A wireless sensor network consists of a set of low-cost, small, and low-powered sensor nodes. Information about the position of a sensor node is essential for many applications such as topology control, clustering, geographical routing, object tracking, and environmental monitoring. This article introduces a novel robust range-free genetic-based algorithm (RRGA) for the task of localization that is resistant to anchor node compromise attacks. The genetic algorithm (GA) serves to find the best set of anchors that can be utilized in a localization process to achieve higher accuracy. The other ordinary sensor nodes estimate their own locations using this set of the selected anchors. The algorithm can perform well even in the presence of malicious anchors. The performance of the presented algorithm was assessed in terms of localization accuracy, storage space, border problem, and resiliency against anchor node compromise attacks. The assessment was conducted through simulation. According to the results, compared to other algorithms, the presented RRGA algorithm decreases the localization error for at least about 10% in normal conditions and at least about 50% in the case of malicious anchor node attacks. It also reduces the effect of the border problem for at least about 10% in normal conditions and at least about 60% in the case of malicious anchor node attacks. Besides, the required storage space is improved for at least about 50%. The results suggest that the RRGA performs better than other localization algorithms.

无线传感器网络由一组低成本，小型和低功耗的传感器节点组成。有关传感器节点位置的信息对于许多应用程序来说都是必不可少的，例如拓扑控制，群集，地理路由，对象跟踪和环境监视。本文介绍了一种新颖的鲁棒的无范围基于遗传的算法（RRGA），用于定位任务，可以抵抗锚节点入侵攻击。遗传算法（GA）用于查找可在定位过程中使用的最佳锚集，以实现更高的精度。其他普通传感器节点使用这组选定的锚点估计自己的位置。即使在存在恶意锚点的情况下，该算法也可以执行良好。根据定位精度评估了所提出算法的性能，存储空间，边界问题以及针对锚节点的恢复能力会危害攻击。评估是通过模拟进行的。根据结果​​，与其他算法相比，本文提出的RRGA算法在正常情况下可将定位误差降低至少约10％，在恶意锚节点攻击的情况下，可降低至少约50％。在正常情况下，它还可以将边界问题的影响降低至少约10％，在恶意锚节点攻击的情况下，至少降低约60％。此外，所需的存储空间至少改善了约50％。结果表明，RRGA的性能优于其他本地化算法。与其他算法相比，本文提出的RRGA算法在正常情况下可将定位误差降低至少约10％，在恶意锚节点攻击的情况下可降低至少约50％。在正常情况下，它还可以将边界问题的影响降低至少约10％，在恶意锚节点攻击的情况下，至少降低约60％。此外，所需的存储空间至少改善了约50％。结果表明，RRGA的性能优于其他本地化算法。与其他算法相比，本文提出的RRGA算法在正常情况下可将定位误差降低至少约10％，在恶意锚节点攻击的情况下可降低至少约50％。在正常情况下，它还至少将边界问题的影响降低了至少约10％，在恶意锚节点攻击的情况下，也将边界问题的影响降低了至少约60％。此外，所需的存储空间至少改善了约50％。结果表明，RRGA的性能优于其他本地化算法。在正常情况下，它还至少将边界问题的影响降低了至少约10％，在恶意锚节点攻击的情况下，也将边界问题的影响降低了至少约60％。此外，所需的存储空间至少改善了约50％。结果表明，RRGA的性能优于其他本地化算法。在正常情况下，它还至少将边界问题的影响降低了至少约10％，在恶意锚节点攻击的情况下，也将边界问题的影响降低了至少约60％。此外，所需的存储空间至少改善了约50％。结果表明，RRGA的性能优于其他本地化算法。