Adaptive compressive sensing for target tracking within wireless visual sensor networks-based surveillance applications

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim is to design low power WVSN surveillance application using adaptive Compressive Sensing (CS) which is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In this paper, an adaptive block CS technique is proposed and implemented to represent the high volume of captured images in a way for energy efficient wireless transmission and minimum storage. Furthermore, to achieve energy-efficient target detection and tracking with high detection reliability and robust tracking, to maximize the lifetime of sensor nodes as they can be left for months without any human interactions. Adaptive CS is expected to dynamically achieve higher compression rates depending on the sparsity nature of different datasets, while only compressing relative blocks in the image that contain the target to be tracked instead of compressing the whole image. Hence, saving power and increasing compression rates. Least mean square adaptive filter is used to predicts target’s next location to investigate the effect of CS on the tracking performance. The tracking is achieved in both indoor and outdoor environments for single/multi targets. Results have shown that with adaptive block CS up to 20 % measurements of data are required to be transmitted while preserving the required performance for target detection and tracking.     

Energy efficient surveillance system using WVSN with reweighted sampling in modified fast Haar wavelet transform domain

Wireless visual sensor network (WVSN) consists of a large number of nodes that are capable of acquiring, compressing and transmitting images. Surveillance becomes a vital application area of WVSN as they can be deployed in various environments to monitor and collect information. The lifetime of the nodes in the network depends on the energy consumption. Hence in this paper, block compressed sensing (BCS) based image transmission technique that utilizes Energy based Reweighted sampling (ERWS) in Modified Fast Haar Wavelet Transform (MFHWT) domain is proposed to reduce energy consumption considerably. Sparse binary random matrix is used to acquire CS measurements in MFHWT domain. In addition, the proposed technique also maintains the image quality. The developed algorithm is applied and tested for a car parking lot monitoring system. It is evident from the simulation results that the proposed method achieves better PSNR values even for fewer measurements. Experimental analysis is performed using Atmega 128 processor of Mica mote in WinAVR by Atmel. The proposed method has approximately 85.5% lesser energy consumption than other Compressed Sensing (CS) methods. Lossless Entropy Coding is applied to the ERWS measurements and considerable reduction in number of transmitted bits is also achieved. The algorithm has also been tested in WINGZ mote in real time.     

基于CS优化的双簇头分簇路由算法研究

为了减少无线传感器网络(WSNs)分簇路由中簇头的能量消耗,提出了一种基于布谷鸟搜索(CS)优化的双簇头分簇路由算法.CS通过采用节点的剩余能量和节点之间的位置关系来构造适应值函数并选举出最优双簇头.其中,主簇头将数据进行融合,副簇头将融合的数据发送给基站,缓解了以往单簇头同时负责数据融合和传输的双重压力,使得整体能耗在各个节点的分配更均衡.仿真实验表明:与LEACH算法、粒子群优化(PSO)算法相比,CS算法在减小网络能耗以及延长网络生存周期上更具优势.     

一种基于自适应代表节点选择的WSN数据收集方法

采用压缩感知的无线传感网络数据收集方法要求每个节点都参与数据收集,会造成很大的能量浪费.本文提出了一种基于自适应代表节点选择的WSN数据收集方法,在保证压缩感知数据重构精度的同时,减少参与数据收集的节点数.首先,采用主成分分析和混合压缩感知相结合的办法设计稀疏基;然后,通过分析稀疏基的框架势FP(Frame Potential)设计压缩感知的稀疏观测矩阵,从而选择代表节点,以减少参与数据收集的节点数目;最后,根据Sink处数据重构精度,自适应调整稀疏观测矩阵以用作下一时刻数据收集,从而保证数据收集的重构精度.仿真结果表明,该方法有效的降低了网络能耗和数据传输量,同时还保证了每个时刻数据重构的精度.     

A comparative analysis of video codecs for multihop wireless video sensor networks

Wireless video sensor networks (WVSNs) have drawn significant attention in recent years due to the advent of low-cost miniaturized cameras, which makes it feasible to realize large-scale WVSNs for a variety of applications including security surveillance, environmental tracking, and health monitoring. However, the conventional video coding paradigms are not suitable for WVSNs due to resource constraints such as limited computation power, battery energy, and network bandwidth. In this paper, we evaluated and analyzed the performance of video codecs based on emerging video coding paradigms such as distributed video coding and distributed compressive video sensing for multihop WVSNs. The main objective of this work was to provide an insight into the computational (encoding/decoding) complexity, energy consumption, node and network lifetime, processing and memory requirements, and the quality of reconstruction of these video codecs. Based on the findings, this paper also provides some guidelines for the selection of appropriate video codecs for a given WVSN application.     

Similarity based image selection with frame rate adaptation and local event detection in wireless video sensor networks

Wireless Video Sensor Networks (WVSNs7unding environmental information. Those sensor nodes can locally process the information and then wirelessly transmit it to the coordinator and to the sink to be further processed. As a consequence, more abundant video and image data are collected. In such densely deployed networks, the problem of data redundancy arises when information are gathered from neighboring nodes. To overcome this problem, one important enabling technology for WVSN is data aggregation, which is essential to be cost-efficient. In this paper, we propose a new approach for data aggregation in WVSN based on images and shot similarity functions. It is deployed on two levels: the video-sensor node level and the coordinator level. At the sensor node level the proposed algorithms aim at reducing the number of frames sensed by the sensor nodes and sent to the coordinator. At the coordinator level, after receiving shots from different neighbouring sensor nodes, the similarity between these shots is computed to eliminate redundancies and to only send the frames which meet a certain condition to the sink. The similarity between shots is evaluated based on their color, edge and motion information. We evaluate our approach on a live scenario and compare the results with another approach from the literature in terms of data reduction and energy consumption. The results show that the two approaches have a significant data reduction to reduce the energy consumption, thus our approach tends to overcome the other one in terms of reducing the energy consumption related to the sensing process, and to the transmitting process while guaranteeing the detection of all the critical events at the node and the coordinator levels.

     

Multiple target localization in wireless visual sensor networks

Target localization is an important service in wireless visual sensor networks (WVSN). Although the problem of single target localization has been intensively studied, few consider the problem of multiple target localization without prior target information in WVSN. In this paper, we first investigate the architecture of WVSN where data transmission is reduced to only target positions. Since target matching is a key issue in the multiple target localization, we propose a statistical method to match corresponding targets to located targets in world coordinates. In addition, we also consider scenarios where occlusion or limited field of view (FOV) occurs. The proposed method utilizes target images to the greatest extent. Our experimental results show that the proposed method obtains a more accurate result in targets localization compared with the camera discard scheme, and saves significant amounts of energy compared with other feature matching schemes.     

Anomaly detection in large-scale data stream networks

This paper addresses the anomaly detection problem in large-scale data mining applications using residual subspace analysis. We are specifically concerned with situations where the full data cannot be practically obtained due to physical limitations such as low bandwidth, limited memory, storage, or computing power. Motivated by the recent compressed sensing (CS) theory, we suggest a framework wherein random projection can be used to obtained compressed data, addressing the scalability challenge. Our theoretical contribution shows that the spectral property of the CS data is approximately preserved under a such a projection and thus the performance of spectral-based methods for anomaly detection is almost equivalent to the case in which the raw data is completely available. Our second contribution is the construction of the framework to use this result and detect anomalies in the compressed data directly, thus circumventing the problems of data acquisition in large sensor networks. We have conducted extensive experiments to detect anomalies in network and surveillance applications on large datasets, including the benchmark PETS 2007 and 83 GB of real footage from three public train stations. Our results show that our proposed method is scalable, and importantly, its performance is comparable to conventional methods for anomaly detection when the complete data is available.     

基于均匀分簇的无线传感器网络寿命最大化理论分析

无线传感器网络路由协议的一个重要目标是均衡节点能量消耗并延长网络寿命.基于均匀分簇的无线传感器网络模型,对网络寿命最大化进行了理论分析.在此基础上,提出了一种改进的数据传输方式,有效地均衡了节点的能量消耗,并显著地延长了网络寿命.模拟实验表明,在网络半径一定时,通过合理地选取最优的簇半径,可以最大化网络寿命.改进数据传输方式后的最大网络寿命比改进前的提高了13%.最后,结合该领域当前研究现状,指出了基于分簇路由协议的最大网络寿命未来的研究重点.     

Data censoring with network lifetime constraint in wireless sensor networks

In wireless sensor networks (WSNs), senor nodes are usually battery-powered with limited energy budget. The network lifetime is directly related to the energy consumption of each node. Online censoring is an effective approach to reduce the overall energy consumption by only transmitting statistical informative data. However, the network lifetime is not proportionally extended with online censoring, since individual sensor may still suffer from energy shortage due to frequent transmission of informative data or transmission over long distance. In this paper, a parameters estimation problem is considered in WSNs, where the goal is to minimize the estimation error under the network lifetime constraint. Two censoring algorithms are developed, which allow sensor nodes to make decisions locally on whether to transmit the sampled data. The proposed algorithms can extend the network lifetime with little performance loss. Simulation results validate their effectivenesses.     

A two-hop clustered image transmission scheme for maximizing network lifetime in wireless multimedia sensor networks

In traditional wireless sensor networks, normal sensor nodes which measure scalar physical phenomena like temperature, pressure and humidity usually compress the data before sending them out to minimize the communication energy consumption. However, this strategy may not be suitable for image transmission in wireless multimedia sensor networks. In the traditional clustering structure, when the camera-equipped node or the cluster head compresses the images, an energy hole will appear. This is a key factor that affects the lifetime of the network. To avoid the energy hole problem, a two-hop clustered image transmission scheme is proposed in this paper. In the proposed scheme, many redirectors are used to compress and forward the images for the purpose of reducing energy consumption of the camera-equipped node and the cluster head. With adaptive adjustment of the transmission radius in the camera cluster and tasks allocation based on the residual energy of the normal sensor nodes by the camera-equipped node, the energy consumption of the nodes in the network is balanced. The experimental results show that the proposed scheme can prolong the network lifetime dramatically in the case of the sensor nodes deployed densely.     

Lifetime extension for surveillance wireless sensor networks with intelligent redeployment

For wireless sensor networks (WSNs), uneven energy consumption is a major problem. A direct consequence of this is the energy hole problem, formation of sensing voids within the network field due to battery depleted sensors in the corresponding region. Hole formations are inherent in the network topology, yet it is possible to develop strategies to delay the hole formations to later stages of the network operation and essentially extend the network lifetime without sensing quality loss. In this work, we initially propose and analyze an approach that can be used to mitigate the hole problem. The approach is presented in detail and the effects on the sustained surveillance quality are presented. The results are based on simulations with different network configurations with realistic sensor models, MAC and routing protocols. By using the proposed approach, sustaining a sensing quality above a given threshold and more than doubling the network lifetime are possible. The results clearly indicate the suitability of the approach for especially demanding WSNs such as the ones used for border surveillance tasks.     

Adaptive image compression technique for wireless sensor networks

When using wireless sensor networks for real-time image transmission, some critical points should be considered. These points are limited computational power, storage capability, narrow bandwidth and required energy. Therefore, efficient compression and transmission of images in wireless sensor network is considered. To address the above mentioned concerns, an efficient adaptive compression scheme that ensures a significant computational and energy reduction as well as communication with minimal degradation of the image quality is proposed. This scheme is based on wavelet image transform and distributed image compression by sharing the processing of tasks to extend the overall lifetime of the network. Simulation results are presented and they show that the proposed scheme optimizes the network lifetime, reduces significantly the amount of the required memory and minimizes the computation energy by reducing the number of arithmetic operations and memory accesses.     

基于阿基米德螺线的双汇聚节点重定位策略

在无线传感器网络中,传感器节点间不平衡的能耗会产生能量空洞问题,而汇聚节点的重定位可以有效地平衡能耗,提高网络寿命.与单汇聚节点相比多汇聚节点具有减小多跳传输能耗,提高数据传输效率等优势,因此本论文提出了一种基于阿基米德螺线的多汇聚节点重定位(ATSR)策略,该策略利用阿基米德螺线具有恒定的线速度和角速度,使多汇聚节点同时沿着对称的两条阿基米德螺线运动的方式来提升网络生存周期,并针对该策略因数据收集间隔导致的额外能耗问题,提出了性能更好的策略(IATSR).实验结果表明,与其他汇聚节点重定位算法相比,本策略具有更好的性能.     

Balancing Energy Consumption to Maximize Network Lifetime in Data-Gathering Sensor Networks

Unbalanced energy consumption is an inherent problem in wireless sensor networks characterized by multihop routing and many-to-one traffic pattern, and this uneven energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of maximizing network lifetime through balancing energy consumption for uniformly deployed data-gathering sensor networks. We formulate the energy consumption balancing problem as an optimal transmitting data distribution problem by combining the ideas of corona-based network division and mixed-routing strategy together with data aggregation. We first propose a localized zone-based routing scheme that guarantees balanced energy consumption among nodes within each corona. We then design an offline centralized algorithm with time complexity O(n) (n is the number of coronas) to solve the transmitting data distribution problem aimed at balancing energy consumption among nodes in different coronas. The approach for computing the optimal number of coronas in terms of maximizing network lifetime is also presented. Based on the mathematical model, an energy-balanced data gathering (EBDG) protocol is designed and the solution for extending EBDG to large-scale data-gathering sensor networks is also presented. Simulation results demonstrate that EBDG significantly outperforms conventional multihop transmission schemes, direct transmission schemes, and cluster-head rotation schemes in terms of network lifetime.     

异构监测传感器网络寿命最大化模型及其求解

对于有两类无线传感器节点组成的异构监测网络,给出了考虑连通覆盖约束条件的求解网络寿命的最优化模型;根据KKT条件,最优解处的不等式约束可以转化为等式约束,得到了模型的最优解,该最优解对于实时调整传感器网络的感知/发射半径具有很强的指导意义.数值结果表明,网络最大寿命值随传感器的感知/发射半径的增加而减小;同时传感器发射半径的调整,可以提高网络寿命.     

无线视觉传感器网络的节点设计

无线视觉传感器网络将监测的环境信息拓展到视觉层面,实现更精细化的监测,引发国内外研究人员的诸多关注.针对无线视觉传感器网络的特殊性,以高性能嵌入式处理器为核心设计了无线视觉传感器节点硬件,包括微处理器模块、无线收发模块、视觉信息采集模块、电源管理模块等;软件程序支持采集环境视频并进行压缩处理,获取节点间距离和节点剩余电量参数,支持自组建Ad-hoc网络进行节点间无线通信、视频传输和节点参数交换等.实验验证表明,所设计的无线视觉传感器节点运行正常,节点间距离测算以及剩余电量获取满足传感器网络应用需求.     

移动传感器网络中能量均衡分簇及移动策略

无线传感器网络各节点能量有限,如果数据收集节点（Sink）能够移动,则可以大大节约节点能量,从而延长网络的寿命。首先提出一种能量均衡的分簇算法,根据节点地理信息进行分簇,使得节点耗费总能量尽可能小的同时,使各簇能量消耗基本平衡;在此基础上提出一种Sink 移动策略,Sink 优先选择能量较充足的簇收集信息。仿真结果表明,与传统的随机移动算法相比,提出的算法能够显著平衡各族之间的能量消耗,并减少总的网络能量消耗,从而提高网络的寿命。     

Detecting moving objects, ghosts, and shadows in video streams

Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. The article proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.     

一种基于功率控制的WSN自适应能量高效传输模式研究

无线传感器网络的能耗决定了网络的生存时间。本文提出了一种基于功率控制WSN自适应能量高效传输模式。分析了在功率控制方案中传输功率损耗与温度之间的关系。使用开环控制去补偿由于温度引起的链路质量的变化。通过结合开环温度感知的补偿与闭环反馈控制,可以在无线传感网络中显著降低传输功率控制的开销。仿真结果表明,与传统的在单一地域最大化增强发送功率来补偿温度的变化相比,该方案能够有效地适应传输链路质量的变化,并且有较少的控制报文开销。