A synchronous duty-cycled reservation based MAC protocol for underwater wireless sensor networks

Roy, Alak; Sarma, Nityananda

doi:10.1016/j.dcan.2020.09.002

Cited by 26 publications

(10 citation statements)

References 22 publications

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“…Here, the various evaluation metrics, like network delay, Energy consumption and delay, throughput, efficiency, packet delivery ratio are analyzed. The performance of the proposed ADGRP‐DL‐MEM‐BASA for UWSNs is analyzed and compared with the existing methods, such as EEPDBR‐UWSN, 34 OCMAC‐UWSN, 15 QL‐EEBDG‐UWSN, 37 EDORQ‐UWSN, 36 CARMA‐ EE‐UWSN 35 algorithm for routing in UWSNs. Then by varying the number of nodes with a fixed data rate of 5000 Mbps in the network.…”

Section: Resultsmentioning

confidence: 99%

“…Roy and Sarma 15 have presented ordered contention MAC (OCMAC) protocol was used in the synchronal duty‐cycled reservation process. Where, the transmitter transmits the data through the ready to send (RTS) frames.…”

Section: Literature Surveymentioning

confidence: 99%

“…In this the underwater sensor node needs high battery power to transmit and to receive the data 10–14 . To reduce such issues AUV is used to save the power 15 . UWSNs are a network which works in the limited battery, and the network life time is decreased 16,17 .…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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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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“…Their analysis shows that a hybrid of Time-Division Multiple Access (TDMA) and Carrier-Sense Multiple Access with Collision Avoidance (CSMA/CA) gives signiicant results if they switched based on the number of competitors' nodes. Considering bursty traic nature in an underwater network, Roy et al [33] increases throughput performance by reducing collisions during contention. The authors analyze the energy eiciency and reliability with a Markov Chain process.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-Based MAC Protocol for Underwater Multimedia Sensor Networks

Gazi

Ahmed

Misra

et al. 2022

ACM Trans. Sen. Netw.

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High propagation delay, high error probability, floating node mobility, and low data-rate, are the key challenges for Underwater Wireless Multimedia Sensor Networks (UMWSNs). In this paper, we propose RL-MAC, a Reinforcement Learning (RL)-based Medium Access Control (MAC) protocol for multimedia sensing in an Underwater Acoustic Network (UAN) environment. The proposed scheme uses Transmission Opportunity (TXOP) for relay nodes in a multi-hop network for improved efficiency concerning the mobility of the relays and sensor nodes. The AP and relay nodes calculate traffic demands from the initial contention of the sensor nodes. Our solution uses Q-learning to enhance the contention mechanism at the initial phase of multimedia transmission. Based on the traffic demands, RL-MAC allocates TXOP duration for the uplink multimedia reception. Further, Structural Similarity Index Measure (SSIM) and compression techniques are used for calculating the image quality at the receiver side and reduce the image at the destination, respectively. We implement a prototype of the proposed scheme over an off-the-shelf, low-cost hardware setup. Moreover, extensive simulation over NS-3 shows a significant packet delivery ratio and throughput compared to the existing state-of-the-art.

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“…Wireless Sensor Networks (WSNs) are widely valued and have received significant attention in the last two decades. They are low cost and flexible thus they are widely applied in many applications scenarios ranging from health-care [ 1 , 2 ], industrial inspection [ 3 ], environmental monitoring [ 4 , 5 ], military defense [ 6 , 7 ], agriculture monitoring [ 8 , 9 ]. Coverage, as a fundamental issue in WSNs, has a direct impact on the networks’ efficiency.…”

Section: Introductionmentioning

confidence: 99%

Maximum Target Coverage Problem in Mobile Wireless Sensor Networks

Liang

Shen

Chen

2020

Sensors

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We formulate and analyze a generic coverage optimization problem arising in wireless sensor networks with sensors of limited mobility. Given a set of targets to be covered and a set of mobile sensors, we seek a sensor dispatch algorithm maximizing the covered targets under the constraint that the maximal moving distance for each sensor is upper-bounded by a given threshold. We prove that the problem is NP-hard. Given its hardness, we devise four algorithms to solve it heuristically or approximately. Among the approximate algorithms, we first develop randomized (1−1/e)-optimal algorithm. We then employ a derandomization technique to devise a deterministic (1−1/e)-approximation algorithm. We also design a deterministic approximation algorithm with nearly ▵−1 approximation ratio by using a colouring technique, where ▵ denotes the maximal number of subsets covering the same target. Experiments are also conducted to validate the effectiveness of the algorithms in a variety of parameter settings.

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A synchronous duty-cycled reservation based MAC protocol for underwater wireless sensor networks

Cited by 26 publications

References 22 publications

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

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

Reinforcement Learning-Based MAC Protocol for Underwater Multimedia Sensor Networks

Maximum Target Coverage Problem in Mobile Wireless Sensor Networks

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