无线可充电传感器网络能耗分级充电策略

在传统的无线传感器网络中,有限的电池能量会限制传感器网络的寿命,而在无线可充电传感器网络中,能量可以通过无线方式给传感器充电,延长传感器网络的寿命,利用充电小车等移动设备对无线传感器网络进行能量补充时,在一个充电周期内减小充电小车的移动总路径,可以有效减少经济成本,基于无线可充电传感网中各节点的能耗差异性,结合蚁群算法和对旅行商(TSP)问题的研究,提出了基于能耗分级的非固定周期和固定周期两种小车充电策略。仿真结果表明,与传统的充电策略相比,两种新策略均能有效减少充电小车的移动总路径。     

一种基于谐振中继的可充电传感器网络移动能量补充方法

多跳无线能量传输技术已经成为延长无线传感器网络(WSN)寿命的有效手段。提出一种基于谐振中继的可充电传感器网络移动能量补充方法(TMWRN)。首先在整个网络中根据三角形外接圆性质部署谐振中继节点,然后,移动充电装置(MC)根据短距离优先的在线充电方式规划充电路径,并利用谐振中继器给多个传感器节点以多跳方式补充能量。最后模拟实验表明,TMWRN可以有效地降低MC的充电成本和节点失效率,延长网络寿命。     

无线可充电传感器网络中能量饥饿避免的移动充电

无线可充电传感器网络（wireless rechargeable sensor networks,简称WRSN）中,如何调度移动充电器（mobile charger,简称MC）,在充电过程中及时为传感器节点补充能量,尽量避免节点能量饥饿的同时降低MC充电代价及节点平均充电延迟,成为无线充电问题的研究挑战.大多数现有WRSN充电策略或是不能适应实际环境中传感器节点能量消耗的动态性和多样性,或是没有充分考虑节点及时充电问题和MC对充电响应的公平性,导致节点由于能量饥饿失效和充电策略性能下降.当网络中请求充电的节点数量较多时,节点能量饥饿现象尤为明显.为此,研究了WRSN中移动充电的能量饥饿问题,提出了能量饥饿避免的在线充电策略（energy starvation avoidance onlinecharging scheme,简称ESAOC）.首先,根据各节点能量消耗的历史统计和实时值计算当前能量消耗率.接着,在调度MC时,根据当前能量消耗率计算各请求充电节点的最大充电容忍延迟和当某节点被选为下一充电节点时各节点的最短充电等待时间,通过比较这两个值,始终选择使其他待充电节点饥饿数量最少的节点作为充电候选节点以尽量避免节点陷入能量饥饿.仿真分析表明：与现有几种在线充电策略相比,ESAOC不仅能有效解决节点的能量饥饿问题,同时具有较低的充电延迟和充电代价.     

多节点部分充电模型下的充电调度优化

在无线可充电传感器网络中,针对移动充电车采用多节点部分充电模型在充电调度过程中往复行驶,导致充电时间增加的问题进行了研究。提出一种新颖的多节点部分充电模型,从全局优化移动充电车在每个充电位置的充电时间,保证每个能量临界的传感器被充满电。同时提出AlgMinTime算法进行路径规划,确定移动充电车的充电环路以及对应位置的充电时间,使得环路上总充电调度时间最小化。最终通过仿真实验评估所提出算法的性能。实验结果表明,所提算法的充电调度平均环路时间相较于SOTA算法缩短9.8%。     

WRSN中多MC协同的一对多能量补充策略

无线可充电传感网络(Wireless Rechargeable Sensor Networks ,WRSN)由于受到传感器有限的电池容量限制,所面临的一项重要挑战是如何调度移动充电器MC(Mobile Charger)及时为传感器进行充电,避免传感器由于能量过低而失效。现有的充电策略中单MC充电策略难以满足大规模WRSNs的电量需求,多MC充电策略常忽略充电的均衡性。为此,本文研究了WRSN中多MC协同充电问题,提出一种多MC协同的一对多能量补充策略(MTORN)。首先通过相交圆算法将网络中的传感器节点划分为若干个节点簇,MC根据节点簇的平均剩余能量以及距离划分簇的优先级,每个MC前往不同的节点簇进行一对多充电以提高充电效率。仿真结果表明,与现有的算法相比,MTORN能够有效降低网络中传感器节点失效数量和MC的移动成本,延长网络生存时间。     

基于WRSN的多节点按需充电策略研究

针对无线可充电传感器网络(WRSN)中的节点死亡率过高问题，为了降低节点死亡率，以按需充电架构为基础，提出了一种动态不均匀分簇的单移动充电设备(MC)多节点在线充电策略SMMCS(single MC multi-node charging strategy)。策略首先将无线可充电传感器网络进行动态不均匀分簇，以此划分移动充电设备的服务分区；然后在此模型基础上以最小网络节点死亡率为目标，进行路径规划时综合考虑节点剩余能量、距离以及能耗等因素。仿真实验结果表明，与SAMER、VTMT以及FCFS策略相比，该策略减少了节点等待时间，缩短了MC总充电代价，减小了节点死亡率。基于仿真条件，网络节点死亡率为4.31%。     

无线可充电传感器网络高效在线充电算法

在无线可充电传感器网络中,传感器节点的电池寿命是决定整个传感器网络生命周期的重要因素之一,而移动充电车可有效地为传感器节点提供电量补给。在动态请求(On-Demand)的无线可充电传感器网络中,研究充电车移动耗能和充电周期内总电量两个约束条件下的充电传感器数量最大化问题。针对该问题建立非线性整型数学模型,并提出一个基于贪心策略的在线算法。该算法在每个充电周期内,充电车依次选择距离最近的传感器节点进行充电。基于聚类思想,提出另一个在线算法。该在线聚类算法利用解决旅行商问题的最小生成树算法,使得充电车在每一个类中的充电路径构成一条回路的同时,减少移动耗能。实验结果表明,在线贪心算法、在线聚类算法得出的充电传感器数量分别占充电请求总数的67%与76%。     

基于充电效率的WRSN能量补充策略

无线可充电传感器网络(WRSN)的节点能量补充问题是当前传感器网络研究的一个热点。已有研究大多假设传感器能量消耗速率较为恒定,因此难以适应能量动态消耗的实际场景;还有些研究虽然考虑了节点充电请求的动态性,却无法选出适当的充电对象,使性能受到限制。为解决该问题,分析了WRSN的充电问题,提出基于充电效率的能量补充策略(CEBER)。该策略首先提出充电效率的量化计算方法,将充电效率作为选择充电对象的重要决策因素;同时其也考虑了节点所能容忍的最长充电等待时间,使决策结果尽可能避免引起节点失效。仿真结果表明,CEBER能够有效降低节点失效率,提高网络整体的充电效率,从而为WRSN提供更加有效的充电服务。     

大规模无线传感器网络中高效按需充电规划

随着无线充电技术的日趋成熟,特别是磁共振无线充电技术的发展,利用移动充电车和无线充电技术给无线传感器补充能量,以保证无线传感器网络持续运转,成为新的研究热点。为此,主要介绍在大规模的无线传感器网络中,如何调度多个充电车给网络中的待充电传感器补充能量。为了均衡多个充电车的充电任务,缩小整个充电任务的完成时间,提出了充电总耗时最短问题,希望能为多个充电车找到各自的充电路径,使得多个充电车中耗时最长的任务完成时间最短。因为充电总耗时最短问题是一个NP难问题,难以在多项式时间内找到最优解,所以针对该问题提出了一个近似比为5的近似算法。最后用模拟实验证明了算法的性能,实验表明该算法的实际近似比不足2。     

大范围WRSNs的数据路由和无线充电算法

针对传统电池供电系统能量有限问题,提出一种大范围无线可充电传感器网络(WRSNs)的数据路由和无线充电算法.以无线感知识别平台为基础,利用等边三角形的强覆盖性,并综合考虑网络路由协议和无线充电器特性对节点能量的影响,提出一种基于六边形路径的动态无线充电算法(IJRC_HP).算法包括两部分:依据充电器特性设计数据路由方法,使得能量接收功率高的节点承担更多的通信任务;依据该路由的特点设计充电方案,为能耗速度快的节点分配更多的充电时间.与等边三角形算法(TRIANGLE)、GRID算法进行仿真对比,实验结果表明,IJRC_HP算法在网络寿命提升、能量均衡、充电器移动效率和节点平均充电延时等方面具有优越性.     

用于智能电网的有差别射频充电传感器网络

针对应用于智能电网中的无线传感器网络(WSNs)节点能量受限问题,分析了基于无线射频充电技术的为传感器节点充电技术,改进了可持续无线充电传感器网络(SWRSNs),提出有差别射频充电传感器网络(DRRSNs)技术,增加节点的优先级设置,建立整数线性规划模型,用CPLEX求解模型确定标志性节点位置。求解数据表明:节点获得的能量平均提高105%,高优先级节点比低优先级节点平均多获得43%的能量,提高了节点的寿命,保证了WSNs的可靠性,但是路径访问效率平均降低了14%。     

TADP: Enabling temporal and distantial priority scheduling for on-demand charging architecture in wireless rechargeable sensor Networks

Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attentions from the research community. The utilization of the mobile energy chargers provides a more reliable energy supply than systems harvesting dynamic energy from the surrounding environment. Wireless power transfer technique provides a new alternative for solving the limited power capacity problem for so many popular mobile wireless devices, and makes wireless rechargeable sensor networks (WRSNs) promising. However, mainly due to the underestimate of the unbalanced influences of spatial and temporal constraints posed by charging requests, traditional scheduling strategies for on-demand WRSNs architecture achieve rather low charging request throughput or successful rate, posing as a major bottleneck for further improvements. In this paper, we propose a TemporAl & Distantial Priority charging scheduling algorithm (TADP), which takes both the distance between nodes and the mobile charger and the arrival time of charging requests into consideration, and quantizes these two factors step by step. TADP forms a mixed priority queue which directs mobile charger to replenish the energy for nodes. At last extensive simulations are conducted to demonstrate the advantages of TADP. Simulation results reveal that TADP can achieve better scheduling performance in guaranteeing the scheduling success of the high-priority tasks and improving stability of the system.     

无线传感器网络RCF-MAC协议研究

射频充电（ RFC）技术是一种通过基站发射射频电波为传感器节点补充电量的新兴技术。但在RFC无线传感器网络中,多基站同时发射能量时产生的能量干涉衰减现象会明显降低充电效率,此外,能量传输与数据通信共享信道会降低整个网络的吞吐量。针对上述问题,提出了RFC—MAC协议,为提高充电效率,将请求充电传感器节点周围的基站按距离分为两组,两组基站分时能量传输,以避免能量干涉衰减现象;传感器网络中设双信道,分别用于能量传输与数据通信,以提高网络的吞吐量。仿真结果表明：RFC—MAC协议显著提高了传感器节点的充电效率和平均网络吞吐量。     

Periodic charging planning for a mobile WCE in wireless rechargeable sensor networks based on hybrid PSO and GA algorithm

Previous studies of periodic charging planning in Wireless Rechargeable Sensor Networks (WRSNs) assumed that the traveling energy of a mobile Wireless Charging Equipment (WCE) has sufficient energy for charging travel and the energy depletion rate at each sensor is identical. These assumptions, however, are not realistic. In fact, the traveling energy of the WCE is limited by the energy capacity of the WCE and the energy consumptions at different sensor nodes are imbalanced. In this paper, a periodic charging planning for a mobile WCE with limited traveling energy is proposed. With the optimization objective of maximizing the the docking time ratio, this periodic charging planning ensures that the energy of the nodes in the WRSN varies periodically and that nodes perpetually fail to die. To deal with the problem, a Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA) is proposed due to the NP-Hard of the problem. Extensive simulations have been conducted, the experimental results indicate that the proposed periodic charging planning can avoid node deaths and keep the energy of sensor nodes varying periodically. Compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), the algorithm HPSOGA outperforms both of these two algorithms empirically.     

无线传感器节点实时能耗监测

无线传感器网络设计的关键技术环节之一是低功耗。首先要准确地检测到运行时各模块的能量消耗,然后才能够进行控制优化。然而,应用上的需求千差万别,导致硬件平台的多样性。本文选取无线传感器网络领域节点能量消耗检测作为研究方向,提出了一种便携的无线传感器节点能耗数据采集装置,能够实时采集节点的电压、电流信息。针对当前研究集中于仿真模拟,这种装置可用于实际环境的能量消耗检测,检测精度高。在MicaZ节点上的实验结果表明,该系统能以较高精度监测节点的能量效率。实际测量发现,传感器网络中传感器能耗也属能量消耗的一部分,且消耗量不可忽视,为今后的研究改进提供了新的参考。     

基于非合作博弈的无线传感器网络功率控制研究

如何提高能量的有效性是无线传感器网络（WSNs）设计的重要问题,针对WSNs在多媒体等业务中的应用,对基于码分多址（CDMA）通信方式的WSNs模型,提出一种基于非合作博弈的WSNs功率控制算法,并证明了该算法纳什均衡的存在性及唯一性.仿真结果表明,所提出的算法在设计时充分考虑了节点的剩余能量问题,因此能够很好地降低网络的总发射功率,有效地节约节点能量,延长网络的生命周期.     

A survey of game-theoretic approaches in wireless sensor networks

Wireless sensor networks (WSNs) comprising of tiny, power-constrained nodes are gaining popularity due to their potential for use in a wide variety of environments like monitoring of environmental attributes, intrusion detection, and various military and civilian applications. While the sensing objectives of these environments are unique and application-dependent, a common performance criteria for wireless sensor networks is prolonging network lifetime while satisfying coverage and connectivity in the deployment region. Security is another important performance parameter in wireless sensor networks, where adverse and remote environments pose various kinds of threats to reliable network operation. In this paper, we look at the problems of security and energy efficiency and different formulations of these problems based on the approach of game theory. The potential applicability of WSNs to intruder detection environments also lends itself to game-theoretic formulation of these environments, where pursuit-evasion games provide a relevant framework to model detection, tracking and surveillance applications.The suitability of using game theory to study security and energy efficiency problems and pursuit-evasion scenarios using WSNs stems from the nature of strategic interactions between nodes. Approaches from game theory can be used to optimize node-level as well as network-wide performance by exploiting the distributed decision-making capabilities of WSNs. The use of game theory has proliferated, with a wide range of applications in wireless sensor networking. In the wake of this proliferation, we survey the use of game-theoretic approaches to formulate problems related to security and energy efficiency in wireless sensor networks.     

Understanding optimal data gathering in the energy and latency domains of a wireless sensor network

The problem of optimal data gathering in wireless sensor networks (WSNs) is addressed by means of optimization techniques. The goal of this work is to lay the foundations to develop algorithms and techniques that minimize the data gathering latency and at the same time balance the energy consumption among the nodes, so as to maximize the network lifetime. Following an incremental-complexity approach, several mathematical programming problems are proposed with focus on different network performance metrics. First, the static routing problem is formulated for large and dense WSNs. Optimal data-gathering trees are analyzed and the effects of several sensor capabilities and constraints are discussed, e.g., radio power constraints, energy consumption model, and data aggregation functionalities. Then, dynamic re-routing and scheduling are considered. An accurate network model is proposed that captures the tradeoff between the data gathering latency and the energy consumption, by modeling the interactions among the routing, medium access control and physical layers.For each problem, extensive simulation results are provided. The proposed models provide a deeper insight into the problem of timely and energy efficient data gathering. Useful guidelines for the design of efficient WSNs are derived and discussed.