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SEALEA: Scalable and Energy Aware k-Leaders Election Algorithm in IoT Wireless Sensor Networks

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Abstract

Wireless Sensor Network applications profit from or necessitate the use of leaders, elected on the basis of some quantifiable and comparable criteria. Hence, a panoply of leader election algorithms have been proposed in the literature. Even though most of the algorithms focus on lowering the control message (messages needed to elect a leader) count, there has been almost no focus on ensuring high availability of a leader despite various types of failures like battery exhaustion and sensor crash, especially, in the scenarios of rescue and warfare, where the absence of the leader, even for a short duration, may lead to havoc. To overcome the problem of electing a unique leader, in this paper, we propose an efficient protocol for electing k-leaders in a wireless sensor network. The proposed protocol, called SEALEA for A Scalable Leader Election protocol, is distributed and, by means of the exchange of messages among neighbors, terminates after informing the elected nodes. The correctness of the protocol is proven through simulation. SEALEA is implemented on the OMNET++ simulator. Our experimental evaluations demonstrate the effectiveness of SEALEA in determining network leaders swiftly and efficiently. The performance of SEALEA is compared to that of other previously proposed k-leaders election protocols WiLE [1] and K-Top Leader [2]. Results show that SEALEA determines the leader faster and consuming less energy than previous solutions. On average, SEALEA is shown to send 0.845% of the messages sent by WiLE, transmitting 0.87% of the bytes transmitted by that protocol. Against K-TOP, SEALEA sends 19.25% of the messages and transmits only 19.28% of the bytes transmitted by K-TOP.

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References

  1. Sheshashayee, A. V., & Basagni, S. (2019). Wile: Leader election in wireless networks. Ad Hoc and Sensor Wireless Networks, 44(1–2), 59–81.

    Google Scholar 

  2. Raychoudhury, V., Cao, J., & Wu, W. (2008). Top k-leader election in wireless ad hoc networks. In: 2008 Proceedings of 17th international conference on computer communications and networks (pp. 1–6). IEEE.

  3. Ghosh, S. (2014). Distributed systems: An algorithmic approach. Boca Raton: CRC Press.

    Book  Google Scholar 

  4. Attiya, H., & Welch, J. (2004). Distributed computing: Fundamentals, simulations, and advanced topics (Vol. 19). Hoboken: Wiley.

    Book  Google Scholar 

  5. Garg, V. K., Garg, V. K., Garg, V. K., & Garg, V. K. (2004). Concurrent and distributed computing in Java. Hoboken: Wiley.

    Book  Google Scholar 

  6. Al Nahas, B., Duquennoy, S., & Landsiedel, O. (2017). Network-wide consensus utilizing the capture effect in low-power wireless networks. In: Proceedings of the 15th ACM conference on embedded network sensor systems (pp. 1–14).

  7. Abdaoui, A., & El-Fouly, T. M. (2014). Tossim and distributed binary consensus algorithm in wireless sensor networks. Journal of Network and Computer Applications, 41, 451–458.

    Article  Google Scholar 

  8. Liao, H.-J., Richard Lin, C.-H., Lin, Y.-C., & Tung, K.-Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1), 16–24.

    Article  Google Scholar 

  9. Shamshirband, S., Anuar, N. B., Kiah, M. L. M., Rohani, V. A., Petković, D., Misra, S., & Khan, A. N. (2014). Co-fais: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks. Journal of Network and Computer Applications, 42, 102–117.

    Article  Google Scholar 

  10. Roman, G.-C., Huang, Q., & Hazemi, A. (2001). Consistent group membership in ad hoc networks. In: Proceedings of the 23rd international conference on software engineering, ser. ICSE ’01 (pp. 381–388). USA: IEEE Computer Society.

  11. Sucasas, V., Marques, H., Rodriguez, J., & Tafazolli, R. (2013). Clustering techniques for energy efficient wireless communication. In: Green communication in 4G wireless systems (p. 89).

  12. Lehane, B., Dolye, L., & O’Mahony, D. (2005). Ad hoc key management infrastructure. In: International conference on information technology: Coding and computing (ITCC’05)-Volume II, vol. 2 (pp. 540–545). IEEE.

  13. Chlamtac, I., & Faragó, A. (1999). A new approach to the design and analysis of peer-to-peer mobile networks. Wireless Networks, 5(3), 149–156.

    Article  Google Scholar 

  14. Shabnam, F., & Jamalipour, A. (2020) An efficient coordinator selection method for geo-routing protocol in vehicular network. In: IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (pp. 1–5). IEEE.

  15. Maurya, A. K., Kumar, A., Kumar, N., et al. (2020). Improved chain based cooperative routing protocol in WSN. Journal of Physics: Conference Series, 1478(1), 012017.

    Google Scholar 

  16. Bhardwaj, R., & Kumar, D. (2019). Mofpl: Multi-objective fractional particle lion algorithm for the energy aware routing in the WSN. Pervasive and Mobile Computing, 58, 101029.

    Article  Google Scholar 

  17. Arora, V., Sharma, V., & Sachdeva, M. (2019). ACO optimized self-organized tree-based energy balance algorithm for wireless sensor network: Aosteb. Journal of Ambient Intelligence and Humanized Computing, 10, 01.

    Article  Google Scholar 

  18. Linping, W., Wu, B., Zhen, C., & Zufeng, W. (2010). Improved algorithm of pegasis protocol introducing double cluster heads in wireless sensor network. In: 2010 International conference on computer, mechatronics, control and electronic engineering vol. 1 (pp. 148–151).

  19. Jesudurai, S. A., & Senthilkumar, A. (2019). An improved energy efficient cluster head selection protocol using the double cluster heads and data fusion methods for IoT applications. Cognitive Systems Research, 57, 101–106.

    Article  Google Scholar 

  20. Fu, J.-S., & Liu, Y. (2015). Double cluster heads model for secure and accurate data fusion in wireless sensor networks. Sensors, 15(1), 2021–2040.

    Article  Google Scholar 

  21. Biswas, A., Maurya, A. K., Tripathi, A. K., & Aknine, S. (2021). Frlle: a failure rate and load-based leader election algorithm for a bidirectional ring in distributed systems. The Journal of Supercomputing, 77(1), 751–779.

    Article  Google Scholar 

  22. Biswas, A., Tripathi, A. K., & Aknine, S. (2021). Lea-TN: leader election algorithm considering node and link failures in a torus network. The Journal of Supercomputing 1–38.

  23. Santoro, N. (2006). Design and analysis of distributed algorithms (Wiley series on parallel and distributed computing). Hoboken: Wiley.

    Book  Google Scholar 

  24. Bounceur, A., Bezoui, M., Lagadec, L., Euler, R., Abdelkader, L., & Hammoudeh, M. (2019). Dotro: A new dominating tree routing algorithm for efficient and fault-tolerant leader election in WSNs and IoT networks. In É. Renault, S. Boumerdassi, & S. Bouzefrane (Eds.), Mobile, secure, and programmable networking (pp. 42–53). Cham: Springer International Publishing.

    Chapter  Google Scholar 

  25. Bounceur, A., Bezoui, M., Euler, R., Kadjouh, N., Lalem, F. (2017). Brogo: A new low energy consumption algorithm for leader election in WSNs. In: 2017 10th International conference on developments in eSystems engineering (DeSE) (pp. 218–223).

  26. Bounceur, A., Bezoui, M., Euler, R., & Lalem, F., (2017). A wait-before-starting algorithm for fast, fault-tolerant and low energy leader election in WSNs dedicated to smart-cities and IoT. In: IEEE Sensors (pp. 1–3). IEEE

  27. Bounceur, A., Bezoui, M., Noreen, U., Euler, R., Lalem, F., Hammoudeh, M., & Jabbar, S. (2018). Logo: A new distributed leader election algorithm in WSNs with low energy consumption. In Z. Patel & S. Gupta (Eds.), Future internet technologies and trends (pp. 1–16). Cham: Springer International Publishing.

    Google Scholar 

  28. Kadjouh, N., Bounceur, A., Bezoui, M., Khanouche, M. E., Euler, R., Hammoudeh, M., Lagadec, L., Jabbar, S., & Al-Turjman, F. (2020). A dominating tree based leader election algorithm for smart cities IoT infrastructure. Mobile Networks and Applications, August 2020.

  29. Biswas, T., Bhardwaj, R., Ray, A. K., & Kuila, P. (2018). A novel leader election algorithm based on resources for ring networks. International Journal of Communication Systems, 31(10), e3583.

    Article  Google Scholar 

  30. BeaulahSoundarabai, P., Thriveni, J., Venugopal, K., & Patnaik, L. (2013). An improved leader election algorithm for distributed systems. International Journal of Next-Generation Networks, 5(1), 21.

    Article  Google Scholar 

  31. Hirschberg, D. S., & Sinclair, J. B. (1980). Decentralized extrema-finding in circular configurations of processors. Communication of the ACM, 23(11), 627–628. https://doi.org/10.1145/359024.359029.

    Article  MathSciNet  MATH  Google Scholar 

  32. Derhab, A., & Badache, N. (2008). A self-stabilizing leader election algorithm in highly dynamic ad hoc mobile networks. IEEE Transactions on Parallel and Distributed Systems, 19(7), 926–939.

    Article  Google Scholar 

  33. Vasudevan, S., Kurose, J., Towsley, D. (2004). Design and analysis of a leader election algorithm for mobile ad hoc networks. In: Proceedings of the 12th IEEE international conference on network protocols, ICNP 2004 (pp. 350–360).

  34. Sharma, S., & Singh, A. K. (2018). An election algorithm to ensure the high availability of leader in large mobile ad hoc networks. International Journal of Parallel, Emergent and Distributed Systems, 33(2), 172–196.

    Article  Google Scholar 

  35. Conard, M., & Ebnenasir, A. (2021). A practical self-stabilizing leader election for networks of resource-constrained IoT devices.

  36. Raychoudhury, V., Cao, J., Niyogi, R., Wu, W., & Lai, Y. (2014). Top k-leader election in mobile ad hoc networks. Pervasive and Mobile Computing, 13, 181–202.

    Article  Google Scholar 

  37. Förster, A., Udugama, A., Virdis, A., Nardini, G. (eds) (2018). Proceedings of the 5th International OMNeT++ Community Summit, ser. In: EPiC series in computing, vol. 56. EasyChair

  38. Younis, O., & Fahmy, S. (2004). Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

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  1. College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

    Mohamed Amine Haddar

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Haddar, M.A. SEALEA: Scalable and Energy Aware k-Leaders Election Algorithm in IoT Wireless Sensor Networks. Wireless Pers Commun 125, 209–229 (2022). https://doi.org/10.1007/s11277-022-09547-8

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