Russian Federation
Due to the development and improvement of wireless sensor networks and their active implementation in various areas, there is a need to increase the dynamics and variability of such networks. Today, there is a clear trend towards an increase in the level of self-organization and decentralization, due to which networks adapt to user needs and operating conditions. For this purpose, distributed control protocols are created and improved, networks with a cellular topology (mesh networks) are actively introduced. Nodes of such networks are able to move, create new data transmission paths based on the current state of communication channels and device resources, and perform various auxiliary and applied tasks. However, the progress of wireless sensor networks gives rise to additional information security risks associated with the use of self-organization and decentralization features by intruders. Attacks on such infrastructure include manipulation of sensor data, flood attacks that deplete equipment resources, failures in signal transmission routes, and other impacts that are characterized by increased variability and efficiency. This article comprises modeling and studying this type of threats, with special attention paid to simulation analysis methods taking into account the specificity properties of self-organization and decentralization of networks and attacks that exploit them. The proposed approach is built on role-based management in the simulation modeling of self-organizing wireless sensor networks. The results of experiments on a model of a network fragment for a group of connected unmanned aerial vehicles confirmed the validity and practicality of the proposed method.
wireless sensor network, modeling, security, attack
1. Li N., Liu X. Research on Self-Organization and Adaptive Strategy of the Internet of Things Sensor Networks // IEEE Access. 2024. Vol. 12. P. 66569–66579. DOI:https://doi.org/10.1109/ACCESS.2024.3399537.
2. Aldawsari H. A blockchain-based approach for secure energy-efficient IoT-based Wireless Sensor Networks for smart cities // Alexandria Engineering Journal. 2025. Vol. 126. P. 1–7. DOI:https://doi.org/10.1016/j.aej.2025.04.052.
3. Sultanow E., Chircu A. A Review of IoT Technologies, Standards, Tools, Frameworks and Platforms // The Internet of Things in the Industrial Sector: Security and Device Connectivity, Smart Environments, and Industry 4.0. 2019. P. 3–34. DOI:https://doi.org/10.1007/978-3-030-24892-5_1.
4. Forensic Analysis of the Xiaomi Mi Smart Sensor Set / J.M. Castelo Gómez [et al.] // Forensic Science International: Digital Investigation. 2022. Vol. 42–43. P. 301451. DOI:https://doi.org/10.1016/j.fsidi.2022.301451.
5. Abdalzaher M.S., Fouda M.M., Ibrahem M.I. Data Privacy Preservation and Security in Smart Metering Systems // Energies. 2022. Vol. 15. № 19. P. 7419. DOI:https://doi.org/10.3390/en15197419.
6. Sharma R. Enhancing Industrial Automation and Safety Through Real-Time Monitoring and Control Systems // International Journal on Smart & Sustainable Intelligent Computing. 2024. Vol. 1. № 2. P. 1–20. DOI:https://doi.org/10.63503/j.ijssic.2024.30.
7. Data Communication for Wireless Mobile Nodes in Intelligent Transportation Systems / K.N. Qureshi [et al.] // Microprocessors and Microsystems. 2022. Vol. 90. P. 104501. DOI:https://doi.org/10.1016/j.micpro.2022.104501.
8. Mills K. A Brief Survey of Self-Organization in Wireless Sensor Networks: Research Articles // Wireless Communications and Mobile Computing. 2007. Vol. 7. P. 823–834. DOI:https://doi.org/10.1002/wcm.499.
9. Khanna R. Evolutionary Approach to Efficient Provisioning and Self-organization in Wireless Sensor Networks (WSN). Oregon State University, 2016.
10. Improving Security in WMNs With Reputation Systems and Self-Organizing Maps / Z. Bankovic [et al.] // Journal of Network and Computer Applications. 2011. Vol. 34. Iss. 2. P. 455–463. DOI:https://doi.org/10.1016/j.jnca.2010.03.023.
11. Decentralized Intrusion Detection in Wireless Sensor Networks / A.P. Silva [et al.] // Proceedings. 2005. P. 16–23. DOI:https://doi.org/10.1145/1089761.1089765.
12. Chatzigiannakis I., Strikos A. A Decentralized Intrusion Detection System for Increasing Security of Wireless Sensor Networks // 2007 IEEE Conference on Emerging Technologies and Factory Automation (EFTA 2007), Patras, Greece. 2007. P. 1408–1411. DOI:https://doi.org/10.1109/EFTA.2007.4416949.
13. Blockchain-powered defense: Securing WSN against DDoS attacks with decentralized authentication / A. Suman [et al.] // Wireless Ad-hoc and Sensor Networks. CRC Press. 2024. P. 318–339.
14. Lee C., Suzuki J. SWAT: A Decentralized Self-Healing Mechanism for Wormhole Attacks in Wireless Sensor Networks // Handbook on Sensor Networks. World Scientific Publishing Co., 2010. P. 511–532. DOI:https://doi.org/10.1142/9789812837318_0021.
15. Energy Optimized Security Against Wormhole Attack in IoT-Based Wireless Sensor Networks / H. Shahid [et al.] // Computers, Materials and Continua. 2021. Vol. 68. Iss. 2. P. 1967–1981. DOI:https://doi.org/10.32604/cmc.2021.015259.
16. Wormhole Attack Mitigation Strategies and Their Impact on Wireless Sensor Network Performance: A Literature Survey / H. Shahid [et al.] // International Journal of Communication Systems. 2022. Vol. 35. DOI:https://doi.org/10.1002/dac.5311.
17. An Efficient Intrusion Detection Framework for Mitigating Blackhole and Sinkhole Attacks in Healthcare Wireless Sensor Networks / J.L. Webber [et al.] // Computers and Electrical Engineering. 2023. Vol. 111, Part B. P. 108964. DOI:https://doi.org/10.1016/j.compeleceng.2023.108964.
18. Detection of Hello Flood Attacks Using Fuzzy-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks / S. Radhika [et al.] // Electronics. 2023. Vol. 12. № 1. P. 123. DOI:https://doi.org/10.3390/electronics12010123.
19. Arunachalam R., Ruby Kanmani E.D. Detection and Mitigation of Vampire Attacks with Secure Routing in WSN Using Weighted RNN and Optimal Path Selection // Computers & Security. 2024. Vol. 145. P. 103991. DOI:https://doi.org/10.1016/j.cose.2024.103991.
20. Almesaeed R., Al-Salem E. Sybil Attack Detection Scheme Based on Channel Profile and Power Regulations in Wireless Sensor Networks // Wireless Networks. 2022. Vol. 28. P. 1361–1374. DOI:https://doi.org/10.1007/s11276-021-02871-0.
21. Nayyar A., Singh R. A Comprehensive Review of Simulation Tools for Wireless Sensor Networks (WSNs) // Journal of Wireless Networking and Communications. 2015. Vol. 5. № 1. P. 19–47. DOI:https://doi.org/10.5923/j.jwnc.20150501.03.
22. Investigating and Analyzing Simulation Tools of Wireless Sensor Networks: A Comprehensive Survey / G.H. Adday [et al.] // IEEE Access. 2024. Vol. 12. P. 22938–22977. DOI:https://doi.org/10.1109/ACCESS.2024.3362889.
23. A Secure Framework for Data Sharing in Private Blockchain-Based WBANs / L. Xiao [et al.] // IEEE Access. 2020. Vol. 8. P. 153956–153968. DOI:https://doi.org/10.1109/ACCESS.2020.3018119.
