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.     

环境能量驱动的无线传感器节点数据传输优化机制

将环境能量收集模块引入无线传感器网络,可以有效改善网络的使用寿命,但是能量的随时补充也给原有的能量管理策略带来了巨大挑战.针对单个环境能量驱动节点在数据序列已经到达的情况下,研究了优化的数据传输策略,采用内罚函数法优化传输过程,使得固定时间内节点传输的数据量最大.理论分析及仿真结果表明,当各个时刻节点的传输功率比较接近时会获得最大的数据传输量;当实际节点获得最大数据传输时节点的传输功率随时间呈增大趋势.     

Novel sensor scheduling and energy-efficient quantization for tracking target in wireless sensor networks

This paper focuses on sensor scheduling and information quantization issues for target tracking in wireless sensor networks (WSNs). To reduce the energy consumption of WSNs, it is essential and effective to select the next tasking sensor and quantize the WSNs data. In existing works, sensor scheduling’ goals include maximizing tracking accuracy and minimizing energy cost. In this paper, the integration of sensor scheduling and quantization technology is used to balance the tradeoff between tracking accuracy and energy consumption. The main characteristic of the proposed schemes includes a novel filtering process of scheduling scheme, and a compressed quantized algorithm for extended Kalman filter (EKF). To make the algorithms more efficient, the proposed platform employs a method of decreasing the threshold of sampling intervals to reduce the execution time of all operations. A real tracking system platform for testing the novel sensor scheduling and the quantization scheme is developed. Energy consumption and tracking accuracy of the platform under different schemes are compared finally.     

Energy-balanced multiple-sensor collaborative scheduling for maneuvering target tracking in wireless sensor networks

An energy-balanced multiple-sensor collaborative scheduling is proposed for maneuvering target tracking in wireless sensor networks (WSNs). According to the position of the maneuvering target, some sensor nodes in WSNs are awakened to form a sensor cluster for target tracking collaboratively. In the cluster, the cluster head node is selected to implement tracking task with changed sampling interval. The distributed interactive multiple model (IMM) filter is employed to estimate the target state. The estimat...     

面向目标跟踪的传感器网络睡眠调度协议

移动目标跟踪是无线传感器网络中的一项重要应用,将睡眠调度机制引入到目标跟踪算法中可以大大降低能耗。针对目标跟踪的实际需求,提出一种面向目标跟踪的传感器网络睡眠调度协议。根据目标跟踪不同阶段,分别设计了目标跟踪前和跟踪过程中传感器节点的睡眠调度机制;另外给出了目标丢失时,如何唤醒节点继续跟踪目标的调度策略。结果表明:该算法能够在保证跟踪质量的同时,降低跟踪能耗。     

Comparison of energy harvesting systems for wireless sensor networks

Wireless sensor networks (WSNs) offer an attractive solution to many environmental, security, and process monitoring problems. However, one barrier to their fuller adoption is the need to supply electrical power over extended periods of time without the need for dedicated wiring. Energy harvesting provides a potential solution to this problem in many applications. This paper reviews the characteristics and energy requirements of typical sensor network nodes, assesses a range of potential ambient energy sources, and outlines the characteristics of a wide range of energy conversion devices. It then proposes a method to compare these diverse sources and conversion mechanisms in terms of their normalised power density.     

基于能量优化的无线传感器网络动态分簇目标跟踪

针对无线传感器网络动态分簇目标跟踪中的数据碰撞与簇首选择过程导致能耗过高问题,提出一种基于能量优化的无线传感器网络动态分簇方法。首先,构建时分竞选传输模型,主动避免动态簇内数据碰撞,降低节点能耗;然后,基于能量信息与跟踪质量,提出能量均衡的最远节点调度策略,优化簇头节点调度;最后,根据加权质心定位算法,完成目标跟踪任务。实验结果表明：在节点随机部署的环境下,所提方法对于非线性运动目标的平均跟踪精度为0.65 m,与多目标跟踪动态簇员选择方法（DCMS）相当,比分布式事件定位动态分簇目标跟踪算法（DELTA）提高了45.8%;能量消耗方面,与DCMS和DELTA相比,所提方法的动态跟踪簇能量消耗有效降低了61.1%,延长了网络寿命。     

Improved clustering algorithms for target tracking in wireless sensor networks

In recent years, there has been a growing interest in wireless sensor networks because of their potential usage in a wide variety of applications such as remote environmental monitoring and target tracking. Target tracking is a typical and substantial application of wireless sensor networks. Generally, target tracking aims basically at estimating the location of the target while it is moving within an area of interest and consequently report it to the base station in a timely manner. However, achieving a high accuracy of tracking together with energy efficiency in target tracking algorithms is extremely challenging. In this article, we propose two algorithms to enhance the adaptive-head clustering algorithm, formerly lunched, namely, the improved adaptive-head and improved prediction-based adaptive head. Particularly, the first algorithm uses dynamic clustering to achieve impressive tracking quality and energy efficiency through optimally choosing the cluster head that participates in the tracking process. On the other hand, the second algorithm incorporates a prediction mechanism to the first proposed algorithm. Our proposed algorithms are simulated using Matlab considering various network conditions. Simulation results show that our proposed algorithms can accurately track a target, even when random moving speeds are considered and consume much less energy, when compared with the previous algorithm for target tracking, which in turn prolong the network lifetime much more.     

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。     

Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks

Due to the severe resource constraints in wireless sensor networks (WSNs), designing an efficient target tracking algorithm for WSNs in terms of energy efficiency and high tracking quality becomes a challenging issue. WSNs usually provide centralized information, e.g., the locations and directions of a target, choosing sensors around the target, etc. However, some ready strategies may not be used directly because of high communication costs to get the responses for tracking tasks from a central server and low quality of tracking. In this paper, we propose a fully distributed algorithm, an auction-based adaptive sensor activation algorithm (AASA), for target tracking in WSNs. Clusters are formed ahead of the target movements in an interesting way where the process of cluster formation is due to a predicted region (PR) and cluster members are chosen from the PR via an auction mechanism. On the basis of PR calculation, only the nodes in the PR are activated and the rest of the nodes remain in the sleeping state. To make a trade-off between energy efficiency and tracking quality, the radius of PR and the number of nodes are adaptively adjusted according to current tracking quality. Instead of fixed interval (usually used in existing work), tracking interval is also dynamically adapted. Extensive simulation results, compared to existing work, show that AASA achieves high performance in terms of quality of tracking, energy efficiency, and network lifetime.     

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。     

基于灰色马尔可夫模型的无线传感器网络目标跟踪算法

基于预测的目标跟踪是无线传感器网络目标定位与跟踪中很重要的一种方法,但在实际环境中由于目标运动模式的动态变化等原因,传统预测算法对目标位置的预测往往不准确。为了克服以上不足,提出了一种基于灰色马尔可夫模型的目标跟踪（GMMTT）算法。将具有震荡特点的Markov模型引入到分段灰色预测中,使目标定位既能获得较好的精度,又能适应目标运动模式的变化。预测得到目标位置后,当前跟踪节点将跟踪信息传送到目标下一时刻将要到达的预测区域,提前唤醒该区域内的节点,用尽可能少的节点实现目标有效的跟踪。实验结果表明：GMMTY算法在跟踪概率和跟踪精度方面具有较好的性能。     

Decision-aware data suppression in wireless sensor networks for target tracking applications

Target tracking applications of wireless sensor networks (WSNs) may provide a high performance only when a reliable collection of target positions from sensor nodes is ensured. The performance of target tracking in WSNs is affected by transmission delay, failure probability, and nodes energy depletion. These negative factors can be effectively mitigated by decreasing the amount of transmitted data. Thus, the minimization of data transfers from sensor nodes is an important research issue for the development of WSN-based target tracking applications. In this paper, a data suppression approach is proposed for target chasing in WSNs. The aim of the considered target chasing task is to catch a moving target by a mobile sink in the shortest time. According to the introduced approach, a sensor node sends actual target position to the mobile sink only if this information is expected to be useful for minimizing the time in which target will be caught by the sink. The presented method allows sensor nodes to evaluate the usefulness of sensor readings and select those readings that have to be reported to the sink. Experiments were performed in a simulation environment to compare effectiveness of the proposed approach against state-of-the-art methods. Results of the experiments show that the presented suppression method enables a substantial reduction in the amount of transmitted data with no significant negative effect on target chasing time.     

Collaborative signal processing for target tracking in distributed wireless sensor networks

Target tracking, especially visual target tracking, in complex situations is challenging, which is always performed in single-view system. Because of the conflict between resolution and tracking range, however, single-view tracking is not robust and accurate. This paper presents a distributed multi-view tracking system using collaborative signal processing (CSP) in distributed wireless sensor networks (DWSNs). In the proposed tracking system, target detection and classification algorithms are based on single-node processing and target tracking is performed in sink node, whereas target localization algorithm is carried out by CSP between multisensor. For conquering the disadvantages of client/server based centralized data fusion, a progressive distributed data fusion are proposed. Finally, an indoor target tracking experiment is illustrated, and then tracking performance, execution time and energy consumption of progressive distributed data fusion are compared with client/server based centralized data fusion. Experimental results demonstrate that the CSP based distributed multi-view tracking system in DWSNs can accomplish multi-target extraction, classification, localization, tracking and association quickly and accurately with little congestion, energy consumption and execution time.     

基于无线传感器网络的压电能量收集技术

随着物联网的发展,多节点的传感器供电成为关键问题,由于环境中普遍存在低频振动,采用了压电悬臂梁结构,建立压电悬臂梁结构的电学模型,并进行了ANSYS的仿真,仿真得到电压76 V,约等于模型的理论值,验证了模型的正确性。进而继续研究了压电悬臂梁几何尺寸对固有频率的影响,振子越长,质量块越长,频率越低。从而收集低频振动环境中的能量,为传感器供电装置提供了设计的理论依据。     

无线传感器网络中移动目标的定位与跟踪

对侵入无线传感器网络中的目标,提出了一种移动节点和静态节点相结合的定位与跟踪方式.静态节点可以发现侵入传感器网络中的目标,移动节点与静态节点配合进一步确定目标的具体位置.仿真实验验证表明:该方法可以减少大规模的频繁移动节点,不需要过多地对移动节点的选择和运动进行特别复杂的计算,具有较好的定位精度和鲁棒性,对多目标的定位与跟踪研究有一定的启发作用.     

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...     

Self-organization of unattended wireless acoustic sensor networks for ground target tracking

A distributed, self-organization algorithm for ground target tracking using unattended acoustic sensor network is developed. Instead of using microphone arrays, each sensor node in the sensor network uses only a single microphone as its sensing device. This design can greatly reduce the size and cost of each sensor node and allow more flexible deployment of the sensor network. The self-organization algorithm presented in this paper can dynamically select proper sensor nodes to form the localization sensor groups that can work as a virtual microphone array to perform energy efficient target localization and tracking. To achieve this, we use a time-delay based bearing estimation plus triangulation for source localization in the sensor network. Major error sources of the localization method like time delay estimation, bearing calculation and triangulation are analyzed and sensor selection criteria are developed. Based on these criteria and neighborhood information of each sensor node, a distributed self-organization algorithm is developed. Simulation results show the effectiveness of the proposed algorithm.     

无线传感器网络目标跟踪动态簇成员调度策略

通过分析目标跟踪无线传感器网络监测精度、节点能量消耗与簇成员唤醒/休眠之间的内在联系,针对网络节点能量有限、密集部署节点监测数据存在冗余、传感器节点的自身位置估计误差和目标监测估计误差等问题,引入部分可观察Markov决策过程(POMDP)理论,提出一种基于目标跟踪准确度和节点能量消耗加权回报率的动态簇成员调度模型;针对动态簇成员调度算法复杂度偏高的问题,采用基于信念点的值迭代在线策略求解算法,实现传感器簇成员节点协作策略的动态生成和在线调整。仿真结果表明:该算法能够提高目标跟踪准确性,降低节点能量消耗,延长网络生存时间。