Novel Learning Algorithms for Efficient Mobile Sink Data Collection Using Reinforcement Learning in Wireless Sensor Network

Soni, Santosh; Shrivastava, Manish

doi:10.1155/2018/7560167

Cited by 23 publications

(14 citation statements)

References 34 publications

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“…The drawback of the proposed solution is that it has a high probability of packet loss. Soni et Shrivastava [90] have picked up nodes in cluster sets using Q-learning and then collect data from cluster heads using a mobile sink. This solution has a double advantage: first, clustering saves the energy consumption of the nodes by reducing the number of hops and distance to the cluster head.…”

Section: Energymentioning

confidence: 99%

Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey

Frikha

Gammar

Lahmadi

et al. 2021

Computer Communications

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Section: Energymentioning

confidence: 99%

Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey

Frikha

Gammar

Lahmadi

et al. 2021

Computer Communications

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“…A reinforcement learning based clustering algorithm (RLBCA) was developed in [14] to find CHs for collecting the data and send to sink node. But the clustering error was higher.…”

Section: Literature Surveymentioning

confidence: 99%

Ensembled Elbow and Bray-Curtis Fuzzy C-Means Clustering For Energy Efficient Data Aggregation in WSN

Kokilavani¹,

Nitesh²

2021

IRJASH

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Wireless sensor network (WSN) comprises the distributed sensors for aggregating and organizing the data. Data aggregation is the major concern in WSN since it relies on several factors, namely energy constraints of sensors, network topology, links conditions and so on. The conventional approach does not perform efficient data aggregation due to their battery power of nodes and degrade the network lifetime. To improve data aggregation and network lifetime, An Energy-Efficient Ensembled Elbow Fuzzy C-means Clustering based Data Aggregation (EEEEFCC-DA) method is designed. Initially, residual energy of each sensor node (SN) is calculated. To determine the number of clusters, the elbow method is used in fuzzy c-means clustering algorithm. Then, Centroids value is calculated for every cluster to group SNs. Bray-Curtis Similarity Index is used to compute the similarity between the SN and Centroids value of cluster. SNs are grouped depends on the similarity value. The process gets iterated until every SNs gets clustered to the suitable clusters. After that, the SN with higher residual energy is selected as cluster head (CH). CH gathers data from each SNs and send to sink node. This, assist to enhance the data gathering accuracy and lessen the energy consumption. Simulation of EEEEFCC-DA method is carried out with various metrics namely energy consumption, network lifetime, data aggregation accuracy (DAA) and data aggregation time with number of SNs and number of data packets (DP). Results show that EEEEFCC-DA method provides better performance in term of DAA , network lifetime , energy consumption and data aggregation time than the conventional methods.

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“…The algorithm failed to transmit the data packet with minimum delay. An on-demand mobile sink traversal (ODMST) procedure was designed [20] to aggregate the information from CHs through the mobile sink. The convergence of procedures in large scale of WSN was not attained in an effective manner.…”

Section: Related Workmentioning

confidence: 99%

Energy efficient chaotic whale optimization technique for data gathering in wireless sensor network

Sridhar¹,

Guruprasad²

2020

IJECE

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A Wireless Sensor Network includes the distributed sensor nodes using limited energy, to monitor the physical environments and forward to the sink node. Energy is the major resource in WSN for increasing the network lifetime. Several works have been done in this field but the energy efficient data gathering is still not improved. In order to amend the data gathering with minimal energy consumption, an efficient technique called chaotic whale metaheuristic energy optimized data gathering (CWMEODG) is introduced. The mathematical model called Chaotic tent map is applied to the parameters used in the CWMEODG technique for finding the global optimum solution and fast convergence rate. Simulation of the proposed CWMEODG technique is performed with different parameters such as energy consumption, data packet delivery ratio, data packet loss ratio and delay with deference to dedicated quantity of sensor nodes and number of packets. The consequences discussion shows that the CWMEODG technique progresses the data gathering and network lifetime with minimum delay as well as packet loss than the state-of-the-art methods.

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Novel Learning Algorithms for Efficient Mobile Sink Data Collection Using Reinforcement Learning in Wireless Sensor Network

Cited by 23 publications

References 34 publications

Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey

Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey

Ensembled Elbow and Bray-Curtis Fuzzy C-Means Clustering For Energy Efficient Data Aggregation in WSN

Energy efficient chaotic whale optimization technique for data gathering in wireless sensor network

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