Sreeja Balachandran Nair Premakumari, Sundaram Gopikrishnan, Rivera Marco, Wheeler Patrick
Department of Information Technology, Karpagam College of Engineering, Myleripalayam Village, Coimbatore 641032, Tamil Nadu, India.
School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India.
Sensors (Basel). 2024 Dec 10;24(24):7893. doi: 10.3390/s24247893.
The accuracy of node localization plays a crucial role in the performance and reliability of wireless sensor networks (WSNs), which are widely utilized in fields like security systems and environmental monitoring. The integrity of these networks is often threatened by the presence of malicious nodes that can disrupt the localization process, leading to erroneous positioning and degraded network functionality. To address this challenge, we propose the security-aware localization using bat-optimized malicious anchor prediction (BO-MAP) algorithm. This approach utilizes a refined bat optimization algorithm to improve both the precision of localization and the security of WSNs. By integrating advanced optimization with density-based clustering and probabilistic analysis, BO-MAP effectively identifies and isolates malicious nodes. Our comprehensive simulation results reveal that BO-MAP significantly surpasses six current state-of-the-art methods-namely, the Secure Localization Algorithm, Enhanced DV-Hop, Particle Swarm Optimization-Based Localization, Range-Free Localization, the Robust Localization Algorithm, and the Sequential Probability Ratio Test-across various performance metrics, including the true positive rate, false positive rate, localization accuracy, energy efficiency, and computational efficiency. Notably, BO-MAP achieves an impressive true positive rate of 95% and a false positive rate of 5%, with an area under the receiver operating characteristic curve of 0.98. Additionally, BO-MAP exhibits consistent reliability across different levels of attack severity and network conditions, highlighting its suitability for deployment in practical WSN environments.
节点定位的准确性在无线传感器网络（WSN）的性能和可靠性中起着至关重要的作用，无线传感器网络广泛应用于安全系统和环境监测等领域。这些网络的完整性常常受到恶意节点的威胁，恶意节点会干扰定位过程，导致错误的定位并降低网络功能。为应对这一挑战，我们提出了使用蝙蝠优化恶意锚点预测（BO-MAP）算法的安全感知定位方法。该方法利用改进的蝙蝠优化算法来提高定位精度和无线传感器网络的安全性。通过将先进的优化与基于密度的聚类和概率分析相结合，BO-MAP有效地识别并隔离恶意节点。我们全面的仿真结果表明，在包括真阳性率、假阳性率、定位精度、能量效率和计算效率等各种性能指标方面，BO-MAP显著优于六种当前的先进方法，即安全定位算法、增强型DV-Hop、基于粒子群优化的定位、无距离定位、鲁棒定位算法和序贯概率比检验。值得注意的是，BO-MAP实现了令人印象深刻的95%的真阳性率和5%的假阳性率，接收器操作特性曲线下的面积为0.98。此外，BO-MAP在不同攻击严重程度和网络条件下均表现出一致的可靠性，突出了其在实际无线传感器网络环境中部署的适用性。
