Local Maximum Routing Recovery in Underwater Sensor Networks: Performance and Trade-offs

Abstract

Local maximum problem, where the node fails in determine the next-hop neighbor to continue forwarding the packet towards the destination, severely degrades the performance of geographic routing protocols. This degradation is more substantial in underwater sensor networks, that intrinsically have harsh environments and high energy consumption due to the underwater acoustic communication characteristics. In this work, we focus on the performance of the most commonly three methodologies used in design of local maximum recovery procedures of geographic routing protocols for underwater sensor networks: power control, bypassing void regions, and mobility controlled. Taking into consideration the underwater acoustic communication characteristics, we further develop a network energy consumption model for representative solutions of local maximum recovery procedure designed from these methodologies and then we evaluate their performance in terms of the improvements on routing task and the energy consumption. Simulation results show that all three methodologies improve the routing even in hard scenarios of lower network density whereas particularities of each methodology impacts on the network energy consumption.

Towards Controlled Transmission: A Novel Power-Based Sparsity-Aware and Energy-Efficient Clustering for Underwater Sensor Networks in Marine Transport Safety

“…The GRACE [33] principle of gradient cost field establishment, which is inspired by the natural phenomenon of water flowing down a valley from top to bottom, is the foundation of our adaptive power control routing model. Data propagates in the same way, in the direction where it finds the cheapest path.…”

“…Two new methods have been built in [33] to restrict energy consumption and remove the existence of a void zone in [34]. During the first approach, mobile sink nodes or AUVs play an important role in reducing node contractual power consumption, while the second strategy entails planning and cooperation with the first.…”