Partition detection and recovery by UAVs in damaged WSANs using N-angle clustering

Connectivity is essential in wireless sensor and actor networks (WSANs) deployed for data collection and monitoring applications. WSANs serving in inhospitable environments are prone to damage, and sometimes, the concurrent multiple node failures in the same area may cause network partitioning. The autonomous network recovery in harsh environments is challenging because the nodes in different partitions cannot determine the scope of the damage. This paper proposes an approach called partition detection and recovery by unmanned aerial vehicles (UAVs) in damaged WSANs using N-angle clustering (N-anglePDRU), which consists of all the phases required to accumulate information about the network partitions and reconnect them successfully. The algorithm is evaluated for different randomly generated network topologies according to various state-of-the-art parameters such as detection and recovery time, messages transmitted, relay node count, and distance traveled by UAVs. The proposed approach is also compared with the existing algorithms, and the obtained results demonstrate that N-anglePDRU recovers the partitioned network in less recovery time with minimum relay nodes and distance traveled.

Automated summary

This paper introduces an approach for partition detection and recovery in damaged WSANs using unmanned aerial vehicles (UAVs), which outperforms existing algorithms in terms of recovery time, relay nodes, and UAVs' travel distance.