Energy- and Delay-Efficient Algorithm for Large-Scale Data Collection in Mobile-Sink WSNs

Azar, Sahebeh; Avokh, Avid; Abouei, Jamshid; Plataniotis, Konstantinos N.

doi:10.1109/jsen.2022.3152180

Cited by 10 publications

(5 citation statements)

References 41 publications

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“…The formula for the received energy calculation is represented by (7), which is equal to the product of the size of the received packet multiplied by E elec , it is independent of the distance between the nodes.…”

Section: Energy Calculation Modelmentioning

confidence: 99%

“…The network energy imbalance creates a hotspot problem, which leads to network holes causing the whole network performance to go down [ 6 ]. An effective way to solve the energy imbalance in the network is to use the mobile sink [ 7 ], mobile sink can realize the energy balance of the network by changing the hotspot nodes around it through moving periodically. However, after using the mobile sink, the nodes in the network need to keep updating the new position of the sink for proper data transmission; flooding to notify all sensor nodes is not energy-efficient.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN

Wang,

Zhu,

Wang

et al. 2024

Sensors

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Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm.

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Section: Energy Calculation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN

Wang,

Zhu,

Wang

et al. 2024

Sensors

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show abstract

“…However, in this paper, we proposed a novel Q-learning approach with a reward function to select a set of CHs that minimised the BS tour. Partitioning of the network into smaller sub-areas and determining the trajectory for the corresponding sink is proposed in [21]. This work examined data collection delays due to the move and stop of the mobile BS for collecting and aggregating the received data from the CHs.…”

Section: Related Workmentioning

confidence: 99%

“…In general, using multiple mobile sinks can reduce the number of hops. The proposed algorithms in [21], [22], and [23] didn't focus on the travel time of the sensing data to reach the CH, where the sensing data is collected with unbounded hop count between the CH and cluster sensor nodes which is one of the requirements of the proposed algorithm in this paper.…”

Section: Related Workmentioning

confidence: 99%

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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“…data storage unit, one or more sensors, and a battery stored power unit. Additionally, depending on the application, WSN nodes could contain other components like a GPS, an electrostatic power generator, and a mobilizer [3]- [5] to move the nodes as required. The components of the WSN node are shown in Fig.…”

mentioning

confidence: 99%

Coverage Area Maximization Using MOFAC-GA-PSO Hybrid Algorithm in Energy Efficient WSN Design

Kumar De,

Banerjee,

Majumder

et al. 2023

IEEE Access

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Coverage area optimization is always a challenging task to configure an efficient Wireless Sensor Network (WSN). This article proposes an energy-efficient coverage area optimization technique of WSN using a novel hybrid algorithm, called MOFAC-GA-PSO (Minimum Overlapped Full Area Coverage using hybridized Genetic Algorithm-Particle Swarm Optimization) algorithm. The objectives of the article are maximization of coverage area, minimization of coverage hole as well as energy requirement. The above-mentioned three objectives had not been yet addressed combinedly with the existing literature. This limitation has been addressed in the proposed work with 100% area coverage. The result of the proposed algorithm is compared with the existing literature as well as with the individual meta-heuristic algorithms (i.e., GA and PSO) to prove the competence of the MOFAC-GA-PSO algorithm. To achieve the benefits of both optimizers, the GA was treated as a global optimizer while the PSO was treated as a local optimizer. The proposed research work achieves 100 percent area coverage with just 25 mobile WSN nodes, but the existing methodology can only provide a maximum of 91.26 percent of area coverage. In terms of energy efficiency, the network built by the proposed algorithm can last 11.06 days as contrasted to the performance of the existing paper, which is 6.33 days. So, a significant improvement concerning the maximization of coverage area as well as minimization of coverage hole, and energy requirement has been observed. Last, but not the least, a statistical analysis is carried out to justify the research for the required number of optimized WSN nodes. INDEX TERMSCoverage area optimization, Energy efficient WSN, Hybrid algorithm, LMCF(Least Movement Consider First), Hexagonal structure, MOFAC-GA-PSO I. INTRODUCTION A Wireless Sensor Node is a battery powered small device with limited computational, transmission and energy capacity. The situation where standard wireless communications networks are difficult or impossible to deploy, the Wireless Sensor Nodes can be deployed to form a collaborative and clustered Wireless Sensor Network(WSN) [1]. Depending on the sensors affixed to the WSN nodes, these WSN nodes collect a variety of physical measurements from their surroundings, including noise intensity, air flow velocity, pollution level, pressure level, temperature, moisture, and surveillance data. After transforming the environmentally acquired data into electrical signals, the WSN nodes send and receive a limited amount of data to other nodes or sink nodes for further processing. The structure of the WSN network is depicted in Fig. 1.Generally, the standard components of a Wireless Sensor Node include a micro-controller unit, a transceiver unit, a

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Energy- and Delay-Efficient Algorithm for Large-Scale Data Collection in Mobile-Sink WSNs

Cited by 10 publications

References 41 publications

Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN

Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN

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

Coverage Area Maximization Using MOFAC-GA-PSO Hybrid Algorithm in Energy Efficient WSN Design

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