Q-learning based energy-efficient and void avoidance routing protocol for underwater acoustic sensor networks

Khan, Zahoor Ali; Karim, Obaida Abdul; Abbas, Shahid; Javaid, Nadeem; Zikria, Yousaf Bin; Tariq, Usman

doi:10.1016/j.comnet.2021.108309

Cited by 35 publications

(21 citation statements)

References 37 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%

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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%

“…Khan et al, 37 have presented a Q‐learning base energy‐efficient and balanced data gathering. In QLEEBDG, the FNs were selected as per its residual energy and grouped as per its neighboring nodes energies.…”

Section: Literature Surveymentioning

confidence: 99%

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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“…Khan et al [7] suggest that a model-free algorithm based on energy and collecting of information to be kept efficiently as well as in balanced conditions using routing protocol. The frequency of the forwarding nodes is chosen on the basics of their residuary power and so joined to their nearby nodes' power.…”

Section: Related Workmentioning

confidence: 99%

Efficient Routing Protocol with Localization Based Priority & Congestion Control for UWSN

Sandhiyaa¹,

Gomathy²

2023

Computers, Materials &Amp; Continua

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The nodes in the sensor network have a wide range of uses, particularly on under-sea links that are skilled for detecting, handling as well as management. The underwater wireless sensor networks support collecting pollution data, mine survey, oceanographic information collection, aided navigation, strategic surveillance, and collection of ocean samples using detectors that are submerged in water. Localization, congestion routing, and prioritizing the traffic is the major issue in an underwater sensor network. Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource. Minimization of localization error using the proposed angle-based forwarding scheme is explained in this paper. We choose the shortest path to the destination using the fitness function which is calculated based on fault ratio, dispatching of packets, power, and distance among the nodes. This work contemplates congestion conscious forwarding using hard stage and soft stage schemes which reduce the congestion by monitoring the status of the energy and buffer of the nodes and controlling the traffic. The study with the use of the ns3 simulator demonstrated that a given algorithm accomplishes superior performance for loss of packet, delay of latency, and power utilization than the existing algorithms.

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“…To address these issues, reinforcement learning-based routing protocols are utilized. Reinforcement learning (RL) uses a trial-and-error technique of learning the environmental attributes and it can be employed with the use of the Markov decision process [2]. Using this paradigm, a node attempts to gather information from its neighbors, for example, the buffer occupancy, number of hops from a particular destination, energy level, to name a few.…”

Section: Introductionmentioning

confidence: 99%

Double Q-learning based routing protocol for opportunistic networks

Singh

Dhurandher

Woungang

et al. 2023

JHS

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Opportunistic Delay Tolerant Networks also referred to as Opportunistic Networks (OppNets) are a subset of wireless networks having mobile nodes with discontinuous opportunistic connections. As such, developing a performant routing protocol in such an environment remains a challenge. Most research in the literature have shown that reinforcement learning-based routing algorithms can achieve a good routing performance, but these algorithms suffer from under-estimations and/or over-estimations. Toward addressing these shortcomings, in this paper, a Double Q-learning based routing protocol for Opportunistic Networks framework named Off-Policy Reinforcement-based Adaptive Learning (ORAL) is proposed, which selects the most suitable next-hop node to transmit the message toward its destination without any bias by using a weighted double Q-estimator. In the next-hop selection process, a probability-based reward mechanism is involved, which considers the node’s delivery probability and the frequency of encounters among the nodes to boost the protocol’s efficiency. Simulation results convey that the proposed ORAL protocol improves the message delivery ratio by maintaining a trade-off between underestimation and overestimation. Simulations are conducted using the HAGGLE INFOCOM 2006 real mobility data trace and synthetic model, showing that when time-to-live is varied, (1) the proposed ORAL scheme outperforms DQLR by 14.05%, 9.4%, 5.81% respectively in terms of delivery probability, overhead ratio and average delay; (2) it also outperforms RLPRoPHET by 16.17%, 9.2%, 6.85%, respectively in terms of delivery ratio, overhead ratio and average delay.

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Q-learning based energy-efficient and void avoidance routing protocol for underwater acoustic sensor networks

Cited by 35 publications

References 37 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

Efficient Routing Protocol with Localization Based Priority & Congestion Control for UWSN

Double Q-learning based routing protocol for opportunistic networks

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