Delay Tolerance and Energy Saving in Wireless Sensor Networks with a Mobile Base Station

Jerew, Oday D.; Bassam, Nizar Al

doi:10.1155/2019/3929876

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…Merging clusters together reduces the number of CHs thereby reducing the length of the BS tour. Finally, the Load Balanced Algorithm (LBA) proposed in [33] is used to balance the number of nodes among the collection trees. The LBA improves the network lifetime by moving some nodes between collection trees and, in particular, those trees constructed at the beginning of the iteration process.…”

Section: A Bounded Hop Count-reinforcement Learning Algorithm (Bhc-rla)mentioning

confidence: 99%

“…In particular, the first constructed tree covers the maximum number of nodes, while the last collection tree covers only the remaining set of nodes. The Load Balanced Algorithm (LBA) proposed in [33] is used to balance the number of nodes in the CTs.…”

Section: F Load Balancing Algorithmmentioning

confidence: 99%

“…We also vary λ to evaluate the performance of the proposed algorithm. We then compare our algorithm with the FNF-BHA and AT-BHC algorithms proposed in [33]. For the AT-BHA, we set β = 0.5 and τ = 0.1.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Input: A sensor network G(V ; E), Set of CTs TC j,h , hop count h Output: set of balanced CTs BTC j,h , Remain Nodes PN 1 K ← number of CH nodes ; 2 N G ← V ;/* set of nodes in the network */ ; 3 N GB ← N G ; 4 BTC j,hc {j = 1 : K , hc = 1 : h} ← φ ; 5 Sort TC j trees in increasing order according to the number of nodes in each tree ; 6 v CH ← set of CHs in TC tree ; The pseudo-code of the Load Balanced Algorithm (LBA) [33].…”

Section: Algorithm 6 Ct Balanced Algorithm (Ctba)mentioning

confidence: 99%

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Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

2023

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Reinforcement Learning (RL) has emerged as a promising approach for improving the performance of Wireless Sensor Networks (WSNs). The Q-learning technique is one approach of RL in which the algorithm continuously learns by interacting with the environment, gathering information to take certain actions. It maximizes performance by determining the optimal result from that environment. In this paper, we propose a data gathering algorithm based on a Q-learning approach named Bounded Hop Count -Reinforcement Learning Algorithm (BHC-RLA). The proposed algorithm uses a reward function to select a set of Cluster Heads (CHs) to balance between the energy-saving and data-gathering latency of a mobile Base Station (BS). In particular, the proposed algorithm selects groups of CHs to receive sensing data of cluster nodes within a bounded hop count and forward the data to the mobile BS when it arrives. In addition, the CHs are selected to minimize the BS tour length. Extensive experiments by simulation were conducted to evaluate the performance of the proposed algorithm against another traditional heuristic algorithm. We demonstrate that the proposed algorithm outperforms the existing work in the mean of the length of a mobile BS tour and a network's lifetime.INDEX TERMS Wireless sensor networks, mobile data gathering, delay tolerance, relay hop count, mobile base station tour.

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Section: A Bounded Hop Count-reinforcement Learning Algorithm (Bhc-rla)mentioning

confidence: 99%

Section: F Load Balancing Algorithmmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Algorithm 6 Ct Balanced Algorithm (Ctba)mentioning

confidence: 99%

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Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

2023

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“…Jerew et al introduced the technique for attaining the balance between delay and energy conservation in sensor networks. 15 The balance between relay hop count and mobile station length was maintained. Only the selective packets were forwarded to the base station through collection trees.…”

Section: Literature Surveymentioning

confidence: 99%

An improved optimal energy aware data availability approach for secure clustering and routing in wireless sensor networks

Roberts¹,

Thangavel²

2022

Trans Emerging Tel Tech

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Maintaining high residual energy is one of the major challenges of wireless sensor networks (WSNs), where nodes are moving at a random speed. In addition, data availability is also a major concern that must be maintained constantly during the packet transmission phase. In this research work, a Residual‐Energy‐Based Data Availability Approach (REDAA) for WSNs is developed to increase the network lifetime by focusing on the selection of stable routing paths and cluster heads. During the first phase of the work, the network model, and assumptions for route creations are adopted to effectively initialize the data transmission phase. During the second phase, a cluster was formed, and two categories of cluster heads were created based on quadrature low energy adaptive clustering hierarchy (Q‐LEACH) and multi‐hop low energy adaptive clustering (MH‐LEACH) algorithms to formalize the route and to make data available whenever requested. During the third phase, energy conservation routes are initialized, and data gathering is improved through slot‐based code division multiple access schemes. Simulation results demonstrate that the proposed REDAA approach can achieve an improvement in terms of throughput and energy consumption by 37% and 70% respectively when compared to MH‐LEACH approach, whereas in comparison to Q‐LEACH approach, these improvements are found to be 30% and 73% respectively.

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Priority-Centered Virtual Groups and Mobile Sink for Wireless Sensor Networks

Gurusamy

Kedir

Emare

2020

Lecture Notes in Networks and Systems

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Delay Tolerance and Energy Saving in Wireless Sensor Networks with a Mobile Base Station

Cited by 11 publications

References 25 publications

Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

An improved optimal energy aware data availability approach for secure clustering and routing in wireless sensor networks

Priority-Centered Virtual Groups and Mobile Sink for Wireless Sensor Networks

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