Wireless Sensor Networks (WSNs) often operate in hostile environments and are subject to frequent failures. Failure of multiple sensor nodes causes the network to split into disjoint segments, which leads to network partitioning. Federating these disjoint segments is necessary to prevent detrimental effects on WSN applications. This paper investigates a recovery strategy using mobile relay nodes (MD-carrier) for restoring network connectivity. The proposed MD-carrier Tour Planning (MDTP) approach restores network connectivity of partitioned WSNs with reduced tour length and latency. For this reason, failure nodes are identified, and disjoint segments are formed with the k-means algorithm. Then, the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are used for the election of an AGgregator Node (AGN) for each segment. Furthermore, an algorithm for identifying sojourn locations is proposed, which coordinates the maximum number of AGNs. Choosing the sojourn locations is a challenging task in WSN since the incorrect selection of the sojourn locations would degrade its data collection process. This paper uses the nature-inspired meta-heuristic Donkey And Smuggler Optimization (DASO) algorithm to compute the optimal touring path. MDTP reduces tour length and latency by an average of 30.28% & 24.56% compared to existing approaches.

无线传感器网络 (WSN) 通常在恶劣的环境中运行，并且经常出现故障。多个传感器节点的故障导致网络分裂成不相交的段，从而导致网络分区。为了防止对 WSN 应用产生有害影响，有必要联合这些不相交的段。本文研究了使用移动中继节点（MD-Carrier）恢复网络连接的恢复策略。所提出的 MD 载波巡视规划 (MDTP) 方法可恢复分区 WSN 的网络连接，同时减少巡视长度和延迟。因此，可以识别故障节点，并使用 k 均值算法形成不相交的段。然后，使用层次分析法（AHP）和理想解相似度排序技术（TOPSIS）为每个分段选择聚合器节点（AGN）。此外，还提出了一种识别停留位置的算法，该算法协调 AGN 的最大数量。选择停留位置是 WSN 中的一项具有挑战性的任务，因为错误选择停留位置会降低其数据收集过程。本文使用受自然启发的元启发式驴子和走私者优化（DASO）算法来计算最佳游览路径。与现有方法相比，MDTP 平均缩短了游览长度和延迟 30.28% 和 24.56%。