A Systematic Analysis, Outstanding Challenges, and Future Prospects for Routing Protocols and Machine Learning Algorithms in Underwater Wireless Acoustic Sensor Networks

Abstract

Water has covered a wide part of the earth’s surface. Oceans and other water bodies contain significant natural and environmental resources as well as aquatic life. Due to humans’ hazardous and unsuitable underwater (UW) settings, these are generally undiscovered and unknown. As a result of its widespread utility in fields as diverse as oceanography, ecology, seismology, and oceanography, underwater wireless sensor networks (UWSNs) have emerged as a cutting-edge area of study. Despite their usefulness, the performance of the network is hampered by factors including excessive propagation delay, a changing network architecture, a lack of bandwidth, and a battery life that is too short on sensor nodes. Developing effective routing protocols is the best way to overcome these challenges. An effective routing protocol can relay data from the network’s root node to its final destination. Therefore, the state of the art in underwater wireless acoustic sensor network (UWASN) routing protocols is assessed with an eye toward their potential for development. In real-world applications, sensor node positions are frequently used to locate relevant information. As a result, it is crucial to conduct research on routing protocols. Reinforcement learning (RL) algorithms have the ability to enhance routing under a variety of conditions because they are experience-based learning algorithms. Underwater routing methods for UWSN are reviewed in detail, including those that rely on machine learning (ML), energy, clustering and evolutionary approaches. Tables are incorporated for the suggested protocols by including the benefits, drawbacks, and performance assessments, which make the information easier to digest. Also, several applications of UWSN are discussed with security considerations. In addition to this, the analysis of node deployment and residual energy is discussed in this review. Furthermore, the domain review emphasizes UW routing protocol research difficulties and future directions, which can help researchers create more efficient routing protocols based on ML in the future.