internet of Things Journal.

  17. Johny Elma, K., & Meenakshi, S. (2021). Optimal coverage along with connectivity maintenance in heterogeneous wireless sensor network. Journal of Ambient Intelligence and Humanized Computing, 12(3), 3647–3658.

    Article  Google Scholar 

  18. Sheikh-Hosseini, M., & Hashemi, Seyed Rouhollah Samareh. (2022). Connectivity and coverage constrained wireless sensor nodes deployment using steepest descent and genetic algorithms. Expert Systems with Applications, 190, 116164.

    Article  Google Scholar 

  19. Liu, Xuxun. (2017). Node deployment based on extra path creation for wireless sensor networks on mountain roads. IEEE Communications Letters, 21(11), 2376–2379.

    Article  Google Scholar 

  20. Al-Karaki, J. N., & Gawanmeh, A. (2017). The optimal deployment, coverage, and connectivity problems in wireless sensor networks: Revisited. IEEE Access, 5, 18051–18065.

    Article  Google Scholar 

  21. Cardei, I., & Cardei, M. (2008). Energy-efficient connected-coverage in wireless sensor networks. International Journal of Sensor Networks, 3(3), 201–210.

    Article  MATH  Google Scholar 

  22. Harizan, S., & Kuila, P. (2019). Coverage and connectivity aware energy efficient scheduling in target based wireless sensor networks: An improved genetic algorithm based approach. Wireless Networks, 25(4), 1995–2011.

    Article  Google Scholar 

  23. Chen, C.-P., Mukhopadhyay, S. C., Chuang, C.-L., Liu, M.-Y., & Jiang, J.-A. (2014). Efficient coverage and connectivity preservation with load balance for wireless sensor networks. IEEE Sensors Journal, 15(1), 48–62.

    Article  Google Scholar 

  24. Jehan, C., & Shalini, D. (2017). Potential position node placement approach via oppositional gravitational search for fulfill coverage and connectivity in target based wireless sensor networks. Wireless Networks, 23(6), 1875–1888.

    Article  Google Scholar 

  25. Gupta, G. P., & Jha, Sonu. (2019). Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks. Wireless Networks, 25(6), 3167–3177.

    Article  Google Scholar 

  26. Zorbas, D., & Douligeris, C. (2011). Connected coverage in WSNS based on critical targets. Computer Networks, 55(6), 1412–1425.

    Article  Google Scholar 

  27. Sun, Z., Zhang, Y., Nie, Y., Wei, W., Lloret, J., & Song, H. (2017). CASMOC: A novel complex alliance strategy with multi-objective optimization of coverage in wireless sensor networks. Wireless Networks, 23(4), 1201–1222.

    Article  Google Scholar 

  28. Binh, H. T. T., Hanh, N. T., Dey, N., et al. (2018). Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Computing and Applications, 30(7), 2305–2317.

    Article  Google Scholar 

  29. Hanh, N. T., Binh, H. T. T., Hoai, N. X., & Palaniswami, M. S. (2019). An efficient genetic algorithm for maximizing area coverage in wireless sensor networks. Information Sciences, 488, 58–75.

    Article  MathSciNet  MATH  Google Scholar 

  30. Amgoth, T., & Jana, Prasanta K. (2015). Energy-aware routing algorithm for wireless sensor networks. Computers & Electrical Engineering, 41, 357–367.

    Article  Google Scholar 

  31. Ez-Zaidi, A., & Rakrak, S. (2016). A comparative study of target tracking approaches in wireless sensor networks. Journal of Sensors, 2016, 3270659. https://doi.org/10.1155/2016/3270659.

  32. Paul, B., Amites, S., and Béla, B. (2008). Percolation, connectivity, coverage and colouring of random geometric graphs. In Handbook of large-scale random networks, (pp. 117–142).

  33. Sah, D. K., Cengiz, K., Donta, P. K., Inukollu, V. N., & Amgoth, T. (2021). EDGFL Empirical dataset generation framework for wireless sensor networks. Computer Communications, 180, 48–56.

    Article  Google Scholar 

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Correspondence to Tarachand Amgoth.

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Banoth, S.P.R., Donta, P.K. & Amgoth, T. Target-aware distributed coverage and connectivity algorithm for wireless sensor networks. Wireless Netw 29, 1815–1830 (2023). https://doi.org/10.1007/s11276-022-03224-1

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