Joint Sensor Selection and Energy Allocation for Tasks-Driven Mobile Charging in Wireless Rechargeable Sensor Networks

Wu, Tao; Yang, Panlong; Dai, Haipeng; Xiang, Chaocan; Rao, Xunpeng; Huang, Jun; Ma, Tao

doi:10.1109/jiot.2020.3019451

Cited by 29 publications

(7 citation statements)

References 51 publications

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“…IoT relies on various types of sensors embedded in football shoes, players' bodies, or other equipment to real-time sense and collect data on players' activities [27,28]. Common types of sensors include accelerometer, gyroscope, heart rate, and others, as shown in Figure 1.…”

Section: A the Application Of Iot Technology In Football Sports Analysismentioning

confidence: 99%

The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning

Mou

2024

IEEE Access

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This work proposes a novel Class Aware Network (CANet) for analyzing football player performance by decoding their body movements. Firstly, the role of the Internet of Things in football sports analysis and the advantages of deep learning techniques are introduced. Secondly, pyramid pooling modules and attention mechanisms are introduced. Moreover, the Group-split-bottleneck (GS-bt) module is employed, and the CANet is designed to extract and utilize multi-scale feature information and enhance the network's ability to perceive details. Finally, the effectiveness of the proposed model is validated through comparisons with other models. The results show that in image classification experiments, the mean accuracy of the GSbt module is at least 2.79% higher than that of other models. In human body parsing experiments, results from two different datasets demonstrate that the CANet model achieves the highest mean Intersection over Union, improving by at least 6.02% compared to other models. These findings indicate that the proposed CANet model performs better in image classification and human body parsing tasks, presenting higher accuracy and generalization capabilities. This work provides new methods and technologies for analyzing football player performance, potentially promoting sports development and application in athletics.

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Section: A the Application Of Iot Technology In Football Sports Analysismentioning

confidence: 99%

The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning

Mou

2024

IEEE Access

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“…In [41], an optimization problem was created in which the WCV departure time and the overall cycle time were maximized in order to address the charging problem in a WRSN. To address the problem associated with sensor selection and energy allocation, the authors in [42] proposed an algorithm that maximized a monotonous submodular function, which was subjected to a general routing constraint while achieving a higher approximation ratio in WRSNs.…”

Section: Optimizationsmentioning

confidence: 99%

On Increasing the Energy Efficiency of Wireless Rechargeable Sensor Networks for Cyber-Physical Systems

Orumwense¹,

Abo-Al-Ez²

2022

Energies

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In recent times, wireless energy transfer has become an effective solution to charge devices due to its efficiency and reliability. In a typical Wireless Rechargeable Sensor Networks (WRSN), wireless energy transfer technique can solve the energy depletion problem with the aid of a Wireless Charging Vehicle (WCV), thereby enabling the network to extend its lifetime. However, sensor nodes in a WRSN still have their energies depleted before it gets replenished by the WCV. In this paper, we proposed a scheme that prioritizes sensor nodes for charging and also developed efficient algorithms to improve on existing charging schemes so as to extend the lifetime of the WRSN. Firstly, an inspection algorithm was developed to visit and inspect sensor nodes in the network so as to determine the sensor nodes to charge. Secondly, a greedy charge algorithm was introduced to ascertain the shortest distance the WCV needs to travel and, lastly, an energy for nodes’ algorithm was proposed to determine the stopping point and when the WCV needs to return to the base station. Simulation experiments were also conducted to determine the performance of our scheme. The simulation experiments revealed that our proposed scheme made significant improvements when compared to other schemes in literature using several metrics.

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“…Mo et al proposed a novel multiple mobile chargers coordination framework for a wireless rechargeable sensor network, where the scheduling, the moving time, and the charging time of the mobile chargers were jointly considered [11]. In order to maximize the overall task utility concerning sensor selection and task cooperation for wireless rechargeable sensor networks, Wu et al proposed a novel energy allocation scheme with a specific theoretical analysis of the submodularity and gap property for the surrogate [12]. In a large-scale rechargeable wireless sensor network, Sha et al assigned the sensor nodes into groups according to their remaining lifetime and balanced the energy consumption among several mobile wireless chargers (MWCs) to maximize the utilization rate of the MWCs and reduce the charging delay [13].…”

Section: Related Workmentioning

confidence: 99%

“…However, the drawbacks are also obvious. The path planning and communication quality of ground mobile chargers are susceptible to the terrain, and therefore it is difficult to replenish energy efficiently and to do so in a timely way [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

Qin

Zhao

et al. 2021

Remote Sensing

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Power consumption in wireless sensor networks is high, and the lifetime of a battery has become a bottleneck, restricting network performance. Wireless power transfer with a ground mobile charger is vulnerable to interference from the terrain and other factors, and hence it is difficult to deploy in practice. Accordingly, a novel paradigm is adopted where a multi-UAV (unmanned aerial vehicle) with batteries can transfer power and information to SDs (sensor devices) in a large-scale sensor network. However, there are discrete events, continuous process, time delay, and decisions in such a complicated system. From the perspective of a hybrid system, a hybrid colored cyber Petri net system is proposed here to depict and analyze this problem. Furthermore, the energy utilization rate and information collection time delay are conflict with each other; therefore, UAV-aided wireless power and information transfer is formulated as a multi-objective optimization problem. For this reason, the MAC-NSGA II (multiple ant colony-nondominated sorting genetic algorithm II) is proposed in this work. Firstly, the optimal trajectory of multiple UAVs was obtained, and on this basis, the above two objectives were optimized simultaneously. Large-scale simulation results show that the proposed algorithm is superior to NSGA II and MOEA/D in terms of energy efficiency and information collection delay.

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Joint Sensor Selection and Energy Allocation for Tasks-Driven Mobile Charging in Wireless Rechargeable Sensor Networks

Cited by 29 publications

References 51 publications

The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning

The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning

On Increasing the Energy Efficiency of Wireless Rechargeable Sensor Networks for Cyber-Physical Systems

Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

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