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Detecting Faulty Nodes in Wireless Sensor Networks Using Harmony Search Algorithm

Abstract

Given the inherent limitations of sensor nodes in wireless sensor networks (WSNs) such as energy limitation and since sensor nodes are distributed in harsh environments in the majority of WSN applications, the probability of their failure is high. Hence, nodes’ failure for any reason is regarded as a challenge for these networks which has a negative impact on the efficiency of the entire network. Consequently, for achieving appropriate performance in important applications, faulty nodes should be detected and removed from network. Detection of faulty nodes in these networks is considered to be an NP-hard problem. Thus, meta-heuristic algorithms are used for solving this problem. Given the significance of this issue, a model based on harmony search algorithm (HSA) is proposed in this paper for detecting faulty nodes. In this model, for doing so, each memory vector in the HSA includes energy and correlation between neighbor nodes data. The results of simulation indicated that the proposed model is more efficient than other methods and is able to optimize detection precision rate, packet delivery rate and remaining energy.

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References

  1. 1.

    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38, 393–422.

  2. 2.

    Nikokheslat, H. D., & Ghaffari, A. (2017). Protocol for controlling congestion in wireless sensor networks. Wireless Personal Communications, 95, 3233–3251.

  3. 3.

    KeyKhosravi, D., Ghaffari, A., Hosseinalipour, A., & Khasragi, B. A. (2010). New clustering protocol to decrease probability failure nodes and increasing the lifetime in WSNs. International Journal of Advanced Computer Technology, 2, 117–121.

  4. 4.

    Ghaffari, A. (2014). Designing a wireless sensor network for ocean status notification system. Indian Journal of Science and Technology, 7, 809–814.

  5. 5.

    Ghaffari, A. (2015). Congestion control mechanisms in wireless sensor networks: A survey. Journal of Network and Computer Applications, 52, 101–115.

  6. 6.

    Azari, L., & Ghaffari, A. (2015). Proposing a novel method based on network-coding for optimizing error recovery in wireless sensor networks. Indian Journal of Science and Technology, 8, 859–867.

  7. 7.

    Ghaffari, A., & Takanloo, V. A. (2011). QoS-based routing protocol with load balancing for wireless multimedia sensor networks using genetic algorithm. World Applied Sciences Journal, 15, 1659–1666.

  8. 8.

    Ghaffari, A., & Rahmani, A. (2008) Fault tolerant model for data dissemination in wireless sensor networks. In International symposium on information technology, 2008. ITSim 2008 (pp. 1–8).

  9. 9.

    Mottaghinia, Z., & Ghaffari, A. (2016). A unicast tree-based data gathering protocol for delay tolerant mobile sensor networks. Information Systems and Telecommunication, 59, 1–12.

  10. 10.

    Panda, M., & Khilar, P. M. (2011). An efficient fault detection algorithm in wireless sensor network. In Contemporary computing (pp. 279–288).

  11. 11.

    Titouna, C., Aliouat, M., & Gueroui, M. (2016). FDS: Fault detection scheme for wireless sensor networks. Wireless Personal Communications, 86, 549–562.

  12. 12.

    Shao, S., Guo, S., & Qiu, X. (2017). Distributed fault detection based on credibility and cooperation for WSNs in smart grids. Sensors, 17, 983.

  13. 13.

    Yuvaraja, M., & Sabrigiriraj, M. (2017). Fault detection and recovery scheme for routing and lifetime enhancement in WSN. Wireless Networks, 23, 267–277.

  14. 14.

    Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76, 60–68.

  15. 15.

    Muhammed, T., & Shaikh, R. A. (2017). An analysis of fault detection strategies in wireless sensor networks. Journal of Network and Computer Applications, 78, 267–287.

  16. 16.

    Lee, M.-H., & Choi, Y.-H. (2008). Fault detection of wireless sensor networks. Computer Communications, 31, 3469–3475.

  17. 17.

    Zhao, X., Gao, Z., Huang, R., Wang, Z., & Wang, T. (2011). A fault detection algorithm based on cluster analysis in wireless sensor networks. In 2011 seventh international conference on mobile ad-hoc and sensor networks (MSN) (pp. 354–355).

  18. 18.

    Zhu, J., Yang, Y., Qiu, X., & Gao, Z. (2014). Sensor failure detection and recovery mechanism based on support vector and genetic algorithm. In 2014 16th Asia-Pacific network operations and management symposium (APNOMS) (pp. 1–4).

  19. 19.

    Lau, B. C., Ma, E. W., & Chow, T. W. (2014). Probabilistic fault detector for wireless sensor network. Expert Systems with Applications, 41, 3703–3711.

  20. 20.

    Yuan, H., Zhao, X., & Yu, L. (2015). A distributed Bayesian algorithm for data fault detection in wireless sensor networks. In 2015 international conference on information networking (ICOIN) (pp. 63–68).

  21. 21.

    Panda, M., & Khilar, P. M. (2015). Distributed Byzantine fault detection technique in wireless sensor networks based on hypothesis testing. Computers & Electrical Engineering, 48, 270–285.

  22. 22.

    Chanak, P., & Banerjee, I. (2016). Fuzzy rule-based faulty node classification and management scheme for large scale wireless sensor networks. Expert Systems with Applications, 45, 307–321.

  23. 23.

    Palanikumar, R., & Ramasamy, K. (2018). Effective failure nodes detection using matrix calculus algorithm in wireless sensor networks. Cluster Computing. https://doi.org/10.1007/s10586-017-1566-0.

  24. 24.

    Manjarres, D., Landa-Torres, I., Gil-Lopez, S., Del Ser, J., Bilbao, M. N., Salcedo-Sanz, S., et al. (2013). A survey on applications of the harmony search algorithm. Engineering Applications of Artificial Intelligence, 26, 1818–1831.

  25. 25.

    Kumar, P., & Singh, S. (2014). Reconfiguration of radial distribution system with static load models for loss minimization. In 2014 IEEE international conference on power electronics, drives and energy systems (PEDES) (pp. 1–5).

  26. 26.

    Das, S., Mukhopadhyay, A., Roy, A., Abraham, A., & Panigrahi, B. K. (2011). Exploratory power of the harmony search algorithm: Analysis and improvements for global numerical optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41, 89–106.

  27. 27.

    Lee, J.-H., Yoon, Y.-S., & Kim, J.-H. (2012). A new heuristic algorithm for mix design of high-performance concrete. KSCE Journal of Civil Engineering, 16, 974–979.

  28. 28.

    Sharma, K. P., & Sharma, T. P. (2017). rDFD: Reactive distributed fault detection in wireless sensor networks. Wireless Networks, 23, 1145–1160.

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Correspondence to Ali Ghaffari.

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Mosavvar, H., Ghaffari, A. Detecting Faulty Nodes in Wireless Sensor Networks Using Harmony Search Algorithm. Wireless Pers Commun 103, 2927–2945 (2018). https://doi.org/10.1007/s11277-018-5981-1

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Keywords

  • WSNs
  • Network lifetime
  • Faulty node
  • Harmony search algorithm
  • Detection precision rate