Tracking In Wireless Sensor Networks Research Articles

An Energy Efficient Multi-Target Tracking in Wireless Sensor Networks Based on Polygon Tracking Method

Wireless sensor networks (WSN) have been considered as a promising system for area surveillance applications. As it moves through a sensor network target tracking has become an increasingly important application in Wireless Sensor Networks. Target detection deals with finding spatial coordinates of a moving object and tracking deals with finding the coordinates and being able to track its movements. Tracking mobile targets using sensor networks is a challenging task. Existing work mostly requires organizing groups of sensor nodes with measurements of a target’s movements or accurate distance measurements from the nodes to the target, and predicting those movements. These are, however, often difficult to accurately achieve in practice, especially in the case of unpredictable environments, sensor faults, etc .In such cases the new tracking framework, called FaceTrack, suits best. FaceTracking employs the nodes of a spatial region surrounding a target, called a face. If a target is detected by a node after a time window, a target is detected by another node. It is assumed to be the same target or single object in FaceTracking method. But it achieves better tracking accuracy and energy efficiency for detecting and tracking single object. This paper proposes a new approach for multi target detection using FaceTracking or polygon tracking method. Simulations are carried out to underline the energy efficiency of the proposed method using NS2.

Local Area Prediction-Based Mobile Target Tracking in Wireless Sensor Networks

Tracking mobile targets in wireless sensor networks (WSNs) has many important applications. As it is often the case in prior work that the quality of tracking (QoT) heavily depends on high accuracy in localization or distance estimation, which is never perfect in practice. These bring a cumulative effect on tracking, e.g., target missing. Recovering from the effect and also frequent interactions between nodes and a central server result in a high energy consumption. We design a tracking scheme, named t-Tracking , aiming to achieve two major objectives: high QoT and high energy efficiency of the WSN. We propose a set of fully distributed tracking algorithms, which answer queries like whether a target remains in a “specific area” (called a “face” in localized geographic routing, defined in terms of radio connectivity and local interactions of nodes). When a target moves across a face, the nodes of the face that are close to its estimated movements compute the sequence of the target’s movements and predict when the target moves to another face. The nodes answer queries from a mobile sink called the “tracker”, which follows the target along with the sequence. t-Tracking has advantages over prior work as it reduces the dependency on requiring high accuracy in localization and the frequency of interactions. It also timely solves the target missing problem caused by node failures, obstacles, etc., making the tracking robust in a highly dynamic environment. We validate its effectiveness considering the objectives in extensive simulations and in a proof-of-concept system implementation.

Coordinated and Adaptive Information Collecting in Target Tracking Wireless Sensor Networks

Energy conservation has been known as the most important issue for many applications in wireless sensor networks (WSNs). For data gathering in a clustered sensor network, most existing work focuses on fusing data at cluster heads (CHs). However, in target tracking applications, these approaches do not consider that one target is usually detected by sensor nodes in multiple clusters, which leads to redundant data transmissions from CHs to the sink so as to waste much energy. To solve it, this paper proposes a coordinated and adaptive information collecting strategy (CAICS) for target tracking in clustered WSNs. In CAICS, a coordinated node selection method considering the spatial correlation of sensing nodes is proposed to dynamically select the best set of nodes for tracking missions. Moreover, an adaptive aggregation node is selected for data collection to reduce the redundant data transmission and balance the energy cost. Experimental results demonstrate that the proposed approach reduces the computational complexity of node selections without degrading tracking accuracy and achieves a significant energy consumption reduction and network lifetime extension compared with the state-of-the-art approaches.

Target tracking using Interactive Multiple Model for Wireless Sensor Network

Target tracking in a Wireless Sensor Network (WSN) environment is a challenging research problem. Interactive Multiple Model (IMM) is a popular scheme for accurate target tracking. The existing target tracking scheme used in WSN employs Kalman Filter (KF) which fails to track the target accurately due to non availability of target data at regular intervals and missing of packets. Though existing KF based tracking in WSN scheme detects the target, it fails to identify the target. To overcome these problems, this paper proposes a IMM based Target Tracking in WSN named ITTWSN that uses multiple models (velocity and acceleration) to handle both maneuvering and non maneuvering targets and multiple sensors to detect and identify the targets. The performance of the proposed ITTWSN is compared with the KF scheme and it is found that the accuracy of the proposed ITTWSN is better than the existing KF based approach.

Adaptive Energy Optimization for Object Tracking in Wireless Sensor Network

Energy efficiency is critical for Wireless Sensor Networks (WSNs) since sensor nodes usually have very limited energy supply from battery. Sleep scheduling and nodes cooperation are two of the most efficient methods to achieve energy conservation in WSNs. In this paper, we propose an adaptive energy optimization approach for target tracking applications, called Energy-Efficient Node Coordination (EENC), which is based on the grid structure. EENC provides an unambiguous calculation and analysis for optimal the nodes cooperation theoretically. In EENC, the sleep schedule of sensor nodes is locally synchronized and globally unsynchronized. Locally in each grid, the sleep schedule of all nodes is synchronized by the grid head, while globally the sleep schedule of each grid is independent and is determined by the proposed scheme. For dynamic sleep scheduling in tracking state we propose a multi-level coordination algorithm to find an optimal nodes cooperation of the network to maximize the energy conservation while preserving the tracking performance. Experimental results show that EENC can achieve energy saving of at least 38.2% compared to state-of-the-art approaches.

IJARCCE - Computer and Communication Engineering

2 Abstract: In the proposed paper the major issue is the target tracking in wireless sensor networks and to obtain exact position of the target as to be measured so that we can expect improvement in the tracking resolution. so there is need of finding accurately sensor and targets trajectory path as to be studied with special care and captured at regular basis. We also studied correlation properties and sensitivity in mobile sensors from the system parameters and maintained good resolution for tracking of mobile sensors at different speeds. Our simulation results gave satisfactory system parameters like sensor density, sensor and target mobility compared static sensor network environment.

Compensation strategy for distributed tracking in wireless sensor networks with packet losses

Packet loss is inevitable in wireless sensor networks (WSNs). On the basis of the distributed Kalman filter (DKF) and consensus filter, we propose both biased and unbiased compensation strategies to improve tracking accuracy and reliability for WSNs' packet dropout problem. In the biased compensation strategy, undelivered data from neighbours is compensated by the data of node itself; and in the unbiased compensation strategy, weights of all nodes are updated when packet dropout happens. With these two compensation strategies, DKF can be valid under both mild and poor connectivity conditions. Furthermore, sufficient condition is given to guarantee the convergence of estimation error system. Simulation results show that statistics average estimation errors of the biased and unbiased compensation strategies reduce 37.541 and 37.542 % of state estimation respectively compared to the DKF when the packet loss rate reaches 33.3 %, which demonstrates that the proposed algorithms perform better in filtering packet dropout than the DKF.

Multiple 3D Target Tracking in Binary Wireless Sensor Network

In this paper, 3D target tracking in wireless sensor network (WSN) is considered. Unlike other published work, this paper addresses the problem of estimating locations of different targets in 3D environment. Each sensor makes T observations to get local decisions. These decisions are modulated using on-off keying (OOK) and sent to a central node called fusion center. The fusion center detects all decisions sent from all sensors to process them in order to estimate the targets locations using maximum likelihood estimator. This approach generalizes the existing work in the literature about a single target localization if we considered that the number of targets equal one. The corresponding Cramer-Rao lower bound (CRLB) has been also derived. Furthermore, simulation results are provided and discussed. 

Energy-Efficient Node Scheduling Method for Cooperative Target Tracking in Wireless Sensor Networks

Using the sensor nodes to achieve target tracking is a challenging problem in resource-limited wireless sensor networks. The tracking nodes are usually required to consume much energy to improve the tracking performance. In this paper, an energy-efficient node scheduling method is proposed to minimize energy consumption while ensuring the tracking accuracy. Firstly, the Kalman-consensus filter is constructed to improve the tracking accuracy and predict the target position. Based on the predicted position, an adaptive node scheduling mechanism is utilized to adjust the sample interval and the number of active nodes dynamically. Rather than using traditional search algorithm, the scheduling problem is decomposed to decouple the sample interval and number of nodes. And the node index is mapped into real domain to get closed-form solution to decide the active nodes. Thus, the NP-complete nature is avoided in the proposed method. The proposed scheduling method can keep the tracking accuracy while minimizing energy consumption. Simulation results validate its effective performance for target tracking in wireless sensor networks.

Target Tracking in Wireless Sensor Network

Target Tracking in Wireless Sensor Network

Online variational Bayesian filtering-based mobile target tracking in wireless sensor networks.

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer–Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying.

Wireless Sensor Networks to Predict the Range Covered by the Priority Scheduling Algorithm for Multi-Target Tracking

Research on multi-target tracking wireless sensor networks, the main problem is how to improve tracking accuracy and reduce energy consumption. Proposed use of forecasting methods to predict the target state, the selection of target detection range forecast based on the relationship between states and between sensor nodes deployed. And in accordance with the selected detection range, to wake up and form a cluster to track the target. In multi-target tracking will use to adjust the detection range, time to time to separate the conflict node of conflict, in order to achieve a successful track multiple targets. Simulation results show that the proposed method can indeed improve the chances of success of the track.

Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

This paper describes an original method for target tracking in wireless sensor networks. The proposed method combines machine learning with a Kalman filter to estimate instantaneous positions of a moving target. The target's accelerations, along with information from the network, are used to obtain an accurate estimation of its position. To this end, radio-fingerprints of received signal strength indicators (RSSIs) are first collected over the surveillance area. The obtained database is then used with machine learning algorithms to compute a model that estimates the position of the target using only RSSI information. This model leads to a first position estimate of the target under investigation. The kernel-based ridge regression and the vector-output regularized least squares are used in the learning process. The Kalman filter is used afterward to combine predictions of the target's positions based on acceleration information with the first estimates, leading to more accurate ones. The performance of the method is studied for different scenarios and a thorough comparison with well-known algorithms is also provided.

Energy-Balanced Scheduling for Target Tracking in Wireless Sensor Networks

A novel energy-balanced task-scheduling method is proposed that extends the lifespan of wireless sensor networks (WSNs) for collaborative target tracking using an unscented Kalman filter (UKF) algorithm. It is shown that the tracking accuracy is approximately proportional to the number of active sensor nodes participating in collaborative tracking. Excessive sensor nodes thus may be put to sleep mode to conserve energy provided there are a sufficient number of active sensor nodes. It is then shown that the lifespan of a WSN is dictated by the distribution of residue energy of sensor nodes. Specifically, we have shown that an energy-balanced WSN is likely to maximize its lifespan. As such, at each step of the tracking task, the head node must judiciously select active nodes from all sensors within the sensing range to minimize residue energy variations (energy balanced) while achieving desired tracking accuracy. This is formulated as a subset selection problem, which is shown to have a complexity that is NP-hard. Several energy-balanced scheduling for tracking (EBaST) heuristic algorithms are proposed to solve this problem with polynomial execution complexities. Extensive simulations have been conducted to compare EBaST against some state-of-the-art scheduling algorithms. It is observed that EBaST is more capable of significantly extending the WSNs lifespan than competing algorithms while delivering comparable or better tracking accuracy.

Sequential Asynchronous Filters for Target Tracking in Wireless Sensor Networks

Asynchronous data fusion is inevitable for target tracking in asynchronous wireless sensor networks, where multiple sensors are required to locate a target collaboratively. The predicted estimates of the follow-up states based on the to-be-estimated state are first introduced to overcome the drawback that asynchronous measurements cannot be fused directly. Then, the sequential asynchronous Bayesian state estimation is deduced based on the predicted estimates. The proposed estimation process is comprised of two steps: 1) the prediction step and 2) the update step. Finally, sequential asynchronous filters based Kalman filter and particle filter are proposed. Simulations demonstrate that the proposed algorithms perform not only better than the benchmark algorithms with asynchronous measurements, but also better than the benchmark algorithm with synchronous measurements.

P-LEACH: An Efficient Cluster-Based Technique to Track Mobile Sinks in Wireless Sensor Networks

For a wireless sensor network (WSN) with a large number of inexpensive sensor nodes, energy efficiency is the major concern in designing network structure and related algorithms. If network collects sensor data using mobile sinks, object tracking mechanism must consider the energy efficiency of sensor nodes in the networks as a whole. Recently research works on WSNs with mobile sinks apply prediction techniques for sink tracking in order to improve tracking precision while keeping the number of active nodes to the minimum. In this paper, we analyze existing works for sink tracking in WSN and propose P-LEACH that is cluster-based prediction technique for WSN with mobile sinks. Simulation results show that P-LEACH performs better than previous techniques in terms of energy saving of sensor nodes and data transmission performance.

Joint estimation of state and noise parameters in a linear dynamic system with impulsive measurement noise: Application to OFDM systems

Interference mitigation is one of the main challenges in wireless communication, especially in ad hoc networks. In such context, the Multiple Access Interference (MAI) is known to be of an impulsive nature. Therefore, the conventional Gaussian assumption is inadequate to model this type of interference. Nevertheless, it can be accurately modeled by stable distributions. In fact, it was shown in literature that the α-stable distribution is a useful tool to model impulsive data. In this paper, we tackle the problem of noise compensation in ad hoc networks. More precisely, this issue is addressed within an Orthogonal Frequency Division Multiplexing (OFDM) transmission link assuming a symmetric α-stable model for the signal distortion due to MAI. Based on Bayesian estimation, the proposed approach estimates the transmitted OFDM symbols in the time domain using the Sequential Monte Carlo (SMC) methods. Unlike existing schemes, we consider the more realistic case where the impulsive noise parameters are assumed to be unknown at the receiver. Consequently, our approach deals also with the difficult task of noise parameters estimation which can be very useful for other purposes such as target tracking in wireless sensor networks or channel estimation. Simulations results, provided in terms of Mean Square Error (MSE) and Bit Error Rate (BER), illustrate the efficiency and the robustness of this scheme.

Ant Based Compression and Sleep Scheduling Technique for Target Tracking In WSN

The main challenges involved in target tracking applications of Wireless Sensor Networks (WSN) are energy consumption, accuracy and security. In order to reduce the amount of energy consumed in target tracking, in this study, Ant based compression and Sleep Scheduling technique is proposed. In this approach initially the information of the nodes are localized and tracked through ACO technique. After the localization process, the data which has to be transmitted to the destination node is compressed in order to make the network more efficient. A Sensor Node Activation Mechanism is developed, where the sensor nodes are turned into sleep mode for a specific time interval. Simulation results show that the proposed technique considerably reduces the network energy consumption.

An endocrine-based intelligent distributed cooperative algorithm for target tracking in wireless sensor networks

In this paper, a novel endocrine-based intelligent distributed cooperative algorithm (EIDCA) for target tracking is proposed inspired by the regulating mechanisms of the human hormone systems. The EIDCA enables the nodes in the wireless sensor networks to self-organize themselves autonomously without a centralized control for target detection. A probability-based scheme for hormone transmission is also introduced to alleviate fluctuations of the network caused by frequent switches of the nodes. Meanwhile, a numerical evaluation method is designed to provide a quantitative metric for comparing the tracking performance of different algorithms. Simulation results show that the decentralized network controlled by the EIDCA can work efficiently and reliably without central control. It is also shown that the proposed EIDCA outperforms the compared algorithms in tracking targets.

A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wireless Sensor Networks

A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wireless Sensor Networks