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HSORS: A Highly Secure and Optimal Route Selection Protocol to Mitigate Against Packet Drop Attack in Wireless Ad-Hoc Sensor Network

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Abstract

Wireless sensor networks are widely utilized in military areas, constructing smart cities and disaster management to monitor environmental conditions like temperature, pressure, etc. They transmit the aggregated data and deliver them to the base station via intermediate nodes with less cost. But, the major challenges in this networks are security and energy efficiency. In case of vulnerable attacks to the network, it may cause loss of data packets. Moreover, if the routes are not chosen properly then the lifetime and efficiency of the network may get affected. To address these issues, a Highly Secure and Optimal Route Selection (HSORS) protocol is proposed in this paper. The HSORS protocol comprises four stages: (i) cluster formation, (ii) encryption, (iii) malicious node detection, and (iv) optimal route selection. At the first stage, the cluster is formed and the data is aggregated from the respective cluster. In the second phase, the aggregated data is encrypted for data confidentiality. At the third stage, the malicious nodes are blocked on the basis of trust mechanism and at the final stage, the most optimal route is selected by the Shark Smell Optimization algorithm. The proposed HSORS protocol is tested in the MATLAB platform then the results are compared with the existing schemes. Based on the experiments carried out, it is confirmed that the proposed HSORS protocol has superior results as compared to the existing schemes in terms of security, energy consumption (0.45 J), Packet Delivery Ratio (65%), detection ratio (85%), throughput (98%), and packet drop ratio (5%).

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References

  1. Rani, A., & Kumar, S. (2017, February). A survey of security in wireless sensor networks. In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT) (pp. 1–5). IEEE.

  2. Sarkar, A., & Murugan, T. S. (2019). Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wireless Networks, 25(1), 303–320.

    Article  Google Scholar 

  3. Nooriman, W. M., Abdullah, A. H., Rahim, N. A., & Kamarudin, K. (2018, April). Development of wireless sensor network for Harumanis Mango orchard's temperature, humidity and soil moisture monitoring. In 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 263–268). IEEE.

  4. Zygowski, C., & Jaekel, A. (2020). Optimal path planning strategies for monitoring coverage holes in wireless sensor networks. Ad Hoc Networks, 96, 101990.

    Article  Google Scholar 

  5. Chawla, P., & Sachdeva, M. (2018). Detection of selective forwarding (Gray Hole) attack on LEACH in wireless sensor networks. In D. K. Lobiyal, V. Mansotra, & U. Singh (Eds.), Next-Generation Networks (pp. 389–398). Springer.

  6. Qiu, T., Chen, N., Li, K., Qiao, D., & Fu, Z. (2017). Heterogeneous ad hoc networks: Architectures, advances and challenges. Ad Hoc Networks, 55, 143–152.

    Article  Google Scholar 

  7. Tomar, R., & Awasthi, Y. (2019, April). Prevention techniques employed in wireless ad-hoc networks. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 192–197). IEEE.

  8. Singh, A., Singh, G., & Singh, M. (2018). Comparative study of OLSR, DSDV, AODV, DSR and ZRP routing protocols under blackhole attack in mobile ad hoc network. In R. Singh, S. Choudhury, & A. Gehlot (Eds.), Intelligent Communication, Control and Devices (pp. 443–453). Springer.

  9. Ren, J., Zhang, Y., Zhang, K., & Shen, X. (2016). Adaptive and channel-aware detection of selective forwarding attacks in wireless sensor networks. IEEE Transactions on Wireless Communications, 15(5), 3718–3731.

    Article  Google Scholar 

  10. Sreelakshmi, T. R., & Binu, G. S. (2018, December). Energy efficient detection-removal algorithm for selective forwarding attack in wireless sensor networks. In 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) (pp. 1–6). IEEE.

  11. Praveena, A. (2017, March). Achieving data security in wireless sensor networks using ultra encryption standard version—IV algorithm. In 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT) (pp. 1–5). IEEE.

  12. Kavidha, V., & Ananthakumaran, S. (2019). Novel energy-efficient secure routing protocol for wireless sensor networks with Mobile sink. Peer-to-Peer Networking and Applications, 12(4), 881–892.

    Article  Google Scholar 

  13. Nivedetha, B., & Vennila, I. (2020). FFBKS: Fuzzy fingerprint biometric key based security schema for wireless sensor networks. Computer Communications, 150, 94–102.

    Article  Google Scholar 

  14. Thippeswamy, B. M., Reshma, S., Tejaswi, V., Shaila, K., Venugopal, K. R., & Patnaik, L. M. (2015). STEAR: Secure trust-aware energy-efficient adaptive routing in wireless sensor networks. Journal of Advances in Computer Networks, 3(2), 146–149.

    Article  Google Scholar 

  15. Alzubi, J. A., Almomani, O., Alzubi, O. A., & Al-shugran, M. (2016). Intelligent and dynamic neighbourhood entry lifetime for position-based routing protocol using fuzzy logic controller. International Journal of Computer Science and Information Security, 14(1), 118.

    Google Scholar 

  16. Thangaramya, K., Kulothungan, K., Gandhi, S. I., Selvi, M., Kumar, S. S., & Arputharaj, K. (2020). Intelligent fuzzy rule-based approach with outlier detection for secured routing in WSN. Soft Computing, 24(21), 16483–16497.

    Article  Google Scholar 

  17. Rajeswari, A. R., Kulothungan, K., Ganapathy, S., & Kannan, A. (2019). A trusted fuzzy based stable and secure routing algorithm for effective communication in mobile adhoc networks. Peer-to-Peer Networking and Applications, 12(5), 1076–1096.

    Article  Google Scholar 

  18. Almomani, O., Al-Shugran, M., Alzubi, J. A., & Alzubi, O. A. (2015). Performance evaluation of position-based routing protocols using different mobility models in manet. International Journal of Computer Applications, 119(3), 43–48.

    Article  Google Scholar 

  19. Deepa, C., & Latha, B. (2019). HHSRP: A cluster based hybrid hierarchical secure routing protocol for wireless sensor networks. Cluster Computing, 22(5), 10449–10465.

    Article  Google Scholar 

  20. Kalidoss, T., Rajasekaran, L., Kanagasabai, K., Sannasi, G., & Kannan, A. (2020). QoS aware trust based routing algorithm for wireless sensor networks. Wireless Personal Communications, 110(4), 1637–1658.

    Article  Google Scholar 

  21. Almazyad, A. S. (2018). Reputation-based mechanisms to avoid misbehaving nodes in ad hoc and wireless sensor networks. Neural Computing and Applications, 29(9), 597–607.

    Article  Google Scholar 

  22. AlFarraj, O., AlZubi, A., & Tolba, A. (2018). Trust-based neighbor selection using activation function for secure routing in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-018-0885-1

    Article  Google Scholar 

  23. Sun, Z., Wei, M., Zhang, Z., & Qu, G. (2019). Secure routing protocol based on multi-objective ant-colony-optimization for wireless sensor networks. Applied Soft Computing, 77, 366–375.

    Article  Google Scholar 

  24. Liu, Y., Dong, M., Ota, K., & Liu, A. (2016). ActiveTrust: Secure and trustable routing in wireless sensor networks. IEEE Transactions on Information Forensics and Security, 11(9), 2013–2027.

    Article  Google Scholar 

  25. Selvi, M., Thangaramya, K., Ganapathy, S., Kulothungan, K., Nehemiah, H. K., & Kannan, A. (2019). An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wireless Personal Communications, 105(4), 1475–1490.

    Article  Google Scholar 

  26. Gomathy, V., Padhy, N., Samanta, D., Sivaram, M., Jain, V., & Amiri, I. S. (2020). Malicious node detection using heterogeneous cluster based secure routing protocol (HCBS) in wireless adhoc sensor networks. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-01797-3

    Article  Google Scholar 

  27. Babu, M. V., Alzubi, J. A., Sekaran, R., Patan, R., Ramachandran, M., & Gupta, D. (2020). An improved IDAF-FIT clustering based ASLPP-RR routing with secure data aggregation in wireless sensor network. Mobile Networks and Applications, 118(3), 1–9.

  28. Alzubi, J. A. (2020). Bipolar fully recurrent deep structured neural learning based attack detection for securing industrial sensor networks. Transactions on Emerging Telecommunications Technologies, 32(7), e4069.

    Google Scholar 

  29. Selvi, M., Kumar, S. S., Ganapathy, S., Ayyanar, A., Nehemiah, H. K., & Kannan, A. (2021). An energy efficient clustered gravitational and fuzzy based routing algorithm in WSNs. Wireless Personal Communications, 116(1), 61-90.

  30. Kumar, S. S., Selvi, M., Kannan, A., & Ganapathy, S. (2021). An energy-efficient and secured hop-by-hop authentication protocol for wireless sensor networks. In Proceedings of international conference on communication and computational technologies (pp. 221–228). Singapore: Springer.

  31. Selvi, M., Velvizhy, P., Ganapathy, S., Nehemiah, H. K., & Kannan, A. (2019). A rule based delay constrained energy efficient routing technique for wireless sensor networks. Cluster Computing, 22(5), 10839–10848.

  32. Kumar, S. S., & Palanichamy, Y. (2018). Energy efficient and secured distributed data dissemination using hop by hop authentication in WSN. Wireless Networks, 24(4), 1343–1360.

  33. Abedinia, O., Amjady, N., & Ghasemi, A. (2016). A new metaheuristic algorithm based on shark smell optimization. Complexity, 21(5), 97–116.

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  1. Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering Karungal, Kanyakumari District, 629157, India

    Femila L

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  1. Femila L
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All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.

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Correspondence to Femila L.

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L, F. HSORS: A Highly Secure and Optimal Route Selection Protocol to Mitigate Against Packet Drop Attack in Wireless Ad-Hoc Sensor Network. Wireless Pers Commun 121, 3403–3424 (2021). https://doi.org/10.1007/s11277-021-08884-4

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