Abstract
In the monitoring area, nodes collaboratively collect various types of critical information about the target object, including temperature, humidity, and stress, and relay this data to users. This study evaluates a scenario with 100 summary points and a communication radius of 20 meters, progressively increasing the number of attack nodes from 0 to 4. Despite the effectiveness of existing security positioning algorithms in mitigating attacks, their substantial network resource consumption remains a challenge. This paper introduces a novel security positioning method designed to tolerate three distinct types of attacks. This approach identifies attack nodes by examining the physical attributes of each node. Analysis indicates that the average positioning error for the witch attack algorithm increases sharply as the number of virtual nodes rises, reaching approximately 80% when four attack nodes are present. Over 1,000 rounds of network observation, node survival rates were documented, starting with an initial pool of 100 nodes. Comparative results reveal that the key-out algorithm begins to experience node failures around the 600th round, with complete node depletion by the 700th round. The MPRP-RSSI algorithm, on the other hand, starts showing failures around the 900th round. By contrast, the proposed algorithm exhibits enhanced robustness, maintaining node stability throughout the entire monitoring period. The enhanced RSSI-based positioning algorithm, which is resilient against replication attacks, applies constraints on communication ranges and enforces unique messaging protocols to regulate node interactions, effectively reducing replication threats.
References
K. B. Abu Bakar, F. T. Zuhra, B. Isyaku, and S. B. Sulaiman, “A Review on the Immediate Advancement of the Internet of Things in Wireless Telecommunications,” Ieee Access, vol. 11, pp. 21020–21048, 2023.
D. Adesina, C. C. Hsieh, Y. E. Sagduyu, and L. J. Qian, “Adversarial Machine Learning in Wireless Communications Using RF Data: A Review,” Ieee Communications Surveys and Tutorials, vol. 25, no. 1, pp. 77–100, 2023.
I. Ahmad et al., “Analysis of Security Attacks and Taxonomy in Underwater Wireless Sensor Networks,” Wireless Communications & Mobile Computing, vol. 2021, pp. 15, 2021.
J. Amutha, S. Sharma, and S. K. Sharma, “Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions,” Computer Science Review, vol. 40, pp. 43, 2021.
A. Attkan and V. Ranga, “Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security,” Complex & Intelligent Systems, vol. 8, no. 4, pp. 3559–3591, 2022.
B. A. Begum and S. V. Nandury, “A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT,” Wireless Communications & Mobile Computing, vol. 2022, pp. 28, 2022.
D. Chawla and P. S. Mehra, “A roadmap from classical cryptography to post-quantum resistant cryptography for 5G-enabled IoT: Challenges, opportunities and solutions,” Internet of Things, vol. 24, pp. 37, 2023.
C. M. Chen, Z. Li, S. A. Chaudhry, and L. Li, “Attacks and Solutions for a Two-Factor Authentication Protocol for Wireless Body Area Networks,” Security and Communication Networks, vol. 2021, pp. 12, 2021.
G. Czeczot, I. Rojek, and D. Mikolajewski, “Analysis of Cyber Security Aspects of Data Transmission in Large-Scale Networks Based on the LoRaWAN Protocol Intended for Monitoring Critical Infrastructure Sensors,” Electronics, vol. 12, no. 11, pp. 14, 2023.
W. D. Fang, W. X. Zhang, W. Chen, T. Pan, Y. P. Ni, and Y. X. Yang, “Trust-Based Attack and Defense in Wireless Sensor Networks: A Survey,” Wireless Communications & Mobile Computing, vol. 2020, pp. 20, 2020.
Y. Y. Ghadi et al., “Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review,” Ieee Access, vol. 12, pp. 12699–12719, 2024.
M. Hanif et al., “AI-Based Wormhole Attack Detection Techniques in Wireless Sensor Networks,” Electronics, vol. 11, no. 15, pp. 28, 2022.
M. Z. Hasan and Z. M. Hanapi, “Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review,” Electronics, vol. 12, no. 2, pp. 23, 2023.
M. Z. Hasan, Z. M. Hanapi, and M. Z. Hussain, “Wireless Sensor Security Issues on Data Link Layer: A Survey,” Cmc-Computers Materials & Continua, vol. 75, no. 2, pp. 4065–4084, 2023.
N. H. Hussein, C. T. Yaw, S. P. Koh, S. K. Tiong, and K. H. Chong, “A Comprehensive Survey on Vehicular Networking: Communications, Applications, Challenges, and Upcoming Research Directions,” Ieee Access, vol. 10, pp. 86127–86180, 2022.
H. Liazid and M. Lehsaini, “A brief review on integration between wireless sensor networks and Cloud,” Concurrency and Computation-Practice & Experience, vol. 33, no. 20, pp. 10, 2021.
J. X. Liu, M. Nogueira, J. Fernandes, and B. Kantarci, “Adversarial Machine Learning: A Multilayer Review of the State-of-the-Art and Challenges for Wireless and Mobile Systems,” Ieee Communications Surveys and Tutorials, vol. 24, no. 1, pp. 123–159, 2022.
X. W. Liu, J. G. Yu, F. Li, W. F. Lv, Y. L. Wang, and X. Z. Cheng, “Data Aggregation in Wireless Sensor Networks: From the Perspective of Security,” Ieee Internet of Things Journal, vol. 7, no. 7, pp. 6495–6513, 2020.
R. Lohiya and A. Thakkar, “Application Domains, Evaluation Data Sets, and Research Challenges of IoT: A Systematic Review,” Ieee Internet of Things Journal, vol. 8, no. 11, pp. 8774–8798, 2021.
B. Narwal and A. K. Mohapatra, “A survey on security and authentication in wireless body area networks,” Journal of Systems Architecture, vol. 113, pp. 45, 2021.
A. Narwaria and A. P. Mazumdar, “Software-Defined Wireless Sensor Network: A Comprehensive Survey,” Journal of Network and Computer Applications, vol. 215, pp. 26, 2023.
S. Nobahary, H. G. Garakani, and A. Khademzadeh, “Detecting Noncooperation Nodes Mechanisms in Wireless Networks: A Survey,” Security and Communication Networks, vol. 2022, pp. 20, 2022.
P. Park, P. Di Marco, J. Nah, and C. Fischione, “Wireless Avionics Intracommunications: A Survey of Benefits, Challenges, and Solutions,” Ieee Internet of Things Journal, vol. 8, no. 10, pp. 7745–7767, 2021.
D. M. G. Preethichandra, L. Piyathilaka, U. Izhar, R. Samarasinghe, and L. C. De Silva, “Wireless Body Area Networks and Their Applications-A Review,” Ieee Access, vol. 11, pp. 9202–9220, 2023.
M. Pundir and J. K. Sandhu, “A Systematic Review of Quality of Service in Wireless Sensor Networks using Machine Learning: Recent Trend and Future Vision,” Journal of Network and Computer Applications, vol. 188, pp. 33, 2021.
R. Ramya and T. Brindha, “A Comprehensive Review on Optimal Cluster Head Selection in WSN-IoT,” Advances in Engineering Software, vol. 171, pp. 16, 2022.
M. Revanesh, J. M. Acken, and V. Sridhar, “DAG block: Trust aware load balanced routing and lightweight authentication encryption in WSN,” Future Generation Computer Systems-the International Journal of Escience, vol. 140, pp. 402–421, 2023.
M. E. Rivero-Angeles, “Quantum-based wireless sensor networks: A review and open questions,” International Journal of Distributed Sensor Networks, vol. 17, no. 10, pp. 13, 2021.
M. Saqib and A. H. Moon, “A Systematic Security Assessment and Review of Internet of Things in the Context of Authentication,” Computers & Security, vol. 125, pp. 27, 2023.
A. Thakkar and R. Lohiya, “A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges,” Archives of Computational Methods in Engineering, vol. 28, no. 4, pp. 3211–3243, 2021.

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