Coordinated network partition detection and bi-connected inter-partition topology creation in damaged sensor networks using multiple UAVs

Abstract

Reliable connectivity restoration mechanism is essential for retaining the prolonged network functioning of Wireless Sensor and Actor Networks (WSANs). WSANs operate in harsh or inhospitable environments and are susceptible to multiple node failures that may fragment the fully connected network into disparate fragments. The well-known network recovery techniques deploy new relay nodes for recovery and introduce more cut vertices in the network that generates the resulting network topology vulnerable to further partitioning. Therefore, it is advantageous to identify the network partitions in less time and establish a fault-tolerant topology during the network restoration. This paper provides a new coordinated network partition detection and Bi-connected Inter-partition Topology creation solution in damaged sensor networks using multiple UAVs (UAV-BITS) that operates in different phases. UAV-BITS employs multiple UAVs to identify the network partitions and engages network nodes to self-identify their partitions by creating Connected Component Sets (CCSs) iteratively. During the network recovery process, UAV-BITS creates two-vertex disjoint paths between the network partitions to make the recovered network topology fault-tolerant. UAV-BITS is assessed according to various parameters like the number of deployed relay nodes, the number of messages transmitted during detection and recovery phases, network partition detection and recovery time, and distance traveled by UAVs. The performance of CCS based partition detection mechanism is also compared with a similar UAVs-assisted partition detection approach. The working of existing bi-connected topology creation algorithms that employ representative nodes is also analyzed and compared with the proposed recovery algorithm. The simulation results illustrate that UAV-BITS provides a better fault-tolerant network topology than already proposed solutions. The algorithm also reduces the travel distance of UAVs and detection time.