Energy-Efficient Void Avoidance Routing Technique For Underwater Sensor Networks.pdf

Underwater sensor networks (UWSNs) have become a significant research area due to their potential in collecting valuable data from underwater environments. However, one of the major issues in UWSNs is the void region, which degrades network performance. A node in the void region does not have a forwarder node to forward packets to another node. This work proposes an algorithm called "An Energy-Efficient Quality of Service (QoS) Parameter-Based Void Avoidance Routing Technique" to avoid the void region. The proposed algorithm uses two-hop node information and considers depth information, distance to next, holding time, and residual energy as QoS parameters to find the best forwarder node. The algorithm has been implemented in MATLAB, and results show a better performance in terms of packet delivery ratio, energy tax, and number of dead nodes compared to the Energy-Efficient Void Avoidance Routing Scheme for Underwater Wireless Sensor Network (E2RV).

The challenges in UWSNs include sensor node deployment, network connectivity, limited energy, low data rate due to acoustic communication, large propagation delay, unpredictable underwater environment, high equipment deployment cost, unscalability, no interaction, and complicated design and network deployment. The selection of the next forwarding node is a major issue in routing techniques, and researchers have generated algorithms to address this problem. The proposed algorithm uses a multi-metric approach to select the forwarder node, considering energy efficiency and authenticity. The use of residual energy metric balances energy between nodes, while link quality and depth metrics minimize energy consumption and upgrade packet delivery ratio.

The proposed algorithm has been compared with the E2RV algorithm, and results show a better performance in terms of packet delivery ratio, energy tax, and number of dead nodes. The algorithm has been implemented in MATLAB, and results show a significant improvement in network performance. The proposed algorithm can be used in various underwater applications, such as environmental monitoring, military operations, and ocean exploration.