An efficient energy consumption and delay aware autonomous data gathering routing protocol scheme using a deep learning mobile edge model and beetle antennae search algorithm for underwater wireless sensor network

Abstract Underwater wireless sensor network (UWSN) is used to monitor the compactness of ocean surveillance, marine and harsh underwater environment. In this article, an efficient energy consumption and delay aware autonomous data gathering routing protocol (ADGRP) scheme based on deep learning (dl) mobile edge model (mem) and beetle antennae search algorithm (BASA) for UWSN is proposed to overcome above problems. ADGRP is used to gather more data from the underwater environment by the use of the autonomous underwater vehicle (AUV). DL‐MEM is used to increase the network life time. Then the deep learning parameters are optimized by using BAS. The objective function is “to increase the efficiency and lifetime of network by decreasing the energy consumptions and delay.” The simulation process is carried out in MATLAB site. The proposed ADGRP‐DL‐MEM‐BASA provides lower energy consumption 20.83%, 34.66%, 18.03%, 20.92%, 22.34%, lower energy drop 7.85%, 23.94%, 17.93%, 21.93%, 31.94% is compared with the existing energy‐efficient probabilistic depth‐based routing (EEPDBR‐UWSN), ordered contention MAC (OCMAC‐UWSN), Q‐learning based energy‐efficient and void avoidance routing protocol for underwater acoustic sensor networks (QL‐EEBDG‐UWSN), energy‐efficient depth‐base opportunistic routing along Q‐learning for underwater wireless sensor networks (EDORQ‐UWSN), channel‐aware reinforcement learning‐based multipath adaptive routing for underwater wireless sensor networks (CARMA‐ EE‐UWSN) respectively.

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