Reinforcement learning based energy efficient protocol for wireless multimedia sensor networks

Joshi, Upasna; Kumar, Rajiv

doi:10.1007/s11042-021-11387-w

Cited by 4 publications

(4 citation statements)

References 32 publications

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“…It is found to be useful in jamming attacks. Different WBAN techniques along with the node selection approaches have been discussed for achieving efficient energy [ 17 , 32 , 37 , 40 ]. In 2022, Ashraf et al [ 3 ] discussed regarding the sensor in WBAN.…”

Section: Literature Reviewmentioning

confidence: 99%

LTE-NBP with holistic UWB-WBAN approach for the energy efficient biomedical application

Kumar

Rathore²,

Dubey

et al. 2023

Multimed Tools Appl

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Increased use of ultra-wideband (UWB) in biomedical applications based on wireless body area networks (WBAN) opens a variety of options in the field of biomedical research. WBAN may aid in the continuous health monitoring of patients while they go about their everyday lives. Many studies and researchers were conducted several experimentations in the same field for the performance improvement. This study covered the hybridization of UWB technology, as well as on-body, off-body, and human-body ultra-wideband communication (HB-UWB). In this paper, the parameters considered are throughput, energy consumption, energy efficiency, energy used, network survival and delay. An improved model for design and assessment of power-saving UWB-WBAN was developed in this paper. A novel protocol model was introduced in this paper, namely low-power traffic-aware emergency based narrowband protocol (LTE-NBP) to overcome the major drawbacks of emergency, critical data transmission, reliability and the power issues in UWB-WBAN. It’s the emergency-based low-power traffic-aware narrowband protocol. It is based on the dual-band physical layer technology. The suggested protocol considered an aware traffic model and an emergency medium access control (MAC) protocol. The proposed model’s performance was evaluated and compared with the related algorithms on different performance parameters. The improved model is found to be efficient in throughput, energy efficiency, energy consumption, and delay.

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Section: Literature Reviewmentioning

confidence: 99%

LTE-NBP with holistic UWB-WBAN approach for the energy efficient biomedical application

Kumar

Rathore²,

Dubey

et al. 2023

Multimed Tools Appl

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“…Priority detection unit selected the more energetic routes according to the importance of node characteristics, including liveliness, path delay and hop count. Joshi et al 28 devised an RL‐based energy‐aware protocol to improve the network lifetime of WMSNs. A Markov decision process was utilized to make energy‐efficient routing decisions.…”

Section: Literature Surveymentioning

confidence: 99%

Energy aware optimal routing model for wireless multimedia sensor networks using modified Voronoi assisted prioritized double deep Q‐learning

Suseela,

Krithiga,

Revathi

et al. 2023

Concurrency and Computation

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SummaryThe energy hole problem is critical in Wireless Multimedia Sensor Network (WMSN), wherein the nodes closer to the sink node will expire sooner than the outer sub‐regions. Because they transmit their packet and forward the outer sub‐regions packet to the sink. Hence, a hole arises near the sink node after a very short time. Thus, an energy‐aware optimal routing model is introduced in this research using Modified Voronoi‐assisted prioritized double deep Q‐learning (PDDQL). Initially, the sensor nodes are deployed using the Voronoi cell structure that splits the entire region of interest into different Voronoi polygons. However, the Voronoi diagram will not always predict an efficient route because of the nodes at a Voronoi edge or vertex outside the transmission range. To tackle this issue, a new Voronoi diagram is proposed that depends on the maximum horizontal transmission range to remove the portion of Voronoi edges uncovered by the sensor node. After the deployment, a PDDQL algorithm is used to select the Relay Nodes based on their remaining energy. The proposed PDDQL determines an alternate route to transmit the packet if an energy hole problem has occurred in WMSN. The analysis of a proposed routing protocol based on the assessment measures like packet delivery ratio, average residual energy, number of alive nodes, and average end‐to‐end delay accomplished the value of 198.33 (ms), 2.05 (J), 100, and 0.85, respectively.

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“…Ding et al provided a comprehensive survey and proposed a model based on machine learning to create an energy efficient green computing model [19]. An RL-based energy efficient routing protocol for WMSN was proposed in [20] that in particular used state-action-reward-state-action algorithm to achieve convergence. A cluster-based routing protocol was proposed in [21] that used a combination of metaheuristics and machine learning.…”

Section: Related Workmentioning

confidence: 99%

A Novel Data Aggregation Mechanism using Reinforcement Learning for Cluster Heads in Wireless Multimedia Sensor Networks

Uddin¹

2022

AETiC

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Wireless multimedia sensor networks (WMSNs) are getting used in numerous applications nowadays. Many robust energy-efficient routing protocols have been proposed to handle multimedia traffic-intensive data like images and videos in WMSNs. It is a common trend in the literature to facilitate a WMSN with numerous sinks allowing cluster heads (CHs) to distribute the collected data to the adjacent sink node for delivery overhead mitigation. Using multiple sink nodes can be expensive and may incur high complexity in routing. There are many single-sink cluster-based routing protocols for WMSNs that lack in introducing optimal path selection among CHs. As a result, they suffer from transmission and queueing delay due to high data volume. To address these two conflicting issues, we propose a data aggregation mechanism based on reinforcement learning (RL) for CHs (RL-CH) in WMSN. The proposed method can be integrated to any of the cluster-based routing protocol for intelligent data transmission to sink node via cooperative CHs. Proposed RL-CH protocol performs better in terms of energy-efficiency, end-to-end delay, packet delivery ratio, and network lifetime. It gains 17.6% decrease in average end-to-end delay and 7.7% increase in PDR along with a network lifetime increased to 3.2% compared to the evolutionary game-based routing protocol which has been used as baseline.

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Reinforcement learning based energy efficient protocol for wireless multimedia sensor networks

Cited by 4 publications

References 32 publications

LTE-NBP with holistic UWB-WBAN approach for the energy efficient biomedical application

LTE-NBP with holistic UWB-WBAN approach for the energy efficient biomedical application

Energy aware optimal routing model for wireless multimedia sensor networks using modified Voronoi assisted prioritized double deep Q‐learning

A Novel Data Aggregation Mechanism using Reinforcement Learning for Cluster Heads in Wireless Multimedia Sensor Networks

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