A Survey on MAC Protocol Approaches for Underwater Wireless Sensor Networks

Guqhaiman, Ahmed Al; Akanbi, Oluwatobi Ayodeji; Aljaedi, Amer; Chow, Chee-Onn

doi:10.1109/jsen.2020.3024995

Cited by 62 publications

(34 citation statements)

References 36 publications

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Section: Issues Summarymentioning

confidence: 99%

“…In this paper, we classify underwater MAC protocols into contention-free (i.e., schedule-based), contention-based (i.e., reservation-based), and hybrid. Contention-free MAC protocols reserve a shared channel for a specific node based on frequency, code, or time [ 34 ]. Contention-based MAC protocols are further classified into random access and handshaking-based.…”

Section: Related Workmentioning

confidence: 99%

“…This table shows that there are eight common performance metrics (i.e., E2ED, EC, PDR, EE, TC, Thpt, COR, and FI). In our earlier work [ 34 ], we evaluated the E2ED, EC, PDR, TC, and Thpt. To provide a complete analysis, it is necessary to conduct further analysis regarding EE, COR, and FI.…”

Section: Related Workmentioning

confidence: 99%

“…This known schedule determines when each node should wake up to transmit its own data packets or to receive packets from neighboring nodes. This schedule helps to avoid collisions between data packets coming from different nodes, aligns the sleep schedule to the data schedule, and hence improves the network’s performance, in terms of E2ED, EC, PDR, TC, and Thpt [ 34 ]. To provide a full analysis of the proposed protocol, we focused on evaluating the impact of all MAC protocols in terms of EE, COR, and FI.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Furthermore, we consider concurrent transmissions to enhance channel use and hence increase network throughput. Consequently, the proposed protocol can outperform well-known MAC protocols in terms of E2ED, EC, PDR, TC, and Thpt [ 34 ]. The main contributions of this paper are as follows: Identify the most efficient MAC protocol approach for a pipeline topology over multi-hop for time-sensitive applications relative to the EE, COR, and FI.…”

Section: Introductionmentioning

confidence: 99%

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DAMAC: A Delay-Aware MAC Protocol for Ad Hoc Underwater Acoustic Sensor Networks

Guqhaiman

Akanbi

Aljaedi

et al. 2021

Sensors

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In a channel shared by several nodes, the scheduling algorithm is a key factor to avoiding collisions in the random access-based approach. Commonly, scheduling algorithms can be used to enhance network performance to meet certain requirements. Therefore, in this paper we propose a Delay-Aware Media Access Control (DAMAC) protocol for monitoring time-sensitive applications over multi-hop in Underwater Acoustic Sensor Networks (UASNs), which relies on the random access-based approach where each node uses Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) to determine channel status, switches nodes on and off to conserve energy, and allows concurrent transmissions to improve the underwater communication in the UASNs. In addition, DAMAC does not require any handshaking packets prior to data transmission, which helps to improve network performance in several metrics. The proposed protocol considers the long propagation delay to allow concurrent transmissions, meaning nodes are scheduled to transmit their data packets concurrently to exploit the long propagation delay between underwater nodes. The simulation results show that DAMAC protocol outperforms Aloha, BroadcastMAC, RMAC, Tu-MAC, and OPMAC protocols under varying network loads in terms of energy efficiency, communication overhead, and fairness of the network by up to 65%, 45%, and 726%, respectively.

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Section: Issues Summarymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DAMAC: A Delay-Aware MAC Protocol for Ad Hoc Underwater Acoustic Sensor Networks

Guqhaiman

Akanbi

Aljaedi

et al. 2021

Sensors

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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

Pradeep¹,

Bapu²

2022

Concurrency and Computation

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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. K E Y W O R D Sautonomous data gathering routing protocol, beetle antennae search optimization algorithm, deep learning mobile edge model, underwater wireless sensor network INTRODUCTIONNowadays, wireless sensors are used in many purposes, such as sensing in the underwater properties, remote sensing areas, and in the ocean and the marine underwater scenarios. 1 It is used to sensing, collecting information from large areas by the help of the sensors present in everynodes. 2 In this wireless sensors consists of many nodes and that nodes are connected by the sensors to collect the data from the underwater environments. 3 If any one of the node get damaged the system consumes more energy and cause delay to collect the data. 4 Then the network life time also decreased. 5

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