Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides apromising solution to this problem, which is not easily affected by the external environment. In this paper, w...Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides apromising solution to this problem, which is not easily affected by the external environment. In this paper, we studythe recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers(MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with theobjective of maximizing the number of surviving sensors, and further propose a collaborative charging schedulingalgorithm(CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors.TwoMCs can adaptively collaboratively charge the sensors based on the energy limit ofMCs and the energy demandof sensors. Finally, we conducted comparative simulations. The simulation results show that the proposed algorithmcan effectively reduce the death rate of the sensor. The proposed algorithm provides a solution to the uncertaintyof node charging tasks and the collaborative challenges posed by multiple MCs in practical scenarios.展开更多
As the technological breakthrough is made in wireless charging, the wireless rechargeable sensor networks (WRSNs) are finally proposed. In order to reduce the charging completion time, most existing works use the “mo...As the technological breakthrough is made in wireless charging, the wireless rechargeable sensor networks (WRSNs) are finally proposed. In order to reduce the charging completion time, most existing works use the “mobilethen- charge” model—the Wireless charging vehicles (WCV) moves to the charging spot first and then charges nodes nearby. These works often aim to reduce the node’s movement delay or charging delay. However, the charging opportunities during the movement are overlooked in this model because WCV can charge nodes when it goes from one spot to the next. In order to use the charging opportunities, a speed grading method is proposed under the circumstance of variable WCV speed, which transformed the problem of final charging delay into a traveling salesman problem with speed grading. The problem was further solved by linear programming method. The simulation experiments show that, compared with the existing charging methods, the proposed method has a significant improvement in charging delay.展开更多
In large-scaleWireless Rechargeable SensorNetworks(WRSN),traditional forward routingmechanisms often lead to reduced energy efficiency.To address this issue,this paper proposes a WRSN node energy optimization algorith...In large-scaleWireless Rechargeable SensorNetworks(WRSN),traditional forward routingmechanisms often lead to reduced energy efficiency.To address this issue,this paper proposes a WRSN node energy optimization algorithm based on regional partitioning and inter-layer routing.The algorithm employs a dynamic clustering radius method and the K-means clustering algorithm to dynamically partition the WRSN area.Then,the cluster head nodes in the outermost layer select an appropriate layer from the next relay routing region and designate it as the relay layer for data transmission.Relay nodes are selected layer by layer,starting from the outermost cluster heads.Finally,the inter-layer routing mechanism is integrated with regional partitioning and clustering methods to develop the WRSN energy optimization algorithm.To further optimize the algorithm’s performance,we conduct parameter optimization experiments on the relay routing selection function,cluster head rotation energy threshold,and inter-layer relay structure selection,ensuring the best configurations for energy efficiency and network lifespan.Based on these optimizations,simulation results demonstrate that the proposed algorithm outperforms traditional forward routing,K-CHRA,and K-CLP algorithms in terms of node mortality rate and energy consumption,extending the number of rounds to 50%node death by 11.9%,19.3%,and 8.3%in a 500-node network,respectively.展开更多
近年来,无线能量传输技术(Wireless Power Transmission,WPT)快速发展.这促使在无线可充电传感器网络系统中可部署或调度充电器为可充电设备进行能量补充,以维持系统运行的持续性.基于此,研究者提出多种合作充电模型和相应的调度方法,...近年来,无线能量传输技术(Wireless Power Transmission,WPT)快速发展.这促使在无线可充电传感器网络系统中可部署或调度充电器为可充电设备进行能量补充,以维持系统运行的持续性.基于此,研究者提出多种合作充电模型和相应的调度方法,但是当前大部分部署方法仅考虑成本受限约束,而忽略了可充电设备可能具有空间占用的属性.因此,本文考虑了具有空间占用且充电成本受限的可移动传感器调度问题(Charging Cost-Constrained Scheduling,CCS).进一步地,本文以最大化充电效用为目的,提出了一个基于贪心的近似比为(1-1/e)的近似算法.大量仿真实验证明本文算法的优越性,该算法与传统算法对比充电效用提升30%,与粒子群算法对比充电效用提升5%.展开更多
新型无线可充电传感器网络中无线充电小车的充电调度算法研究,针对网络中传感器节点发出的充电请求,进行Revised Earliest Deadline First(REDF)无线充电调度优化算法设计。REDF算法综合考虑充电期限和节点距离两个制约因素,使得每一个...新型无线可充电传感器网络中无线充电小车的充电调度算法研究,针对网络中传感器节点发出的充电请求,进行Revised Earliest Deadline First(REDF)无线充电调度优化算法设计。REDF算法综合考虑充电期限和节点距离两个制约因素,使得每一个传感器的充电需求都能得到及时满足,并且无线充电小车还能够在较短的时间内完成充电工作,从而延长无线传感器网络生命周期,建立一个稳定供应能量的无线传感器网络。算法的性能仿真结果表明,REDF算法的性能要优于Earliest Due Date First(EDDF)。展开更多
针对无线可充电传感器网络中现有充电路径与充电时长联合调度的方法未充分考虑信用分配问题和时序依赖性而导致充电效率低、节点失效多的问题,提出了一种基于单调值函数分解的时空协同充电调度方法。首先研究了保证节点正常工作的充电...针对无线可充电传感器网络中现有充电路径与充电时长联合调度的方法未充分考虑信用分配问题和时序依赖性而导致充电效率低、节点失效多的问题,提出了一种基于单调值函数分解的时空协同充电调度方法。首先研究了保证节点正常工作的充电阈值及充电上限范围;其次简化了问题的动作空间;最后通过门控循环单元提取充电请求队列的时序特征,并通过基于单调值函数分解的多智能体深度强化学习方法得到充电路径与充电时长的联合调度策略。仿真实验表明,该方法与动态时空充电调度方法(a dynamic Spatiotemporal Charging Scheduling scheme based on Deep reinforcement learning,SCSD)、最近作业下一步抢占规则(Nearest-Job-Next with Preemption,NJNP)、先来先服务规则(First-Come-First-Serve,FCFS)相比,失效节点数减少了7.41%~21.88%,充电延迟减少了3.28%~10.94%,吞吐量增加了5.63%~49.3%。展开更多
基金Hubei Provincial Natural Science Foundation of China under Grant No.2017CKB893Wuhan Polytechnic University Reform Subsidy Project Grant No.03220153.
文摘Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides apromising solution to this problem, which is not easily affected by the external environment. In this paper, we studythe recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers(MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with theobjective of maximizing the number of surviving sensors, and further propose a collaborative charging schedulingalgorithm(CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors.TwoMCs can adaptively collaboratively charge the sensors based on the energy limit ofMCs and the energy demandof sensors. Finally, we conducted comparative simulations. The simulation results show that the proposed algorithmcan effectively reduce the death rate of the sensor. The proposed algorithm provides a solution to the uncertaintyof node charging tasks and the collaborative challenges posed by multiple MCs in practical scenarios.
文摘As the technological breakthrough is made in wireless charging, the wireless rechargeable sensor networks (WRSNs) are finally proposed. In order to reduce the charging completion time, most existing works use the “mobilethen- charge” model—the Wireless charging vehicles (WCV) moves to the charging spot first and then charges nodes nearby. These works often aim to reduce the node’s movement delay or charging delay. However, the charging opportunities during the movement are overlooked in this model because WCV can charge nodes when it goes from one spot to the next. In order to use the charging opportunities, a speed grading method is proposed under the circumstance of variable WCV speed, which transformed the problem of final charging delay into a traveling salesman problem with speed grading. The problem was further solved by linear programming method. The simulation experiments show that, compared with the existing charging methods, the proposed method has a significant improvement in charging delay.
基金funded by National Natural Science Foundation of China(No.61741303)Guangxi Natural Science Foundation(No.2017GXNSFAA198161)the Foundation Project of Guangxi Key Laboratory of Spatial Information and Mapping(No.21-238-21-16).
文摘In large-scaleWireless Rechargeable SensorNetworks(WRSN),traditional forward routingmechanisms often lead to reduced energy efficiency.To address this issue,this paper proposes a WRSN node energy optimization algorithm based on regional partitioning and inter-layer routing.The algorithm employs a dynamic clustering radius method and the K-means clustering algorithm to dynamically partition the WRSN area.Then,the cluster head nodes in the outermost layer select an appropriate layer from the next relay routing region and designate it as the relay layer for data transmission.Relay nodes are selected layer by layer,starting from the outermost cluster heads.Finally,the inter-layer routing mechanism is integrated with regional partitioning and clustering methods to develop the WRSN energy optimization algorithm.To further optimize the algorithm’s performance,we conduct parameter optimization experiments on the relay routing selection function,cluster head rotation energy threshold,and inter-layer relay structure selection,ensuring the best configurations for energy efficiency and network lifespan.Based on these optimizations,simulation results demonstrate that the proposed algorithm outperforms traditional forward routing,K-CHRA,and K-CLP algorithms in terms of node mortality rate and energy consumption,extending the number of rounds to 50%node death by 11.9%,19.3%,and 8.3%in a 500-node network,respectively.
文摘近年来,无线能量传输技术(Wireless Power Transmission,WPT)快速发展.这促使在无线可充电传感器网络系统中可部署或调度充电器为可充电设备进行能量补充,以维持系统运行的持续性.基于此,研究者提出多种合作充电模型和相应的调度方法,但是当前大部分部署方法仅考虑成本受限约束,而忽略了可充电设备可能具有空间占用的属性.因此,本文考虑了具有空间占用且充电成本受限的可移动传感器调度问题(Charging Cost-Constrained Scheduling,CCS).进一步地,本文以最大化充电效用为目的,提出了一个基于贪心的近似比为(1-1/e)的近似算法.大量仿真实验证明本文算法的优越性,该算法与传统算法对比充电效用提升30%,与粒子群算法对比充电效用提升5%.
文摘新型无线可充电传感器网络中无线充电小车的充电调度算法研究,针对网络中传感器节点发出的充电请求,进行Revised Earliest Deadline First(REDF)无线充电调度优化算法设计。REDF算法综合考虑充电期限和节点距离两个制约因素,使得每一个传感器的充电需求都能得到及时满足,并且无线充电小车还能够在较短的时间内完成充电工作,从而延长无线传感器网络生命周期,建立一个稳定供应能量的无线传感器网络。算法的性能仿真结果表明,REDF算法的性能要优于Earliest Due Date First(EDDF)。
文摘针对无线可充电传感器网络中现有充电路径与充电时长联合调度的方法未充分考虑信用分配问题和时序依赖性而导致充电效率低、节点失效多的问题,提出了一种基于单调值函数分解的时空协同充电调度方法。首先研究了保证节点正常工作的充电阈值及充电上限范围;其次简化了问题的动作空间;最后通过门控循环单元提取充电请求队列的时序特征,并通过基于单调值函数分解的多智能体深度强化学习方法得到充电路径与充电时长的联合调度策略。仿真实验表明,该方法与动态时空充电调度方法(a dynamic Spatiotemporal Charging Scheduling scheme based on Deep reinforcement learning,SCSD)、最近作业下一步抢占规则(Nearest-Job-Next with Preemption,NJNP)、先来先服务规则(First-Come-First-Serve,FCFS)相比,失效节点数减少了7.41%~21.88%,充电延迟减少了3.28%~10.94%,吞吐量增加了5.63%~49.3%。
