Distributed partition detection and recovery using UAV in wireless sensor and actor networks

Abstract

Wireless Sensor and Actor Networks (WSANs) have been extensively employed in various domains ranging from elementary data collection to real-time control and monitoring for critical applications. Network connectivity is a vital robustness measure for overall network performance. Different network functions such as routing, scheduling, and QoS provisioning depends on network connectivity. The failure of articulation points in the network disassociates the network into disjoint segments. We proposed Distributed Partition Detection and Recovery using Unmanned Aerial Vehicle (UAV) (DPDRU) algorithm, as an optimal solution to recover the partitioned network. It consists of three steps: Initialization, Operational and Detection, and Recovery. In the Initialization phase sink node collects all the information about the network. In the Operational and Detection phase, network nodes communicate regularly by exchanging HEARTBEATS, detects failure if some nodes do not get a message from the neighbor node and send failure reports, and sink node identifies network partition. In the recovery phase, the sink node sends UAV at the positional coordinates of the failed node and examines network recovery after UAV reaches the desired location. Our approach primarily focuses on reducing message overhead by sending few update messages to sink node and energy consumption by engaging network nodes only for communication. The requirements of the recovery process (physical movement and communication) are fulfilled by UAV. The algorithm is tested according to the following parameters: Detection Time, Recovery Time, message overhead, and distance traveled by UAV. Simulation results validate the efficacy of the proposed algorithm based on these parameters to provide reliable results. The minimum and the maximum number of messages transmitted are 11 for 10 nodes and 24 for 100 nodes respectively. Hence these results demonstrate that the message overhead in our proposed solution is less as compared to other techniques when the number of nodes increases.

Network partition detection and recovery with the integration of unmanned aerial vehicle

“…The details of the experimental results and their analysis with respect to these parameters are described below. The proposed algorithm is compared with the DPDRU algorithm proposed in Reference 46. DPDRU algorithm engages network nodes to detect node failure and report this to the sink node.…”

“…This approach focused on deploying the relay nodes near the centroid of the triangle. Zear and Ranga 46 proposed DPDRU algorithm to recover the network partitioned due to a single cut‐vertex node failure. The algorithm employs network nodes to detect the failure of their neighboring node and report this failure to the sink.…”

Dynamic weight-based connectivity recovery in wireless sensor and actor networks

“…Too many nodes are involved in cascaded motion, and some network recovery methods overreact to general node failures, which can result in an excessive number of relocated nodes and long recovery times. In summary, determining how to discover and resolve node failures as soon as possible and autonomously in a distributed environment [27] is crucial.…”

QoS based congestion evasion clustering framework of wireless sensor networks

“…Wireless sensor networks comprise profuse sensor nodes to create an ad hoc distributed data proliferation network that collects context information about the physical environment (Shahraki et al, 2020). Routing (Zear et al, 2021;Saha et al, 2021) would not be an intricate calculation and can acclimate to dynamic topology changes, ensuring consistent energy indulgence across a network while also helping to accomplish the quality of services. The multipath routing strategy is extensively utilized in WSN to increase network performance by efficiently using the available network resources.…”