International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

Novel Two-Level Randomized Sector-based Routing to Maintain Source Location Privacy in WSN for IoT

  • Jainulabudeen, A. (PG & Research Department of Computer Science, Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University) ;
  • Surputheen, M. Mohamed (PG & Research Department of Computer Science, Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University)
  • Received : 2022.03.05
  • Published : 2022.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

WSN is the major component for information transfer in IoT environments. Source Location Privacy (SLP) has attracted attention in WSN environments. Effective SLP can avoid adversaries to backtrack and capture source nodes. This work presents a Two-Level Randomized Sector-based Routing (TLRSR) model to ensure SLP in wireless environments. Sector creation is the initial process, where the nodes in the network are grouped into defined sectors. The first level routing process identifies sector-based route to the destination node, which is performed by Ant Colony Optimization (ACO). The second level performs route extraction, which identifies the actual nodes for transmission. The route extraction is randomized and is performed using Simulated Annealing. This process is distributed between the nodes, hence ensures even charge depletion across the network. Randomized node selection process ensures SLP and also avoids depletion of certain specific nodes, resulting in increased network lifetime. Experiments and comparisons indicate faster route detection and optimal paths by the TLRSR model.

Keywords

References

  1. Jianbing Ni, Kuan Zhang, Xiaodong Lin, XueminShen, Securing fog computing for internet of things applications: challenges and solutions, IEEE Commun. Surv. Tutor. 20 (1) (2017) 601-628.
  2. Ke Zhang, SupengLeng, Yejun He, SabitaMaharjan, Yan Zhang, Mobile edge computing and networking for green and low-latency internet of things, IEEE Commun. Mag. 56 (5) (2018) 39-45. https://doi.org/10.1109/MCOM.2018.1700882
  3. Huang, W.W., Peng, Y.L., Wen, J., Yu, M., 2009. Energy-efficient multi-hop hierarchical routing protocol for wireless sensor networks. In: Proceedings of International Conference on Networks Security, Wireless Communications and Trusted Computing, pp. 469-472.
  4. A. Boukerche, A. Mostefaoui, M. Melkemi, Efficient and robust serial query processing approach for large-scale wireless sensor network applications, Ad Hoc Netw. 47 (2016) 82-98. https://doi.org/10.1016/j.adhoc.2016.04.012
  5. Chunsheng Zhu, T. Yang, Lei Shu, ShojiroNishio, Insights of top-k query in duty-cycled wireless sensor networks, IEEE Trans. Ind. Electron. 62 (2) (2015) 1317-1328. https://doi.org/10.1109/TIE.2014.2334653
  6. Mohan, R., Ananthula, V.R., 2019. Reputation-based secure routing protocol in mobile ad-hoc network using Jaya Cuckoo optimization. Int. J. Modeling, Simul., Sci. Comput. 10 (3).
  7. RoneIlidio da Silva, Daniel FernandesMacedo, Jose Marcos S. Nogueir, Duty Cycle Aware Spatial Query Processing in Wireless Sensor Networks, Vol. 41, 2015, pp. 240-255.
  8. MihaelaMiticia, MartijnOnderwater, Maurits de Graafa, Optimal query assignment for wireless sensor networks, Int. J. Electron. Commun. 69 (8) (2015) 1102-1112. https://doi.org/10.1016/j.aeue.2015.04.009
  9. Secure and energy aware multi-hop routing protocol in WSN using Taylor-based hybrid optimization algorithm
  10. Zhan, G., Shi, W., Deng, J., 2012. Design and implementation of TARF: a trust-aware routing framework for WSNs. IEEE Trans. Dependable Secure Comput. 9 (2), 184-197. https://doi.org/10.1109/TDSC.2011.58
  11. Zahariadis, T., Leligou, H., Karkazis, P., Trakadas, P., Papaefstathiou, I., Vangelatos, C., Besson, L., 2011. Design and implementation of a trust-aware routing protocol for Largewsns. Int. J. Network Security Appl. 2 (3).
  12. Cengiz, K., Dag, T., 2018. Energy aware multi-hop routing protocol for WSNs. IEEE Access 6, 2622-2633. https://doi.org/10.1109/access.2017.2784542
  13. Purkait, R., Tripathi, S., 2017. Energy aware fuzzy based multi-hop routing protocol using unequal clustering. Wireless Pers. Commun. 94 (3), 809-833. https://doi.org/10.1007/s11277-016-3652-7
  14. Selvi, M., Velvizhy, P., Ganapathy, S., Nehemiah, H.K., Kannan, A., 2017. A rule based delay constrained energy efficient routing technique for wireless sensor networks. Cluster Comput., 1-10
  15. MOFPL: Multi-objective fractional particle lion algorithm for the energy aware routing in the WSN
  16. R. Mohanasundaram, P.S. Periasamy, Clustering based optimal data storage strategy using hybrid swarm intelligence in WSN, Wirel. Pers. Commun. 85 (3) (2015) 1381-1397. https://doi.org/10.1007/s11277-015-2846-8
  17. R. Kumar, D. Kumar, Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network, Wirel. Netw. (2015) 1-14. https://doi.org/10.1007/s11276-017-1537-7
  18. M. Faheem, R.A. Butt, B. Raza, M.W. Ashraf, Seema Begum, Md.A. Ngadi, V.C. Gungor, Bio-inspired routing protocol for WSN-based smart grid applications in the context of industry 40, Trans. Emerg. Telecommun. Technol. (2018).
  19. Upendran, V., and R. Dhanapal. "Secure and Distributed On-Demand Randomized Routing in WSN." International Journal of Computers & Technology 15, no. 6 (2016): 6850-6856. https://doi.org/10.24297/ijct.v15i6.3976
  20. Solving the load balanced clustering and routing problems in WSNs with an fpt-approximation algorithm and a grid structure
  21. C.P. Low, C. Fang, J.M. Ng, Y.H. Ang, Efficient load-balanced clustering algorithms for wireless sensor networks, Comput. Commun. 31 (4) (2008) 750-759. https://doi.org/10.1016/j.comcom.2007.10.020
  22. P. Kuila, P.K. Jana, Approximation schemes for load balanced clustering in wireless sensor networks, J. Supercomput. 68 (1) (2014) 87-105. https://doi.org/10.1007/s11227-013-1024-6
  23. P. Kuila, S.K. Gupta, P.K. Jana, A novel evolutionary approach for load balanced clustering problem for wireless sensor networks, Swarm Evol. Comput. 12 (2013) 48-56. https://doi.org/10.1016/j.swevo.2013.04.002
  24. S.K. Gupta, P.K. Jana, Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach, Wirel. Pers. Commun.83 (3) (2015) 2403-2423. https://doi.org/10.1007/s11277-015-2535-7
  25. P. Kuila, P.K. Jana, Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach, Eng. Appl. Artif. Intell. 33 (2014) 127-140. https://doi.org/10.1016/j.engappai.2014.04.009
  26. M. Azharuddin, P.K. Jana, PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks, Soft Comput. 21 (22) (2017) 6825-6839. https://doi.org/10.1007/s00500-016-2234-7
  27. P.S. Mann, S. Singh, Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks, J. Netw. Comput. Appl. 83 (2017) 40-52. https://doi.org/10.1016/j.jnca.2017.01.031
  28. Stutzle, T. and Dorigo, M., 1999. ACO algorithms for the traveling salesman problem. Evolutionary algorithms in engineering and computer science, 4, pp.163-183.
  29. Prakasam, Anandkumar, and Nickolas Savarimuthu. "Metaheuristic algorithms and probabilistic behaviour: a comprehensive analysis of Ant Colony Optimization and its variants." Artificial Intelligence Review 45, no. 1 (2016): 97-130. https://doi.org/10.1007/s10462-015-9441-y
  30. Van Laarhoven, P.J. and Aarts, E.H., 1987. Simulated annealing. In Simulated annealing: Theory and applications (pp. 7-15). Springer, Dordrecht.