A survey: localization and tracking mobile targets through wireless sensors network

Wireless sensor network applications have been deployed widely. Sensor networks involve sensor nodes which are very small in size. They are low in cost, and have a low battery life. Sensor nodes are capable of solving a variety of collaborative problems, such as, monitoring and surveillance. One of the critical components in wireless sensor networks is the localizing tracking sensor or mobile node. In this paper we will discuss the various location system techniques and categorize these techniques based on the communication between nodes into centralized and decentralized localization techniques. The tracking techniques are categorized into four main types. Each type will be compared and discussed in detail. We will suggest ways of implementing the techniques and finally carry out an evaluation

Dynamic quantization for multisensor estimation over bandlimited fading channels with fusion center feedback.

This paper considers the state estimation of hidden Markov models(HMMs) in a network of sensors which communicate with the fusion center viafinite symbols by fading channels. The objective is to minimize the long term meansquare estimation error for the underlying Markov chain. By using feedback fromthe fusion center, a dynamic quantization scheme for the sensor nodes is proposedand analyzed by a Markov decision approach. The performance improvement byfeedback, as well as the effect of fading, is illustrated

An improved energy efficient approach for wsn based tracking applications

Tracking systems using a high number of low cost sensor nodes have been proposed for use in diverse applications including civil, military, and wildlife monitoring applications. In tracking applications, each sensor node attempts to send the target's location information to a sink node. Deploying a tracking system with a high number of sensor nodes results in the following limitations: high packet dropping rate, high congestion, transmission delay, and high power-consumption. Data aggregation schemes can reduce the number of messages transmitted over the network, while prediction schemes can decrease the number of activated beacon nodes in the tracking process. Consequently, data aggregation and prediction approaches can reduce the energy consumed during the tracking process. In this paper, we propose and implement an energy efficient approach for WSN-based tracking applications by integrating both a novel data aggregation method with a simple prediction approach. Three metrics are utilized for the evaluation purposes: total number of messages transmitted in the network, overall power-consumption, and the quality of the tracking accuracy. The proposed system is simulated using the NS2 simulation environment

Study on Target Tracking and Distributed Particle Filter in Binary Sensor Networks

To further reduce the wireless sensor target tracking traffic and improving target tracking in real time. This paper describes a distributed particle filter algorithm for target tracking in binary sensor network, this algorithm can be applied to the cluster head replacement process, at this time, only needs error variance and transmission filter value between cluster heads, and no longer need to pass particles. On the other hand, this algorithm can achieve an appropriate adjustment of the filter variance online particle count, thus contributing to gradually reduce the amount of algorithm's calculation

Une ontologie pour la description des intrusions dans les RCSFs.

Session Sécurité RéseauInternational audienceNous proposons une définition formelle d'une intrusion par la mise sur pied d'une ontologie de différentes intrusions dans les Réseaux de Capteurs Sans Fil (RCSF); l'objectif étant d'offrir une classification complète qui prend en compte l'intrusion aussi bien du point de vue de son impact sur le service offert, du point de vue des fonctionnalités implantées dans les protocoles de sécurité, et celui des dysfonctionnements aléatoires dus à un phénomène naturel ou inattendu

Towards optimal energy-quality tradeoff in tracking via sensor networks

Abstract-The paper addresses tracking of a moving target by means of a wireless sensor network. A centralized tracking filter and procedure for selectively activating sensors around the expected target's position are combined. Unlike selective activation methods existing in the literature, which are concerned only with the tracking accuracy, the one considered in this work attempts to tradeoff tracking performance optimization versus lifetime maximization. A performance evaluation via Monte Carlo simulations shows the effectiveness of the proposed approach

Multilevel Object Tracking in Wireless Multimedia Sensor Networks for Surveillance Applications Using Graph-Based Big Data

https://ieeexplore.ieee.org/document/8721634/keywords#keywordsWireless Multimedia Sensor Networks (WMSN), for object tracking, have been used as an emerging technology in different application areas, such as health care, surveillance, and traffic control. In surveillance applications, sensor nodes produce data almost in real-time while tracking the objects in a critical area or monitoring border activities. The generated data is generally treated as big data and stored in NoSQL databases. In this paper, we present a new object tracking approach for surveillance applications developed using a big data model based on graphs and a multilevel fusion. Our approach consists of three main steps: intra-node fusion, inter-node fusion, and object trajectory construction. Intra-node fusion exploits the detection and tracking of objects in each sensor, while inter-node fusion uses spatio-temporal data and neighboring sensors. Then, the fused data of all sensor nodes are combined to construct global trajectories of the detected objects in the monitored area on the WMSN. We implemented a prototype system and evaluated the performance of the proposed approach with both a real dataset and a synthetic dataset. The results of our experiments on the two datasets show that the use of third-level fusion in addition to inter-node and intra-node fusions provides significantly better performance for object tracking in the WMSN applications

Tracking mobile targets through Wireless Sensor Networks

In recent years, advances in signal processing have led to small, low power, inexpensive Wireless Sensor Network (WSN). The signal processing in WSN is different from the traditional wireless networks in two critical aspects: firstly, the signal processing in WSN is performed in a fully distributed manner, unlike in traditional wireless networks; secondly, due to the limited computation capabilities of sensor networks, it is essential to develop an energy and bandwidth efficient signal processing algorithms. Target localisation and tracking problems in WSNs have received considerable attention recently, driven by the necessity to achieve higher localisation accuracy, lower cost, and the smallest form factor. Received Signal Strength (RSS) based localisation techniques are at the forefront of tracking research applications. Since tracking algorithms have been attracting research and development attention recently, prolific literature and a wide range of proposed approaches regarding the topic have emerged. This thesis is devoted to discussing the existing WSN-based localisation and tracking approaches. This thesis includes five studies. The first study leads to the design and implementation of a triangulation-based localisation approach using RSS technique for indoor tracking applications. The presented work achieves low localisation error in complex environments by predicting the environmental characteristics among beacon nodes. The second study concentrates on investigating a fingerprinting localisation method for indoor tracking applications. The proposed approach offers reasonable localisation accuracy while requiring a short period of offline computation time. The third study focuses on designing and implementing a decentralised tracking approach for tracking multiple mobile targets with low resource requirements. Despite the interest in target tracking and localisation issues, there are few systems deployed using ZigBee network standard, and no tracking system has used the full features of the ZigBee network standard. Tracking through the ZigBee is a challenging task when the density of router and end-device nodes is low, due to the limited communication capabilities of end-device nodes. The fourth study focuses on developing and designing a practical ZigBee-based tracking approach. To save energy, different strategies were adopted. The fifth study outlines designing and implementing an energy-efficient approach for tracking applications. This study consists of two main approaches: a data aggregation approach, proposed and implemented in order to reduce the total number of messages transmitted over the network; and a prediction approach, deployed to increase the lifetime of the WSN. For evaluation purposes, two environmental models were used in this thesis: firstly, real experiments, in which the proposed approaches were implemented on real sensor nodes, to test the validity for the proposed approaches; secondly, simulation experiments, in which NS-2 was used to evaluate the power-consumption issues of the two approaches proposed in this thesis

Cooperative tracking with binary-detection sensor networks

We present a novel method for tracking the movement of people or vehicles in open outdoor environments using sensor networks. Unlike other sensor network-based methods, which depend on determining distance to the target or the angle of arrival of the signal, our cooperative tracking approach requires only that a sensor be able to determine if an object is somewhere within the maximum detection range of the sensor. We propose cooperative tracking as a method for tracking moving objects and extrapolating their paths in the short term. By combining data from neighboring sensors, this approach enables tracking with a resolution higher than that of the individual sensors being used. We employ statistical estimation and approximation techniques to further increase the tracking precision, and to enable the system to exploit the tradeoff between accuracy and timeliness of the results. We analyze the behavior of the cooperative tracking algorithm through simulation, focusing on the effects of approximation techniques on the quality of estimates achieved. This work focuses on acoustic tracking, however the presented methodology is applicable to any sensing modality where the sensing range is relatively uniform.