The emergence of IoT technology makes the applications of IoT wireless sensor networks (WSNs) attract more attentions, and most of the applications are based on the sensor’s location. This paper addresses two main challenges encountered in the localization issue in IoT WSNs, i.e., limited energy in battery-powered sensors, and non-line-of-sight (NLOS) induced errors in harsh environments. We propose a node selection algorithm and an NLOS mitigation algorithm to modify the conventional cooperative localization algorithm for an improved performance. In the node selection algorithm, a node selection criterion is designed to select the most informative reference nodes for each agent, avoiding unnecessary energy consumption. An $N$-probabilistic hard weight ($N$-PHW) strategy is developed in the NLOS mitigation algorithm, in which a link condition indicator is set to quantify the quality of each link and NLOS errors are penalized based on this indicator. Numerical results show that by virtue of the proposed node selection algorithm, the energy consumption of the network is significantly reduced. Furthermore, the localization accuracy is improved with the proposed NLOS mitigation algorithm, especially in more severe NLOS environments.

物联网技术的出现使得物联网无线传感器网络（WSN）的应用吸引了更多的关注，并且大多数应用基于传感器的位置。本文解决了物联网WSN本地化问题中遇到的两个主要挑战，即电池供电传感器的能量有限以及恶劣环境下非视距（NLOS）引起的错误。我们提出了一种节点选择算法和一种NLOS缓解算法来修改传统的协作定位算法，以提高性能。在节点选择算法中，设计了一个节点选择标准来为每个代理选择信息量最大的参考节点，从而避免了不必要的能耗。一个$ñ$-概率硬重（$ñ$-PHW）策略是在NLOS缓解算法中开发的，其中设置了链接条件指示器以量化每个链接的质量，并基于此指示器对NLOS错误进行了惩罚。数值结果表明，借助所提出的节点选择算法，可以显着降低网络的能耗。此外，通过提出的NLOS缓解算法提高了定位精度，尤其是在更严酷的NLOS环境中。