Energy-Efficient Depth Based Probabilistic Routing Within 2-Hop Neighborhood for Underwater Sensor Networks

Zhang, Meiyan; Cai, Wenyu

doi:10.1109/lsens.2020.2995236

Cited by 42 publications

(34 citation statements)

References 13 publications

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“…Even though, using a multi-sink mechanism to avoid the quick depletion of batteries power for the nodes nearer to the sink node, but no improvements regarding FNI are achieved. 5) Energy Efficient Depth Based Routing (EEDBR) [35]: In addition to the depth metrics, this protocol addresses the residual energy of node against the consequence of the horrible death of low-depth DBR nodes. If a sender node is essential to choose a forwarding node, it must pick the lowest depth having greater residual energy forwarder, from its one-hop neighbors.…”

Section: B Location Free (Or) Forwarding Node Impingementmentioning

confidence: 99%

Underwater Pragmatic Routing Approach Through Packet Reverberation Mechanism

et al. 2020

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The advances in underwater sensor communication has become imperative getting up-to-date information about underwater happenings, especially when world has already faced the calamity like Tsunami. The underwater environment possessed freak and unpredictable movements which becomes more harsh time to time. The sensor nodes deployed under such juncture are the main source of information which in fact, facing numerous challenges. These nodes are mainly energy-constrained and rely on limited battery source. Due to most intricated underwater routing architecture, the biggest detriment is the limited battery lifespan. Therefore, it is imperative to adopt the pragmatic and possible alternate to improve the life expectancy of these sensor nodes. The solution of such shortcomings and identifying the varieties of impingements impelled by forwarding node on battery lifespan during packet transmission course are meticulously explored by developing an Underwater Pragmatic Routing Approach through Packet Reverberation mechanism (UPRA-PR). It is a novel approach and never considered in past. Through Packet Reverberation technique, the use of energy has been confined and the desired outcomes are achieved in four phases. In the first phase, the eligibility criteria for both packet and nodes have been computed by setting the Node Depth Factor (Ndf). Second phase formulates the forwarding relay node mechanism and rummage out the path failure by complying a Data Rate criterion 0. The selection of the shrewd communication link is established in third phase by considering an Accepted Link Quality (ALQ) factor. The fourth phase where most prominent developments has been made regarding impingements effects on the battery lifespan left by the forwarding node after the packet transmission. The UPRA-PR performance metrics are assessed by staging extensive NS2 simulation with AquaSim 2.0 and compared to state-existing routing protocols i.e., DBR, H 2 DAB, GEDAR and FBR for Packet dissemination ratio, Path failure, Point-to-point delay estimation, System energy consumption, Network lifespan, Forwarding node impingement and Network throughput. The simulation results have ratified the UPRA-PR performance and justified the statements made in this respect.

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Section: B Location Free (Or) Forwarding Node Impingementmentioning

confidence: 99%

Underwater Pragmatic Routing Approach Through Packet Reverberation Mechanism

et al. 2020

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“…Various strategies have been proposed in the literature for reliable data distribution in which the sense node dynamically detects and utilizes the effective bandwidth and energy [10], [11], [12], thereby improving the resource utilization of WSNs. This article considers the energy-efficient depthbased probabilistic routing (EEPDB) algorithm in [13] for WSNs that uses the node's vertical depth, residual energy and forwarding number within a 2-hop neighbourhood to determine the forwarding probability of qualified candidates and thus avoid the local optimization problem and packet delivery failure in some scenarios. In addition, the proposed flow splitting optimization (FSO) algorithm [14] can transmit the perceptual data with the least redundancy from the source node to the destination node, and proves that the FSO algorithm can minimize the transmission load of the wireless sensor network.…”

Section: ) Backgroundmentioning

confidence: 99%

“…These mechanisms of perceived data distribution in [13], [14] are based on the traditional structure of a wireless sensor network. In the process of data analysis and scheduling, each source node first needs to detect the resource state of other sensor nodes, and the source node obtains the idle bandwidth of the transmission link and the residual energy of relay nodes before the transmission of perceptual data [15].…”

Section: ) Backgroundmentioning

confidence: 99%

“…The EEPDB algorithm in [13] mainly considers the effective energy allocation in the process of data analysis and transmission, but the processing efficiency of effective data is very low. Although the energy allocation of the network reaches a relatively better value, the effective bandwidth cannot be utilized efficiently.…”

Section: ) Motivationmentioning

confidence: 99%

“…We can obtain Equation 130 ≤ E mj ≤ E mj , ∀j ∈ J. (13) where E mj and E mj are the energy test statistics and the energy request for satisfying the required data transmission of relay node m j , respectively. The detected energy sum of all wireless sensors shall be greater…”

Section: A Available Node Judgement Standardmentioning

confidence: 99%

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Data Distribution Optimization Strategy in Wireless Sensor Networks With Edge Computing

Song

2020

IEEE Access

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Resource-constrained wireless sensor networks (WSNs) impose a substantial challenge to the transmission and processing of non-redundant perceptual data. Moreover, breakthroughs in this area have opened up a new dimension that integrates edge computing technology in wireless sensor networks. However, achieving reliable data distribution in wireless sensor networks with edge computing (EWSN) and balancing the link bandwidth and node energy resources have become challenging problems. In this article, we propose a joint bandwidth allocation and energy consumption (JBAEC) algorithm for distributing different perceptual data to different sensor nodes of the EWSN by considering the available transmission links between the source sensor and destination nodes under the condition of early perceived energy. Therefore, we first establish the model of different data packet types and energy detection costs. Second, we describe the problem of effective bandwidth allocation and prove its NP-hard feature. In addition, we formulate the bandwidth allocation problem as an optimization problem that is divided into the two states of sufficient energy and lack of energy. Afterwards, we present an available node selection (ANS) algorithm for sorting the available relay nodes under the condition of different energy-sufficient nodes. The effective transmission network construction (ETNC) algorithm selects the available transmission links based on the set of available nodes. Finally, we implement the JBAEC algorithm by using the NS-2 simulator and present extensive simulation results to verify the data distribution efficiency of the JBAEC algorithm in the EWSN. INDEX TERMS Available node selection (ANS) algorithm, data packet type, edge wireless sensor network (EWSN), effective bandwidth allocation, joint bandwidth allocation and energy consumption (JBAEC) algorithm

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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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Energy-Efficient Depth Based Probabilistic Routing Within 2-Hop Neighborhood for Underwater Sensor Networks

Cited by 42 publications

References 13 publications

Underwater Pragmatic Routing Approach Through Packet Reverberation Mechanism

Underwater Pragmatic Routing Approach Through Packet Reverberation Mechanism

Data Distribution Optimization Strategy in Wireless Sensor Networks With Edge Computing

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

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