Routing Void Prediction and Repairing in AUV-Assisted Underwater Acoustic Sensor Networks

Abstract

Underwater Acoustic Sensor Networks have attracted much attention due to various applications. However, routing voids lead performance degradation of UASNs in terms of network connectivity and packet delivery ratio. In this paper, we propose a Routing Void Prediction and Repairing (RVPR) algorithm in AUV-assisted UASNs, which utilizes AUVs to carry sensor nodes to repair the routing voids when foreseeing the occurrence of voids. First, the repair position is calculated based on Particle Swarm Optimization algorithm by maximizing the connectivity of the void area and minimizing the AUV moving distance. Then, the routing void prediction based on Markov chain model is proposed to ensure that the AUVs come to the repair task before the voids have already formed. Next, we design a task selecting rule to let the AUVs choose the most important and urgent repair task. Lastly, RVPR applies an energy-efficient interaction mechanism among nodes and AUVs, which guarantees reliable operation of the algorithm. In the simulation, the RVPR algorithm is applied in several different types of routing protocols (HHVBF, QELAR, EAVARP). The simulation results show that RVPR algorithm improves the protocol performances in terms of the packet delivery ratio and the link connection. More specifically, when there are 100 nodes deployed in the network, the packet delivery ratio of HHVBF, EAVAPR and QELAR employing RVPR are increased by 29.4%,79% and 65% respectively. INDEX TERMS Underwater acoustic sensor networks, routing voids, autonomous underwater vehicle.

REVOHPR: Relay-based Void Hole Prevention and Repair by Virtual Routing in Clustered Multi-AUV Underwater Wireless Sensor Network

“…If the nodes with low energy are located far away from the current AUV position, then there will be higher energy consumption. The greedy algorithm has higher time consumption and complexity In [38] authors proposed an AUV assisted void prediction and repair mechanism in underwater sensor network. The repair position is calculated by Particle Swarm Optimization (PSO) algorithm.…”

Underwater Networked Wireless Sensor Data Collection for Computational Intelligence Techniques: Issues, Challenges, and Approaches

“…During the time of fault in UWSN lot of cost is required due to high range of battery prices used in UWSN and bigger cost for the ship management team which repairs the fault area underwater. Since it seems that there is imbalance in energy consumption at the time of the transmission, for that there must me one central or parent nodes in UWSN which restricts the network traffic to pass only the needed data [24][25][26]. Being having control of all other nodes the battery life of the central node will be depleted sooner as the central node is responsible for the transmission of data at the time of battery faults the node is unable to transmit the data results in the error in working of the application [27][28][29].…”

Modified Mackenzie Sound Equation and CVOA Algorithm Based UASN Routing in Dissolved Gases with Divergent Wind Environment to Reduce Propagation Delay

“…Prediction or measurement of the ocean environment and water column parameters need efficient methods. However, algorithms such as VODA [1], RVPR [2], glider-assist routing [3], DVOR [4], DQELR [5], RCAR [6], DSR-SDN [7], EBOR [8] developed for efficient routing in different ocean environmental conditions, whereas for water column variations such as geometric spread, sedimentation drift, Doppler effect few UASN algorithms proposed such as COCAN, LOCAN [9]. In UASN, acoustic signal performs better than extreme Low frequencies (ELF) due to less attenuation in underwater.…”