Integrated Clustering-based Approach for Energy Efficient base Station Placement Strategy in Learning Wireless Sensor Intrusion Detection System
DOI:
https://doi.org/10.48314/ceti.v1i4.40Keywords:
Wireless sensor networks, Energy efficiency, Base station placement, Intrusion detection system, Machine learningAbstract
Wireless Sensor Networks (WSN) are becoming more popular with the advent of Internet of Things (IoT) applications in recent years. Enormous applications in Business, Government, Research and Personal applications use WSNs. Though WSNs are beneficiary, security issues prevailing in WSNs pose challenges in various aspects due to the limitation of Resources. Due to unmatured security features, Intrusion in WSNs is common. Several Intrusion Detection Systems (IDS) are in use for WSNs, but they need to be improved for robustness, reliability, trustworthiness and Energy Efficiency (EE). This paper proposes a technique for Energy Efficient Base Station Placement (BSP) for Learning based IDS using an Integrated and Clustering Approach.
References
Gulati, K., Boddu, R. S. K., Kapila, D., Bangare, S. L., Chandnani, N., & Saravanan, G. (2022). A review paper on wireless sensor network techniques in Internet of Things (IoT). Materials today: Proceedings, 51, 161–165. https://doi.org/10.1016/j.matpr.2021.05.067
Kim, B.-S., Kim, K.-I., Shah, B., Chow, F., & Kim, K. H. (2019). Wireless sensor networks for big data systems. Sensors, 19(7), 1565. https://doi.org/10.3390/s19071565
Ibrahim, D. S., Mahdi, A. F., & Yas, Q. M. (2021). Challenges and issues for wireless sensor networks: A survey. Journal global science research, 6(1), 1079–1097. https://www.academia.edu/download/101122372/jgsr15919933.pdf
Zhang, W., Han, D., Li, K.-C., & Massetto, F. I. (2020). Wireless sensor network intrusion detection system based on MK-ELM. Soft computing, 24(16), 12361–12374. https://doi.org/10.1007/s00500-020-04678-1
Godala, S., & Vaddella, R. P. V. (2020). A study on intrusion detection system in wireless sensor networks. International journal of communication networks and information security, 12(1), 127–141. https://B2n.ir/z61366
Sivagaminathan, V., Sharma, M., & Henge, S. K. (2023). Intrusion detection systems for wireless sensor networks using computational intelligence techniques. Cybersecurity, 6(1), 27. https://doi.org/10.1186/s42400-023-00161-0
Khan, K., Mehmood, A., Khan, S., Khan, M. A., Iqbal, Z., & Mashwani, W. K. (2020). A survey on intrusion detection and prevention in wireless ad-hoc networks. Journal of systems architecture, 105, 101701. https://doi.org/10.1016/j.sysarc.2019.101701
Saba, T., Haseeb, K., Ud Din, I., Almogren, A., Altameem, A., & Fati, S. M. (2020). EGCIR: Energy-aware graph clustering and intelligent routing using supervised system in wireless sensor networks. Energies, 13(16), 4072. https://doi.org/10.3390/en13164072
Aruchamy, P., Gnanaselvi, S., Sowndarya, D., & Naveenkumar, P. (2023). An artificial intelligence approach for energy-aware intrusion detection and secure routing in internet of things-enabled wireless sensor networks. Concurrency and computation: practice and experience, 35(23), e7818. https://doi.org/10.1002/cpe.7818
Sharma, R., Vashisht, V., & Singh, U. (2020). WOATCA: A secure and energy aware scheme based on whale optimisation in clustered wireless sensor networks. IET communications, 14(8), 1199–1208. https://doi.org/10.1049/iet-com.2019.0359
Han, L., Zhou, M., Jia, W., Dalil, Z., & Xu, X. (2019). Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model. Information sciences, 476, 491–504. https://doi.org/10.1016/j.ins.2018.06.017