Approximation Algorithms for Charging Reward Maximization in Rechargeable Sensor Networks via a Mobile Charger

Liang, Weifa; Xu, Zichuan; Xu, Wenzheng; Shi, Jiao; Mao, Guoqiang; Das, Sajal K.

doi:10.1109/tnet.2017.2723605

Cited by 114 publications

(24 citation statements)

References 26 publications

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“…The sensor nodes has scheduled for efficiently observing the operational quality in the network. Approximation model based on constant approximation rates for charging efficiency using mobile chargers could be enhanced [16].…”

Section: Related Workmentioning

confidence: 99%

LBCSM: Load Balanced Constant Scheduling Model for Node Recharging in Wireless Rechargeable Sensor Networks using Multiple Mobile Charging Devices

Sakthidharan¹,

Saravanan²,

Reddy³

et al. 2019

IJITEE

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In large-scale Wireless Rechargeable Sensor Networks (WRSNs), limited battery capacity of nodes may reduce the network longevity. For enhancing the network lifetime, the nodes in the network can recharged periodically based on their operational executions. The rechargeable sensor nodes in the network are replenished using external sources. Using single charging device can be feasible only for small scale WSNs, whereas in managing large scale wireless sensor networks, multiple charging devices are to be modelled for efficiently recharging the sensor nodes, since single devices are having energy constraints to recharge more number of nodes. On focussing those issues, this paper contributes on developing a new model called Load Balanced Constant Scheduling (LBCS) for the replenishment of the sensor nodes. Moreover, multiple Mobile Charging Devices (MCDs) are used here for recharging the sensor nodes effectively, without facing resource limitations. In this model, constant and time based charge scheduling approach and charging route for MCD has been frame optimally. The scheduling mode focuses on a concrete classification procedure for avoiding needless visits of nodes having adequate energy. Providing further improvement in schedule based node replenishment, algorithm for Charging Route Definition (CRD) is also developed in this work. For evidencing the efficiency of the proposed model, the work is simulated and evaluated. The simulation results are compared with some existing models based on the network lifetime, time taken for recharge and efficiency.

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Section: Related Workmentioning

confidence: 99%

LBCSM: Load Balanced Constant Scheduling Model for Node Recharging in Wireless Rechargeable Sensor Networks using Multiple Mobile Charging Devices

Sakthidharan¹,

Saravanan²,

Reddy³

et al. 2019

IJITEE

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“…The employment of mobile charging vehicles is a promising technique to replenish sensor energy, and the vehicle scheduling for prolonging network lifetimes has been studied in literature. Most of these studies assumed that every sensor is powered with a low-cost off-the-shelf battery [ 17 , 18 , 19 , 20 , 21 , 23 , 38 , 39 , 40 ], e.g., Lithium battery, where the price of such a battery is about only a few dollars [ 31 ]. It, however, usually takes some non-trivial time to fully charge such a battery, e.g., 30–80 min [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

“…Some studies employed only one charging vehicle to charge sensors in a sensor network [ 17 , 18 , 19 , 20 , 23 ]. He et al [ 17 ] proposed a Nearest-Job-Next with Preemption discipline to increase the average number of charged sensors per unit time and shorten their charging latencies.…”

Section: Related Workmentioning

confidence: 99%

“…He et al [ 17 ] proposed a Nearest-Job-Next with Preemption discipline to increase the average number of charged sensors per unit time and shorten their charging latencies. Liang et al [ 20 ] proposed approximation algorithms to maximize the total utility of charging sensors in a charging tour, subject to the energy capacity of a charging vehicle. Lin et al [ 18 ] devised a temporal-spatial charging scheduling algorithm to minimize the number of dead sensors and maximize energy efficiency.…”

Section: Related Workmentioning

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%

See 2 more Smart Citations

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

Guo

et al. 2018

Sensors

Self Cite

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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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An efficient on‐demand charging scheduling scheme for mobile charging vehicle

Zhong

Gao

et al. 2021

Int J Communication

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Summary Shared transportation is a new way of traveling generated by the rapid development of the “Internet +” and the sharing economy. Due to the limited energy carried by shared e‐bikes, the energy problem becomes the primary obstacle restricting its development. At present, one way to solve the energy problem is to use mobile charging vehicles (MCVs). However, how to schedule MCVs to achieve fast response, reduce energy consumption, and improve charging efficiency is crucial. In this paper, we propose a Charging Scheduling scheme based on Dynamic Characteristic of shared e‐bike in Obstacle Space (CSDC‐OS). Firstly, we design two MCV departure mechanisms to solve the problems of low utilization of MCVs and long waiting time for shared e‐bikes. Then, we design the schedulability conditions based on the A‐star algorithm and the dynamic change of e‐bikes to ensure that MCVs can return to the service station in time. Finally, we propose a charging sequence algorithm based on linear skyline query and a selective insertion algorithm to improve the charging efficiency of MCVs. Simulation results show that the CSDC‐OS can improve the response time, mobile consumption, and charging efficiency by more than 8% on average.

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Approximation Algorithms for Charging Reward Maximization in Rechargeable Sensor Networks via a Mobile Charger

Cited by 114 publications

References 26 publications

LBCSM: Load Balanced Constant Scheduling Model for Node Recharging in Wireless Rechargeable Sensor Networks using Multiple Mobile Charging Devices

LBCSM: Load Balanced Constant Scheduling Model for Node Recharging in Wireless Rechargeable Sensor Networks using Multiple Mobile Charging Devices

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

An efficient on‐demand charging scheduling scheme for mobile charging vehicle

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