无线传感器网络自适应目标跟踪节点调度算法

在无线传感器网络中,设计合理的节点调度算法是提高网络感知能力、降低系统能耗的关键。在分析节点能耗模型的基础上,针对移动目标跟踪型网络应用,提出一种高能效的无线传感器网络自适应节点调度算法ANSTT。该算法根据节点对移动目标的感知能力,以及节点的相对剩余能量水平,自动调整节点工作模式。仿真实验表明,ANSTT算法在维持低感知延时、高目标感知率的同时,可有效降低系统能耗,延长网络寿命。     

Securing a wireless sensor network for human tracking: a review of solutions

Currently, wireless sensor networks (WSNs) are formed by devices with limited resources and limited power energy availability. Thanks to their cost effectiveness, flexibility, and ease of deployment, wireless sensor networks have been applied to many scenarios such as industrial, civil, and military applications. For many applications, security is a primary issue, but this produces an extra energy cost. Thus, in real applications, a trade‐off is required between the security level and energy consumption. This paper evaluates different security schemes applied to human tracking applications, based on a real‐case scenario. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive quantized target tracking in wireless sensor networks

This paper addresses target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose to improve the use of the variational filtering (VF) by optimally quantizing the data collected by the sensors. Recently, VF has been proved to be suitable to the communication constraints of WSN. Its efficiency relies on the fact that the online update of the filtering distribution and its compression are executed simultaneously. However, this problem has been used only for binary sensor networks neglecting the transmission energy consumption in a WSN and the information relevance of sensor measurements. Our proposed method is intended to jointly estimate the target position and optimize the quantization level under fixed and variable transmitting power. At each sampling instant, the adaptive method provides not only the estimate of the target position by using the VF but gives also the optimal number of quantization bits per observation. The adaptive quantization is achieved by minimizing the predicted Cramér–Rao bound if the transmitting power is constant for all sensors, and optimizing the power scheduling under distortion constraint if this power is variable. The computation of the predicted Cramér–Rao bound is based on the target position predictive distribution provided by the VF algorithm. The proposed adaptive quantization scheme suggests that the sensors with bad channels or poor observation qualities should decrease their quantization resolutions or simply become inactive in order to save energy.     

Energy-efficient collaborative target tracking algorithm using cost-reference particle filtering in wireless acoustic sensor networks

Target tracking is one of the most important applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of sensor nodes. A new robust and energy-efficient collaborative target tracking framework is proposed in this article. After a target is detected, only one active cluster is responsible for the tracking task at each time step. The tracking algorithm is distributed by passing the sensing and computation operations from one cluster to another. An event-driven cluster reforming scheme is also proposed for balancing energy consumption among nodes. Observations from three cluster members are chosen and a new class of particle filter termed cost-reference particle filter (CRPF) is introduced to estimate the target motion at the cluster head. This CRPF method is quite robust for wireless sensor network tracking applications because it drops the strong assumptions of knowing the probability distributions of the system process and observation noises. In simulation experiments, the performance of the proposed collaborative target tracking algorithm is evaluated by the metrics of tracking precision and network energy consumption.     

Optimal sensor and path selection for target tracking in wireless sensor networks

This paper addresses target tracking in wireless sensor networks where the nonlinear observed system is assumed to progress according to a probabilistic state space model. Thus, we propose to improve the use of the quantized variational filtering by jointly selecting the optimal candidate sensor that participates in target localization and its best communication path to the cluster head. In the current work, firstly, we select the optimal sensor in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor networks. This selection is also based on the local cluster node density and their transmission power. Secondly, we select the best communication path that achieves the highest signal‐to‐noise ratio at the cluster head; then, we estimate the target position using quantized variational filtering algorithm. The best communication path is designed to reduce the communication cost, which leads to a significant reduction of energy consumption and an accurate target tracking. The optimal sensor selection is based on mutual information maximization under energy constraints, which is computed by using the target position predictive distribution provided by the quantized variational filtering algorithm. The simulation results show that the proposed method outperforms the quantized variational filtering under sensing range constraint, binary variational filtering, and the centralized quantized particle filtering. Copyright © 2012 John Wiley & Sons, Ltd.     

Reliable and energy aware query-driven routing protocol for wireless sensor networks

Wireless sensor networks become very attractive in the research community, due to their applications in diverse fields such as military tracking, civilian applications and medical research, and more generally in systems of systems. Routing is an important issue in wireless sensor networks due to the use of computationally and resource limited sensor nodes. Any routing protocol designed for use in wireless sensor networks should be energy efficient and should increase the network lifetime. In this paper, we propose an efficient and highly reliable query-driven routing protocol for wireless sensor networks. Our protocol provides the best theoretical energy aware routes to reach any node in the network and routes the request and reply packets with a lightweight overhead. We perform an overall evaluation of our protocol through simulations with comparison to other routing protocols. The results demonstrate the efficiency of our protocol in terms of energy consumption, load balancing of routes, and network lifetime.     

Energy-efficient information routing in sensor networks for robotic target tracking

Target tracking problems have been studied for both robots and sensor networks. However, existing robotic target tracking algorithms require the tracker to have access to information-rich sensors, and may have difficulty recovering when the target is out of the tracker??s sensing range. In this paper, we present a target tracking algorithm that combines an extremely simple mobile robot with a networked collection of wireless sensor nodes, each of which is equipped with an unreliable, limited-range, boolean sensor for detecting the target. The tracker maintains close proximity to the target using only information sensed by the network, and can effectively recover from temporarily losing track of the target. We present two algorithms that manage message delivery on this network. The first, which is appropriate for memoryless sensor nodes, is based on dynamic adjustments to the time-to-live (TTL) of transmitted messages. The second, for more capable sensor nodes, makes message delivery decisions on-the-fly based on geometric considerations driven by the messages?? content. We present an implementation along with simulation results. The results show that our system achieves both good tracking precision and low energy consumption.     

基于无线传感网络的小世界演化研究

提高无线传感网络的传输效率、节约整个网络的能量消耗是我们研究无线传感网络的重要内容。通常都是通过改变网络的拓扑结构来实现效率的提高,本文给出了一种新的思路去节约能耗。本文讨论了权重与与无线传感网传输效率的关系,将有权的无线传感网络链路上的权值进行随机的分配,我们发现将权重随机分配后网络的传输效率得到了提高并且随着权重分布概率的增大网络的传输效率不断增大。这为我们研究权重对无线传感网络的影响提供了基础。     

Target localization and tracking in noisy binary sensor networks with known spatial topology

Target localization and tracking are two of the critical tasks of sensor networks in many applications. Conventional localization and tracking techniques developed for wireless systems that rely on direction‐of‐arrival (DOA) or time‐of‐arrival (TOA) information are not suitable for low‐power sensors with limited computation and communication capabilities. In this paper, we propose a low‐complexity and energy‐efficient method for target localization and tracking in noisy binary sensor networks, where the sensors can only perform binary detection, and the physical links are characterized by additive white Gaussian noise (AWGN) channels. The proposed method is based on known spatial topology. An efficient wake‐up strategy is used to activate a particular group of sensors for cooperative localization and tracking. We analyze the localization error probability and tracking miss probability in the presence of prediction errors. Simulation results validate the theoretic analysis and demonstrate the effectiveness of the proposed approach. Copyright © 2008 John Wiley & Sons, Ltd.     

基于强跟踪滤波的传感器目标跟踪算法

为了提高运动目标的跟踪精度,提出一种基于强跟踪滤波的传感器目标跟踪算法.首先通过传感器节点测量目标的状态值,并通过融合中心对信息进行融合,然后利用Cholesky分解技术变换成噪声独立的量化融合系统,并采用强跟踪滤波算法对目标状态进行估计,最后与其它目标跟踪算法进行对比实验.结果表明,本文算法不仅提高了目标跟踪的精度,而且具有更好的鲁棒性.     

A new QoS routing algorithm based on self-organizing maps for wireless sensor networks

For the past ten years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore and compare the performance of two very well known routing paradigms, directed diffusion and Energy-Aware Routing, with our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes.     

Efficient in-network moving object tracking in wireless sensor networks

The rapid progress of wireless communication and embedded microsensing MEMS technologies has made wireless sensor networks possible. In light of storage in sensors, a sensor network can be considered as a distributed database, in which one can conduct in-network data processing. An important issue of wireless sensor networks is object tracking, which typically involves two basic operations: update and query. This issue has been intensively studied in other areas, such as cellular networks. However, the in-network processing characteristic of sensor networks has posed new challenges to this issue. In this paper, we develop several tree structures for in-network object tracking which take the physical topology of the sensor network into consideration. The optimization process has two stages. The first stage tries to reduce the location update cost based on a deviation-avoidance principle and a highest-weight-first principle. The second stage further adjusts the tree obtained in the first stage to reduce the query cost. The way we model this problem allows us to analytically formulate the cost of object tracking given the update and query rates of objects. Extensive simulations are conducted, which show a significant improvement over existing solutions.     

Collaborative tracking via particle filter in wireless sensor networks

Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of the sensor nodes. A framework and analysis for collaborative tracking via particle filter are presented in this paper Collaborative tracking is implemented through sensor selection, and results of tracking are propagated among sensor nodes. In order to save communication resources, a new Gaussian sum particle filter, called Gaussian sum quasi particle filter, to perform the target tracking is presented, in which only mean and covariance of mixands need to be communicated. Based on the Gaussian sum quasi particle filter, a sensor selection criterion is proposed, which is computationally much simpler than other sensor selection criterions. Simulation results show that the proposed method works well for target tracking.     

An adaptive target tracking method for 3D underwater wireless sensor networks

Today, underwater target tracking using underwater wireless sensor networks (UWSNs) is an essential part in many military and non-military applications. Most of moving target tracking studies in UWSNs are considered in two-dimensional space. However, most practical applications require to be implemented in three-dimensional space. In this paper an adaptive method based on Kalman filter for moving target tracking in three dimensional space using UWSNs is proposed. Since, energy protection is a vital task in UWSNs; the proposed method reduces the energy consumption of the entire network by a sleep/wake plan. In this plan only 60% of the closer nodes along the path of the moving target will be waked up using a sink activation message and participate in the tracking, while the other nodes remain in sleep state. At each stage of tracking, the location of the target is estimated using a 3D underwater target tracking algorithm with the trilateration method. Subsequently, the estimations and target tracking results are inserted into the Kalman filter as measuring model to produce the final result. Performance evaluation and simulations results indicated that the proposed method improves the average location error by 45%, average estimated velocity by 86%, and average energy consumption by 33% in comparison to the trilateration method. However, computation time is increased as a result of improving tracking accuracy; and tracking accuracy is lost about 20% due to saving energy. It was shown that the proposed method has been able to adaptively achieve a trade-off between tracking accuracy and energy consumption based on real-time user requirements. Such adaption can be controlled trough the sink node based on real-time requirements.     

Information Content-Based Sensor Selection and Transmission Power Adjustment for Collaborative Target Tracking

For target tracking applications, wireless sensor nodes provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration among themselves to improve the target localization and tracking accuracies. An energy-efficient collaborative target tracking paradigm is developed for wireless sensor networks (WSNs). A mutual-information-based sensor selection (MISS) algorithm is adopted for participation in the fusion process. MISS allows the sensor nodes with the highest mutual information about the target state to transmit data so that the energy consumption is reduced while the desired target position estimation accuracy is met. In addition, a novel approach to energy savings in WSNs is devised in the information-controlled transmission power (ICTP) adjustment, where nodes with more information use higher transmission powers than those that are less informative to share their target state information with the neighboring nodes. Simulations demonstrate the performance gains offered by MISS and ICTP in terms of power consumption and target localization accuracy.     

Efficient REBTA data reporting algorithm for object tracking in wireless sensor networks

Object tracking is widely referred as one of the most interesting applications of wireless sensor networks (WSNs). This application is able to detect and track objects and report information about these objects to a central base station. One of the major drawbacks in the current research in WSNs is the quality of the data reporting where the major research focus is dedicated to localization of objects; however, few of these works were concentrated on the data reporting. An efficient data reporting algorithm for object tracking in WSNs is proposed in this paper. The main objective of this paper is to enhance the WSN lifetime by achieving both minimum energy and balancing such consumption in sensor nodes during reporting operation. Furthermore, in our model, the enhancement of network reliability is considered. Finally, it reduces the effects of congestion by sufficiently utilizing the under loaded nodes to improve the network throughput. This paper formulates the object tracking problem in large‐scale WSN into 0/1 integer linear programming problem, and then proposes a reliable energy balance traffic aware approach to solve the optimization problem. From the obtained simulation results, the proposed solution has proved to be able to enhance the network performance in network lifetime, throughput, end‐to‐end delay, energy balance, and complexity for both homogeneous and heterogeneous networks.     

用无线传感器网络探测跟踪目标

与传统的探测跟踪方法相比,无线传感器网络以其良好的特性弥补了传统跟踪方法的不足。文章介绍了无线传感器网络的体系结构,探讨了无线传感器网络探测跟踪目标的策略和方法,最后提出了用无线传感器网络跟踪目标需要考虑的问题。     

A battery-less BLE smart sensor for room occupancy tracking supplied by 2.45-GHz wireless power transfer

Wireless power transfer (WPT) has emerged as a solution for supplying smart sensors for long-term battery-less deployment. Because the amount of power harvested by the smart sensor is limited due to WPT path loss, the optimization objective is twofold: achieving ultra-low-power operation for the sensing task and improving the harvesting efficiency even at low incident power. In this paper, we focus on the use case of a Bluetooth LE-connected motion detection system supplied by 2.45-GHz RF power. The full system (RF energy harvester, power management, sensor transducer and interface, control, data processing and wireless transmission) is implemented using low-power off-the-shelf components. In the sensing sub-system, ultra-low-power operation is achieved by the duty-cycling of the sensor interface and by an event-driven scheme for communication. In the harvesting sub-system, the design of the matching network and rectifier, combined with maximum power point tracking (MPPT), is optimized for increasing the power harvesting efficiency (PHE) at low incident power. Measurements show a total reduction in the power consumption for the sensing sub-system by a factor 20. When using custom WPT waveform with high peak-to-average power ratio, the RF energy harvester is functional with an incident RF power starting from −20 dBm. The smart sensor is able to perform its motion-detection task with an incident power as low as −17.3 dBm.     

无线传感网络安全防护路径激光追踪方法

为了提高无线传感网络安全防护能力,需要进行网络安全防护路径设计,提出基于联合节点行为覆盖的无线传感网络安全防护路径激光追踪方法.构建无线传感网络安全防护路径的覆盖关系模型,根据传感器节点与目标节点从属关系进行无线传感网络安全防护的路径空间规划设计,采用最短路径寻优方法进行无线传感网络安全防护路径的激光控制,采用激光扫描...     

一种基于无交集节点分组的无线传感器网络覆盖算法

无线传感器网络是一种无线自组织网络,它由大量能量有限的传感器节点组成.能量消耗和网络覆盖是无线传感器网络的两个核心问题,网络覆盖决定了无线传感器网络对物理世界的监测能力,能量消耗则决定了无线传感器网络的生存时间.本文研究了一种改进的基于无交集节点分组算法,针对随机选取节点实现无交集节点分组方式获得的分组个数少且节点通信...