一种射频能量捕获网络移动能量源均衡化充电策略

为解决传统电池供电传感器网络存在的电池不易更换、节点能量容易耗尽等问题,射频能量捕获技术已逐步应用于无线可充电传感器网络中.由于不同位置传感器节点的工作负荷不同,捕获能量也有差异,实现节点能量的均衡化分布可以有效地提高节点的存活率.考虑射频能量源移动充电的场景,在已知节点位置信息的条件下,设计合理均衡的路由方案和充电算法.首先将区域基于蜂窝六边形网格划分,分别对网格和节点分层,提出逐层传输的均衡式路由策略,然后给出无线充电小车的移动路径,对相邻两层内节点剩余能量的方差最小化问题建模,由内层向外层依次确定能量源在各停留点的充电时间.仿真结果表明,相比已有的均衡化充电方法,该策略可以明显提高节点剩余能量的均衡性,从而延长网络的生命周期.     

Prolonging lifetime for wireless rechargeable sensor networks through sleeping and charging scheduling

Prolonging network lifetime is a fundamental requirement in wireless sensor network (WSN). Existing charging scheduling algorithms suffer from high node redundancy and energy consumption issues. In this paper, we study WSN charging problem from the perspectives of energy conservation combined with energy replenishment scheduling. Firstly, we detect the redundant nodes whose energy is wasted in the network functionality and develop a K‐covering redundant nodes sleeping scheduling algorithm (KRSS) for reducing energy. Secondly, we employed multiple wireless charging vehicles (WCVs) for energy replenishment and optimize the charging scheduling algorithm to prevent any exhaustion of nodes, and we proposed a distance and energy–oriented charging scheduling algorithm (DECS) with multiple WCVs. Simulation experiments are conducted to show the advantages of the proposed KRSS+DECS, confirming that our scheme is capable of removing redundant nodes, lowering node failures, and prolonging network lifetime.     

Collaborative data gathering and recharging using multiple mobile vehicles in wireless rechargeable sensor network

Many cutting-edge studies on collaborative data gathering and charging (CDAC) assume that the mobile vehicle (MV) has enough energy to charge the sensors as well as collect data from them. The current studies also took into account that the sensors always receive full charge from the MV, resulting in a wireless rechargeable sensor network (WRSN) with very little dead period and very little data gathering latency. It is also believed that the energy consumption rates of the sensors are constant. However, in large-scale WRSN, the aforementioned considerations are not always practical. In addition, the utilization of single base station (BS) in a large-scale WRSN cannot guarantee improved network scalability, expedite charging decisions by minimizing the substantial overhead of the BS, and enhance the traveling distance for MV. In order to overcome the aforementioned problems, we present an effective on-demand partial CDAC scheme using battery-limited multiple MVs. The proposed scheme implements the CDAC process in such a way that the total dead periods of the sensors and the energy consumption of MVs are reduced. We use a multi-objective-based genetic algorithm (GA) to optimize the entire CDAC process. The simulation is carried out to demonstrate the usefulness and competitiveness of the proposed scheme. In comparison with existing works, the proposed work improves CDAC performance by reducing sensor dead time and energy consumption of MV.     

Demand‐based charging strategy for wireless rechargeable sensor networks

A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a widespread research problem. In this paper, we propose a demand‐based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to‐be‐charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K‐means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on‐demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.     

Age of information analysis for internet of things information status update system with cellular backhaul and dedicated energy harvesting beacons

The Internet of Things (IoT) that is usually deployed with the assistance of cellular backhaul and energy harvesting (EH) is characterized by the status update freshness. However, the traditional nature EH results in the loss of information freshness due to the randomness of nature energy process. Although using wireless energy transferring (WET) to charge sensors can overcome this problem, this method has low-energy efficiency. With these considerations, this paper proposes a novel IoT system under cellular communication scenario by simultaneously exploiting the nature EH and WET. The proposed IoT system is consisted of a cellular backhaul subsystem, a wireless EH and transferring (WEHT) subsystem, and a wireless sensor status update subsystem. The WEHT subsystem employs the dedicated power beacons harvesting energy from natural energy source and transferring the harvested energy to sensor. For the proposed IoT system, this paper first investigates the WEHT subsystem and constructs the Markov Chain (MC) of discrete energy states as well as the MC state transition matrix. Second, the average AoI and peak AoI (PAoI) are formulated by separately considering cellular backhaul and EHTBs. The AoI (PAoI) comparison shows that the proposed EHTB-based IoT system can outperform the conventional non-EHTB one where the sensor directly harvests energy from natural source. At the same time, the numerical results exploit the impact of system parameters on AoI and PAoI, respectively. It is found that there exists a trade-off between the nature energy arrival and EHTB energy transfer so that the average minimum AoI and PAoI are achieved.     

可充电概率传感网络中的充电小车休息时间最大化方法

在保证无线可充电传感网中目标点持久覆盖的情况下,基于概率监测模型和多节点充电模型,对最大化传感网络中充电小车休息时间比例问题进行研究。文中通过放宽节点不可死亡的限制,提出基于单位子簇的充电选择算法和基于全局的重聚类启发式算法。将目标点周围冗余的传感器构建成目标簇并根据贪心思想划分子簇。充电小车以子簇为单位服务覆盖过程中的充电请求,通过调整子簇内距离要求并对全局请求节点重聚类来选择锚点进行充电,从而降低子簇的混合增益,减少锚点数量。仿真实验表明,与单节点充电模型相比,新算法可将充电小车的休息时间提升10%~15%。     

无线传感器网络时变充电和动态数据路由算法研究

利用无线充电设备周期地为无线传感器节点进行充电,可使传感器节点持续工作,解决无线传感器网络能量问题,但需设计合适的节点充电方案和网络路由方案。以无线充电设备驻站比最大化为优化目标,依次提出了充电及网络路由方案的连续时变模型、离散N+1阶段非线性模型及可解的离散N+1阶段线性模型,在证明模型合理性后最终求解。结论给出了具体网络的最优充电和路由策略。     

An improved algorithm for dispatching the minimum number of electric charging vehicles for wireless sensor networks

The very limited sensor battery energy greatly hinders the large-scale, long-term deployments of wireless sensor networks. This paper studies the problem of scheduling the minimum charging vehicles to charge lifetime-critical sensors in a wireless rechargeable sensor network, by utilizing the breakthrough wireless charging technology. Existing studies still employ a number of charging vehicles to charge sensors. The purchase cost of a charging vehicle however is not inexpensive. To further reduce the number of employed charging vehicles, we propose a novel approximation algorithm, by exploring the combinatorial properties of the problem. The techniques exploited in this paper are essentially different from that in existing studies. Not only do we show that the approximation ratio of the proposed algorithm is much better than that of the state-of-the-art, but also extensive experimental results demonstrate that the number of scheduled charging vehicles by the proposed algorithm is at least 10% less than that by the existing algorithms and the total travel energy consumption of the charging vehicles is also smaller than that by the existing algorithms.

     

Charging utility maximization in wireless rechargeable sensor networks

Wireless energy transfer as a promising technology provides an alternative solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). In this paper, we study replenishing energy on sensors in a WRSN to shorten energy expiration durations of sensors, by employing a mobile wireless charger to replenish sensors dynamically. We first formulate a novel sensor recharging problem with an objective of maximizing the charging utility of sensors, subject to the total traveling distance of the mobile charger per tour and the charging time window of each to-be-charged sensor. Due to the NP-hardness of the problem, we then propose an approximation algorithm with quasi-polynomial time complexity. In spite of the guaranteed performance ratio of the approximate solution, its time complexity is prohibitively high and may not be feasible in practice. Instead, we devise a fast yet scalable heuristic for the problem in response to dynamic energy consumption of sensors in the network. Furthermore, we also consider the online version of the problem where sensor replenishment is scheduled at every fixed time interval. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are very promising.     

一种基于ZigBee协议的DSDV改进型路由算法

大量的研究人员也投入精力研究和改进无线传感网。其中,ZigBee协议在无线传感网中扮演着举足轻重的角色,其可使大量传感器高效互连的能力,是有线网络所不能及的。提出了一种基于ZigBee协议DSDV路由算法的改进型路由算法,根据仿真,改进后的算法在处理存在移动节点通信时有一定的优势。     

基于中继充电模型的充电小车休息时间最大化方法

文中基于中继充电模型,研究了在保证无线可充电传感器网络持久运行的情况下,最大化充电小车在服务基站休息时间的优化调度问题。针对该问题,文中提出的近似算法包括3个部分:基于充电效益的锚点选择算法为充电小车选择停靠点、基于TSP的路径规划算法为充电小车规划移动路径以及基于充电集合再优化算法进一步提升充电小车的休息时间。通过仿真实验对比不同的充电策略,结果显示相比AASA算法,在不同传感器分布密度下,文中提出的充电策略可以将充电小车在服务基站的休息时间提升15%~88%。     

无线充电及其在图像采集粮虫检测传感器网络中的应用

随着科技的发展,无线充电技术在各个领域的应用也越来越广泛,比如在手机、电脑、相机、汽车等行业的部分最新产品中已经使用了无线充电技术。介绍了一种简便高效的无线充电方案,应用于埋入式图像采集粮虫检测系统,以确保传感器网络节点能够方便、安全、稳定地工作。该系统的工作环境特殊,无线充电技术有无需物理接触的优势,可以合理有效地替代传统的有线充电方法。使用高度集成的XKT-408A作为系统的发射芯片,再配合极少的外部元件就可以制作可靠稳定的无线充电器,不仅保证了无线充电的传输效率,也大大降低传感器网络节点的体积。     

A Learning Automata Based Area Coverage Algorithm for Wireless Sensor Networks

One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.     

可充电无线传感器网络动态拓扑问题研究

在可充电无线传感器网络中的能量补给设备兼任数据采集设备的情况下,提出了可充电无线传感器网络时变动态拓扑模型,并在此基础上根据最大化能量补给设备驻站时间比为目标提出了最优化问题。通过分析不同时刻不同传感器节点和无线能量补给/数据采集设备的工作情况及需要遵循的约束条件,得到与原问题具有等优性的多状态线性规划问题。求解该优化问题,获得可充电无线传感器网络动态拓扑下的周期动态路由和无线能量补给/数据采集设备的工作策略。与之前的研究成果相比,优化目标值均有20%以上的提升。     

基于QI标准的无线充电传感器网络节点设计

本文为无线传感网络节点设计无线充电电路,采用基于QI标准的IC芯片,通过发射线圈和接收线圈之间的耦合,借助电磁感应完成能量传输,实现无线充电,并设计PCB板进行测试。实验证明该方案可行,且具有较高的应用价值。     

An optimal algorithm for solving partial target coverage problem in wireless sensor networks

This paper deals with the partial target coverage problem in wireless sensor networks under a novel coverage model. The most commonly used method in previous literature on the target coverage problem is to divide continuous time into discrete slots of different lengths, each of which is dominated by a subset of sensors while setting all the other sensors into the sleep state to save energy. This method, however, suffers from shortcomings such as high computational complexity and no performance bound. We showed that the partial target coverage problem can be optimally solved in polynomial time. First, we built a linear programming formulation, which considers the total time that a sensor spends on covering targets, in order to obtain a lifetime upper bound. Based on the information derived in previous formulation, we developed a sensor assignment algorithm to seek an optimal schedule meeting the lifetime upper bound. A formal proof of optimality was provided. We compared the proposed algorithm with the well‐known column generation algorithm and showed that the proposed algorithm significantly improves performance in terms of computational time. Experiments were conducted to study the impact of different network parameters on the network lifetime, and their results led us to several interesting insights. Copyright © 2011 John Wiley & Sons, Ltd.     

Maximizing the lifetime of energy‐constrained cooperative spectrum sensing sensor network

Adding the cognitive capability to wireless sensor networks allows the sensors to monitor the spectrum and identify the spectrum holes to operate in different frequencies according to the radio environment which result in better spectrum utilization. Spectrum sensing is a main component in any cognitive radio network. In this paper, we propose a new cooperative sensing scheme based on energy detection for cognitive sensor networks which is constrained by the energy limitation of the wireless sensor elements. The proposed scheme minimizes the sensing energy for individual sensor and carefully selects the suitable participant sensors in each cooperative sensing process. This results in maximizing the lifetime of energy‐constrained wireless sensor networks. The proposed scheme also takes into consideration the constraints on the detection accuracy. The simulation results show that the proposed scheme prolonged the lifetime of the cognitive network, makes efficient usage of available spectrum by secondary users, and satisfy the target detection performance.     

射频能量捕获传感网移动能量源的布置策略研究

射频能量捕获传感网（RF Energy Harvesting Wireless Sensor Network,RFEH-WSN）由专用射频能量源设备（Energy Transmitter,ET）和具有射频能量捕获功能的传感器节点（Energy Harvesting Recevier,简称EHR）组成.该网络解决了传感器网络中电池不易更换与节点能量容易耗尽的问题,使得RFEH-WSN应用前景更加广阔.RFEH-WSN应用中一个值得研究的问题是如何布置ET的充电位置,降低ET能耗且提高覆盖率.已有的工作主要考虑ET布置中单目标优化问题,如最小充电时间、最小功耗、最大覆盖率等.本文以时间最小和覆盖率最大为目标建立多目标优化模型,并提出利用粒子群算法（Particle Swarm Optimization,PSO）求解多目标函数（Multiple Object Program,MOP）的低复杂度近似算法,获得了最优Pareto解集.仿真结果表明,多目标优化可以满足不同情况的需求,提高充电效用.     

Path planning algorithm for WCE with joint energy replenishment and data collection based on multi-objective optimization

Considering limited energy of the wireless charging equipment (WCE) in wireless rechargeable sensor network,an energy replenishment strategy and a data collection strategy are designed.On the basis of these,a path planning model for WCE with functions of joint energy replenishment and data collection based on multi-objective optimization is constructed with two optimization objectives,maximizing the total energy utility of WCE and minimizing the average delay of data transmission of all the sensor nodes in the network.To deal with it,a multi-objective ant colony optimization algorithm based on elitist strategy was proposed,where the state transition strategy and the pheromone updating strategy were improved.Then,the Pareto set was obtained in terms of this multi-objective optimization problem.The parameter setting of ant colony algorithm’s effects on the proposed algorithm were analyzed under 20 sensor nodes.50 groups of contrastive experiments show that the average number of energy utilization obtained by ES-MOAC algorithm is 4.53% higher than that of NSGA-II algorithm.The average number of average delay of all node data transmission obtained by ES-MOAC algorithm is 5.12% lower than that of NSGA-II algorithm.     

基于遗传聚类的无线传感器负载均衡路由算法

通常的无线传感器分簇网络存在节点负载不均衡的问题。为均衡各节点能量消耗,延长网络生存周期,将K均值算法与遗传算法相结合,提出一种负载均衡的无线传感器网络路由算法,算法利用遗传算法的全局寻优能力以克服传统K均值算法的局部性和对初始中心的敏感性,实现了传感器网络节点自适应成簇与各节点负载均衡。仿真实验表明,该算法显著延长了网络寿命,相对于其他分簇路由算法,其网络生存时间延长了约43%。