The ability to obtain the accurate location of nodes in wireless sensor networks is crucial for practical applications. The sensed data is meaningless if it is not accompanied by its location. Range-free localization techniques are favored to overcome the hardware limitations of sensor nodes and to avoid the costly range-based techniques. DV-Hop is a range-free localization algorithm that is well-known for its simplicity. However, it suffers from low accuracy and poor stability. In this paper, an enhanced variant of the DV-Hop algorithm is used to estimate the distance between the unknown nodes and anchor nodes, then the position estimation phase is formulated as a minimization problem solved by means of the recently developed squirrel search algorithm (SSA). The SSA is utilized to find the locations of the unknown sensor nodes. Our proposed algorithm is thus called SSIDV-Hop algorithm. The performance of our proposed algorithm is compared to that of existing localization algorithms including the DV-Hop, PSODV-Hop, GADV-Hop, and DEIDV-Hop algorithms. Extensive simulations showed that our proposed algorithm is superior to other existing algorithms as it achieved higher localization accuracy, better stability and faster convergence rate.

获得无线传感器网络中节点的准确位置的能力对于实际应用至关重要。如果不伴随其位置，则感测到的数据是没有意义的。无范围定位技术被推荐用来克服传感器节点的硬件限制并避免昂贵的基于范围的技术。DV-Hop是一种无范围定位算法，以其简单性而闻名。但是，它具有精度低和稳定性差的缺点。本文使用DV-Hop算法的增强型变体来估计未知节点与锚节点之间的距离，然后将位置估计阶段公式化为通过最近开发的松鼠搜索算法（SSA）解决的最小化问题）。利用SSA查找未知传感器节点的位置。因此，我们提出的算法称为SSIDV-Hop算法。将我们提出的算法的性能与现有的本地化算法（包括DV-Hop，PSODV-Hop，GADV-Hop和DEIDV-Hop算法）进行了比较。大量的仿真表明，我们提出的算法优于其他现有算法，因为它具有更高的定位精度，更好的稳定性和更快的收敛速度。