Maximizing Sensor Lifetime with the Minimal Service Cost of a Mobile Charger in Wireless Sensor Networks

Xu, Wenzheng; Liang, Weifa; Jia, Xiaohua; Xu, Zichuan; Li, Zheng; Liu, Yiguang

doi:10.1109/tmc.2018.2813376

Cited by 105 publications

(48 citation statements)

References 23 publications

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“…The existing wireless energy transfer can be divided into Single-Input Single-Output energy transfer model [23,24,25,26,27,28,29,30,31,32,33,34] and Single-Input Multiple-Output energy transfer model [13,14,15,16,17,31,32,33,34,35]. Energy transfer optimization problems can be divided into static charging stations’ deployment [11,18,19,20,35,36,37,38] and mobile charging vehicles’ dispatching problems [13,14,15,16,17,23,24,25,26,27,28,29,30,31,32,33,34].…”

Section: Related Workmentioning

confidence: 99%

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Optimizing Charging Efficiency and Maintaining Sensor Network Perpetually in Mobile Directional Charging

Chen

Cheng

2019

Sensors

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Wireless Power Transfer (WPT) is a promising technology to replenish energy of sensors in Rechargeable Wireless Sensor Networks (RWSN). In this paper, we investigate the mobile directional charging optimization problem in RWSN. Our problem is how to plan the moving path and charging direction of the Directional Charging Vehicle (DCV) in the 2D plane to replenish energy for RWSN. The objective is to optimize energy charging efficiency of the DCV while maintaining the sensor network working continuously. To the best of our knowledge, this is the first work to study the mobile directional charging problem in RWSN. We prove that the problem is NP-hard. Firstly, the coverage utility of the DCV’s directional charging is proposed. Then we design an approximation algorithm to determine the docking spots and their charging orientations while minimizing the number of the DCV’s docking spots and maximizing the charging coverage utility. Finally, we propose a moving path planning algorithm for the DCV’s mobile charging to optimize the DCV’s energy charging efficiency while ensuring the networks working continuously. We theoretically analyze the DCV’s charging service capability, and perform the comprehensive simulation experiments. The experiment results show the energy efficiency of the DCV is higher than the omnidirectional charging model in the sparse networks.

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Section: Related Workmentioning

confidence: 99%

“…Energy transfer optimization problems can be divided into static charging stations’ deployment [11,18,19,20,35,36,37,38] and mobile charging vehicles’ dispatching problems [13,14,15,16,17,23,24,25,26,27,28,29,30,31,32,33,34]. …”

Section: Related Workmentioning

confidence: 99%

Optimizing Charging Efficiency and Maintaining Sensor Network Perpetually in Mobile Directional Charging

Chen

Cheng

2019

Sensors

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“…Assume that the charging time of each lifetime-critical off-the-shelf sensor in

is a constant

[ 37 ], e.g.,

= 1 h, since its residual energy is very low, where

is the energy capacity of the sensor, and

is the off-the-shelf sensor charging rate. We further assume that the traveling time among two consecutive visited sensors can be considered as a small constant

, e.g.,

min, as the vehicle traveling time usually is much shorter than the charging time

of a lifetime-critical off-the-shelf sensor in

, e.g., 1 min vs. 1 h [ 23 ]. Let

.…”

Section: Algorithm For the Dead Duration Minimization Problemmentioning

confidence: 99%

“…Figure 7 also shows that the longest dead duration by each of the six algorithms only slightly decreases with a faster vehicle traveling speed. The improvement is only slight, since the sensor charging time is much longer than the vehicle traveling time [ 23 , 26 ].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…A lot of attention has been paid to the vehicle charging scheduling for wireless sensor networks. Most of these studies assumed that sensors are powered with off-the-shelf batteries [ 15 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], such as Lithium batteries, we here abbreviate an off-the-shelf battery powered sensors by an off-the-shelf sensor. The price of such a battery is only about a few dollars [ 26 ]; however, it usually takes some non-trivial time to fully charge one, e.g., 30–80 min [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Towards Low-Cost Yet High-Performance Sensor Networks by Deploying a Few Ultra-fast Charging Battery Powered Sensors

Guo

et al. 2018

Sensors

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The employment of mobile vehicles to charge sensors via wireless energy transfer is a promising technology to maintain the perpetual operation of wireless sensor networks (WSNs). Most existing studies assumed that sensors are powered with off-the-shelf batteries, e.g., Lithium batteries, which are cheap, but it takes some non-trivial time to fully charge such a battery (e.g., 30–80 min). The long charging time may incur long sensor dead durations, especially when there are many lifetime-critical sensors to be charged. On the other hand, other studies assumed that every sensor is powered with an ultra-fast charging battery, where it only takes some trivial time to replenish such a battery, e.g., 1 min, but the adoption of many ultra-fast sensors will bring about high purchasing cost. In this paper, we propose a novel heterogeneous sensor network model, in which there are only a few ultra-fast sensors and many low-cost off-the-shelf sensors. The deployment cost of the network in the model is low, as the number of ultra-fast sensors is limited. We also have an important observation that we can significantly shorten sensor dead durations by enabling the ultra-fast sensors to relay more data for lifetime-critical off-the-shelf sensors. We then propose a joint charging scheduling and routing allocation algorithm, such that the longest sensor dead duration is minimized. We finally evaluate the performance of the proposed algorithm through extensive simulation experiments. Experimental results show that the proposed algorithm is very promising and the longest sensor dead duration by it is only about 10% of those by existing algorithms.

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Charging scheduling strategy for linear wireless power transfer network with external power injection

Yang

Jiang

et al. 2021

Circuit Theory & Apps

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Summary Wireless power transfer network (WPTN) can provide efficient, timely, and dynamic charging service for multiple devices wirelessly by adopting mobile chargers. However, there are still urgent requirements to reduce the cost and improve the charging performance of WPTN. Therefore, this paper proposes the charging scheduling strategy for linear WPTN in which devices are distributed linearly or approximately linearly. An improved energy consumption model of the WPTN node is discussed firstly. Then, considering the charging capability of the wireless power transfer module, a charging threshold is selected to improve energy use efficiency. Moreover, the regional charging of mobile chargers is realized, so the distribution of carried energy on mobile chargers could be balanced and the maximum required energy that a single mobile charger carried could be reduced. Meanwhile, the processes of charging scheduling and energy delivery are analyzed in detail. Finally, simulation analysis and experimental data verify the effectiveness of the proposed charging scheduling strategies.

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Maximizing Sensor Lifetime with the Minimal Service Cost of a Mobile Charger in Wireless Sensor Networks

Cited by 105 publications

References 23 publications

Optimizing Charging Efficiency and Maintaining Sensor Network Perpetually in Mobile Directional Charging

Optimizing Charging Efficiency and Maintaining Sensor Network Perpetually in Mobile Directional Charging

Towards Low-Cost Yet High-Performance Sensor Networks by Deploying a Few Ultra-fast Charging Battery Powered Sensors

Charging scheduling strategy for linear wireless power transfer network with external power injection

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