Sensor-node localization is among the greatest concerns in the field of wireless sensor networks. Range-based localization techniques generally outperform range-free techniques, particularly in terms of their accuracy. Range-based localization techniques depend on a popular distance estimation method, which requires conversion from a received signal strength indicator to distances. In a case where sensor nodes are in an area with obstacles, direct communication between certain pairs of nodes is impracticable; the data must be relayed over multihop (or detour) routes. One promising approach to improve the accuracy of sensor-node distance estimation is to segment (or cluster) sensor nodes to a restricted set of anchor nodes whose estimated distances to unknown nodes are not on a detour route. Some certain topologies can decrease the localization precision; e.g., when each group’s node density is low, large empty spaces (or gaps) might affect the localization precision. If an unknown node is close to another group, using only anchor nodes within its own group could reduce the estimation precision. When anchor nodes within the same group lie along a straight line, the approximation of the unknown-node location could be misinterpreted. Thus, to enhance the localization precision, we make use of anchor nodes in other nearby groups to estimate the locations of unknown nodes. We also apply particle swarm optimization (PSO) with an improved fitness function to estimate the locations of unknown nodes. The localization performance is intensively evaluated in obstacle-prone scenarios. The simulation results show that the proposed scheme achieves higher accuracy than recent state-of-the-art PSO-based methods.

传感器节点的本地化是无线传感器网络领域最关注的问题。基于范围的定位技术通常要优于无范围的技术，尤其是在准确性方面。基于范围的定位技术取决于一种流行的距离估计方法，该方法需要从接收到的信号强度指示符转换为距离。在传感器节点位于有障碍物的区域的情况下，某些节点对之间的直接通信是不可行的。数据必须通过多跳（或绕行）路线中继。一种提高传感器节点距离估计精度的有前途的方法是将传感器节点分段（或聚类）为一组受限的锚节点，这些锚节点到未知节点的估计距离不在绕行路线上。一些特定的拓扑会降低定位精度。例如，当每个组的节点密度较低时，较大的空白空间（或间隙）可能会影响定位精度。如果未知节点靠近另一个组，则仅使用其自身组内的锚点节点可能会降低估计精度。当同一组内的锚点节点沿着一条直线放置时，未知节点位置的近似值可能会被误解。因此，为了提高定位精度，我们利用附近其他组中的锚节点来估计未知节点的位置。我们还应用具有改进的适应度函数的粒子群优化（PSO）来估计未知节点的位置。在容易发生障碍的情况下，将对本地化性能进行深入评估。