一种太阳能传感器网络的自适应休眠调度算法

通过从外界获取太阳能,传感器网络节点的能量限制得到缓解。提出一种太阳能传感器网络的自适应休眠调度算法。当观测场景从区域观测到目标跟踪转变时,节点自适应地转换活跃和休眠状态,同时剩余能量低于能量阈值的休眠节点从外界获取太阳能。与不考虑太阳能获取的休眠调度算法相比,提出的算法延长了网络生命周期。     

Power management in SMAC-based energy-harvesting wireless sensor networks using queuing analysis

One of the most important constraints in traditional wireless sensor networks is the limited amount of energy available at each sensor node. The energy consumption is mainly determined by the choice of media access mechanism. SMAC is a typical access mechanism that has drawn much attention in recent years. In WSNs, sensors are usually equipped with capacity-limited battery sources that can sustain longer or shorter period, depending on the energy usage pattern and the activeness level of sensor nodes. To extend the lifetime of the sensor networks, ambient energy resources have been recently exploited in WSNs. Even though solar radiation is known as the superior candidate, its density varies over time depending on many factors such as solar intensity and cloud states, which makes it difficult to predict and utilize the energy efficiently. As a result, how to design an efficient MAC in a solar energy harvesting based WSN becomes a challenging problem. In this paper, we first incorporate a solar energy-harvesting model into SMAC and conduct its performance analysis from a theoretical aspect. Our research works provide a fundamental guideline to design efficient MAC for energy harvesting based WSNs. Our major contribution includes three folders: firstly, we model solar energy harvesting in a photovoltaic cell and then derive the throughput of SMAC in the energy-harvesting based WSNs. Second, we develop a new model based on queuing theory to calculate the average number of energy packets in battery in terms of both duty cycle and throughput. Finally, we form an optimization problem to find a suitable range for the duty cycle to satisfy both quality of service (QoS) and network lifetime requirements.     

基于模糊控制的自供能无线传感器网络分簇算法

现有自供能无线传感器网络（WSN）分簇算法较少考虑网络最优分簇数,导致网络能量消耗过快,全网能耗不均衡。针对这个问题,提出了基于模糊控制的自供能WSN分簇算法（EH-FLC）。首先,在网络能量消耗模型中引入太阳能补给模型,得出每一轮次网络能量总消耗与网络分簇数目的函数关系,并对其求导从而得到网络的最佳分簇数。然后,利用双层模糊决策系统来评定网络中的节点能否成为簇头节点。先将节点剩余能量、相邻节点数作为判定指标输入第一层（能力层）对所有节点进行筛选,得到备选簇头节点;再将中心度参数、邻近度参数作为判定指标输入第二层（协作层）对备选簇头节点进行筛选,得到网络簇头节点。最后,通过Matlab仿真分析了该算法的网络生存周期、网络能量消耗和网络吞吐量等性能指标,与低功耗自适应集簇分层型协议（LEACH）、改进的非均匀分簇路由算法（WUCH）和利用双层模糊控制的簇头选择算法（CTLFL）相比,该算法在网络工作寿命上分别提高了约1.4倍、0.4倍和0.6倍,网络吞吐量上分别提高了约20倍、1.5倍和1.28倍。仿真结果表明所提算法在网络生存周期和网络吞吐量方面的性能较优。     

基于模糊控制的自供能无线传感器网络分簇算法

现有自供能无线传感器网络（WSN）分簇算法较少考虑网络最优分簇数，导致网络能量消耗过快，全网能耗不均衡。针对这个问题，提出了基于模糊控制的自供能WSN分簇算法（EH-FLC）。首先，在网络能量消耗模型中引入太阳能补给模型，得出每一轮次网络能量总消耗与网络分簇数目的函数关系，并对其求导从而得到网络的最佳分簇数。然后，利用双层模糊决策系统来评定网络中的节点能否成为簇头节点。先将节点剩余能量、相邻节点数作为判定指标输入第一层（能力层）对所有节点进行筛选，得到备选簇头节点；再将中心度参数、邻近度参数作为判定指标输入第二层（协作层）对备选簇头节点进行筛选，得到网络簇头节点。最后，通过Matlab仿真分析了该算法的网络生存周期、网络能量消耗和网络吞吐量等性能指标，与低功耗自适应集簇分层型协议（LEACH）、改进的非均匀分簇路由算法（WUCH）和利用双层模糊控制的簇头选择算法（CTLFL）相比，该算法在网络工作寿命上分别提高了约1.4倍、0.4倍和0.6倍，网络吞吐量上分别提高了约20倍、1.5倍和1.28倍。仿真结果表明所提算法在网络生存周期和网络吞吐量方面的性能较优。     

Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks

Target tracking in wireless sensor networks can be considered as a milestone of a wide range of applications to permanently report, through network sensors, the positions of a mobile target to the base station during its move across a certain path. While tracking a mobile target, a lot of open challenges arise and need to be investigated and maintained which mainly include energy efficiency and tracking accuracy. In this paper, we propose three algorithms for tracking a mobile target in wireless sensor network utilizing cluster-based architecture, namely adaptive head, static head, and selective static head. Our goal is to achieve a promising tracking accuracy and energy efficiency by choosing the candidate sensor nodes nearby the target to participate in the tracking process while preserving the others in sleep state. Through Matlab simulation, we investigate the performance of the proposed algorithms in terms of energy consumption, tracking error, sensor density, as well as target speed. The results show that the adaptive head is the most efficient algorithm in terms of energy consumption while static and selective static heads algorithms are preferred as far as the tracking error is concerned especially when the target moves rapidly. Furthermore, the effectiveness of our proposed algorithms is verified through comparing their results with those obtained from previous algorithms.     

基于动态数据压缩的能量采集无线传感网络数据收集优化

针对能量采集无线传感网络（WSN）中的数据收集优化问题,考虑传感器节点能量采集的时空变化特性,提出一种基于节点动态采样速率和数据压缩的策略,以实现网络中采样数据总量的最大化。首先,提出一种根据节点的邻居信息决定其最优压缩策略的本地压缩算法,基于节点在数据汇聚树中的拓扑位置考虑其数据接收和转发能耗,逐渐增加其采样速率直到其总能耗到达采集能耗阈值。接着构造网络性能的全局优化问题并提出一种启发式的算法,通过迭代求解线性规划问题计算最优的采样速率和压缩策略。实验结果表明,与现有的自适应传感和压缩率选择方案相比,所提出的两种数据收集优化算法能够维持更加稳定的传感器节点电量水平并实现更高的网络性能。     

基于无线传感器网络的战场目标跟踪

基于无线传感器网络,对目标定位跟踪应用进行了研究。在对目标定位跟踪时,如何既保证跟踪精度又能有效降低能量消耗,针对这个问题,提出了一种简便的加权坐标质心定位方法,通过对目标的定位,给出了一种基于测量信息的跟踪方法,方法实现简单。性能分析表明:提出的定位跟踪方法能有效地降低能量消耗,延长节点和网络寿命,基本可以满足战场目标跟踪需求。     

Elastic neural network method for multi-target tracking task allocation in wireless sensor network

Aiming at the task allocation of collaborative technique in wireless sensor network, a method for optimized task allocation based on elastic neural network is proposed under the background of multi-sensor tracking. First a model of multi-coalition tracking multi-target is designed. Then disjoint fully connected subgraphs of neurons are constructed to solve the problem of optimized task allocation in tracking multi-target and the increment of system energy consumption when dynamic coalitions compete and conflict for the resource of sensor nodes. Compared with the conventional method, simulation results show that the energy consumption of the tracking system is reduced significantly and the tracking accuracy is improved greatly, demonstrating the effectiveness of elastic neural network in handling the optimized task allocation problem of multi-sensor tracking multi-target.     

Cooperative distributed target tracking algorithm in mobile wireless sensor networks

The paper proposes a cooperative distributed target tracking algorithm in mobile wireless sensor networks.There are two main components in the algorithm:distributed sensor-target assignment and sensor motion control.In the key idea of the sensor-target assignment,sensors are considered as autonomous agents and the defined objective function of each sensor concentrates on two fundamental factors:the tracking accuracy and the tracking cost.Compared with the centralized algorithm and the noncooperative distrib...     

Energy-efficient localization and target tracking via underwater mobile sensor networks

Underwater mobile sensor networks (UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. Target tracking, whose performance depends on sensor localization accuracy, is one of the broad applications of UMSNs. However, in UMSNs, sensors move with environmental forces, so their positions change continuously, which poses a challenge on the accuracy of sensor localization and target tracking. We propose a high-accuracy localization with mobility prediction (HLMP) algorithm to acquire relatively accurate sensor location estimates. The HLMP algorithm exploits sensor mobility characteristics and the multi-step Levinson-Durbin algorithm to predict future positions. Furthermore, we present a simultaneous localization and target tracking (SLAT) algorithm to update sensor locations based on measurements during the process of target tracking. Simulation results demonstrate that the HLMP algorithm can improve localization accuracy significantly with low energy consumption and that the SLAT algorithm can further decrease the sensor localization error. In addition, results prove that a better localization accuracy will synchronously improve the target tracking performance.     

Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks

Sensor scheduling is essential to collaborative target tracking in wireless sensor networks (WSNs). In the existing works for target tracking in WSNs, such as the information-driven sensor query (IDSQ), the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energyefficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter (EKF)-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.     

FCM-AFSA的无线传感器网络多目标跟踪节点任务分配方法

多目标跟踪是无线传感器网络重要应用之一。提出了基于离散人工鱼群算法的无线传感器网络多目标跟踪节点任务分配方法。该方法首先利用类间距阈值的模糊C均值聚类算法,估计监测区域可能出现的目标数量和目标位置;再根据任务分配的目标函数,使用改进的离散人工鱼群算法优化目标函数,从而得到任务分配方案,并同其他算法进行比较。仿真实验结果表明,该方法比最近邻方法、MEM方法以及粒子群算法的能耗有所降低,任务分配时间比最近邻方法、MEM方法以及粒子群算法有所减少。因此,所提出的改进算法能有效地提高无线传感器网络的综合性能,满足实际应用的需求。     

无线传感器网络的差错控制技术研究

在分析传统无线网络差错控制方案的基础上,研究差错控制引起的能量消耗因素,通过介绍无线传感器网络能量消耗模型,借助Synopsys power comp lier分析了采用前项差错控制以及常用纠错码所引起的能量消耗。最后,讨论了无线传感器网络差错控制技术今后的研究重点。     

传感器网络分布式事件触发多目标估计

本文主要研究无线传感器网络中目标数目已知且固定的一类分布式多目标跟踪问题,提出了一种完全分布式的基于事件触发的测量和通信策略使得每个节点在不需要全局信息的情况下实现估计误差和能量消耗之间的平衡.监测区域存在多个移动目标,传感器能否测量到单个目标由事件触发测量机制和节点的测量半径来综合决定.基于节点和邻居的信息采用k-means聚类算法来解决数据关联问题,同时提出了基于最小迹原则的一致性卡尔曼滤波算法.从理论上证明了该事件触发策略不仅在性能指标上优于基于时间触发的算法,而且在网络中如果存在节点对多目标协同可观,系统估计误差在均方意义下是稳定的.最后给出了仿真例子验证了该算法的有效性和可行性.     

无线传感器网络中面向目标跟踪的动态分簇方法

针对传感器网络中的目标跟踪问题,提出一种能量有效的动态分簇方法,通过设置簇内传感器节点数目门限,自适应地调整簇的激活半径,通过多传感器节点的协作处理提高目标跟踪精度;并对动态簇的构建、重组过程以及能量消耗进行了描述和分析。仿真结果表明,与现有算法相比,所提出的方法能够在保证一定跟踪精度的基础上,有效降低网络的能量消耗,提高网络寿命。     

Study on the algorithm for smart community sensor network routing with adaptive optimization via cluster head election

To reduce the uneven energy consumption for the data transmission and extend network life of intelligent community sensor network, an adaptive routing optimized algorithm for intelligent community sensor networks with cluster head election is proposed. In this algorithm, a three-dimensional clustering method adapted to the structure of intelligent community sensor network is proposed. The three-dimensional clustering method uses the cluster head election mechanism based on minimizing the total transmission loss to optimize the energy of the intelligent community sensor network. Second, an adaptive ant colony propagation method is proposed to solve the problem of intercluster data propagation after clustering. With the best path finding algorithm of ant colony algorithm, energy balance routing with lower energy loss and lower packet error rate is proposed. Finally, the simulation results show that the algorithm has better performance in reducing energy consumption and delay, improving transmission efficiency and node survival time.     

有限马尔可夫链的水声传感器网络协作中继算法

针对水下传感器网络误码率高,能量效率低等问题,基于有限马尔可夫链状态空间分析,提出一种水声传感器网络协作中继算法。该算法采用马尔可夫链状态空间获取协作节点的误码率和能量的状态转移概率。基于能量策略对中转节点进行判定,使网络优先保障对已采集的数据进行传输,提升传输效率。提出基于最佳中继选择的协作节点状态评价函数,使网络优先选择评价结果最高的协作节点作为转发节点,减少数据传输过程中的误码率和能量损耗。实验仿真结果表明,该算法相比基于增强型能源平衡数据传输的水声网络协议及水下网络自适应路由协议,数据包平均成功投递率分别提升了2.3%和3.1%,网络能量效率分别提升了10.6%和5.8%,在提升数据传输效率和减少网络能耗上具有较好效果。     

基于信标优化的无线传感网络定位算法研究

针对传统无线传感器网络节点定位精度低、能耗大及适应性不强等缺陷,提出了一种基于信标优化的无线传感网络定位算法ConDV-Hop.该算法采用贡献因子对信标节点进行优化选择,使过程累积误差大大减少,利用反馈思想引入修正系数对待定位节点的初始估算位置进行修正,有效地克服了定位精度对网络拓扑的依赖.仿真实验结果表明,ConDVHop算法在均匀网络和非均匀网络中都表现出良好的性能,是无线传感器网络中节点定位的一种实用方案.     

成簇无线传感网中能量高效的协作通信

在成簇无线传感器网络中,会出现极大簇和极小簇并存的现象,从而导致整个网络的能量消耗不均衡,进而降低网络性能。通过采用邻近极大极小簇协作发送的方案,极大地均衡了网络的能量消耗,提高了通信的可靠性。仿真结果表明,基于邻近极大极小簇的协作发送方案是一种高能效协作方案,能够很好地均衡网络能量消耗,在延长网络生存周期方面具有更优越的性能。     

基于无线传感器网络的同时定位与跟踪

研究无线传感器网络在位置信息不确定时,同时定位无线传感器网络节点并跟踪移动目标。利用RSSI测量节点对之间的距离,多维定标技术根据距离矩阵完成传感器网络的初始定位。估计与更新阶段提出了压缩EKF滤波确定传感器节点位置和目标位置。仿真结果显示:算法在较低的网络覆盖率下有较高的定位和跟踪准确度,在初始定位误差为5m时,节点和跟踪误差均小于3m,特别是在长距离的跟踪任务中有很好的精度和实时性。