Tracking In Wireless Sensor Networks Research Articles

An Improved Binary Dragonfly Fault-Tolerant Sensor Scheduling Based Approach for Target Tracking in Wireless Sensor Networks

Abstract: Target tracking in wireless sensor networks (WSNs) is a critical application, often relying on node scheduling based on trajectory prediction. However, traditional approaches may lead to target loss due to prediction errors, requiring resourceintensive target recovery mechanisms and potentially resulting in data loss. To address this challenge, we propose an improved binary dragonfly fault-tolerant sensor scheduling (BD-FTSS) approach aimed at enhancing target tracking performance in WSNs. Our approach focuses on minimizing target loss probability while reducing energy consumption through a low-power scheduling mechanism. The BD-FTSS approach begins by designing a fault-tolerant domain to expand the scheduling range of candidate nodes. By considering factors such as remaining energy, sensing coverage, and overlapping coverage, we aim to activate the minimum number of sensors necessary to cover the fault-tolerant domain effectively.

Research on adaptive particle swarm optimization particle filter target tracking algorithm in wireless sensor networks

AbstractWith regard to target tracking in wireless sensor networks, we are faced with problems like deficient occlusion handling and tracking failures during rapid movements due to complex and diverse circumstances. In order to effectively improve the accuracy of particle filter tracking caused by particle degradation, we propose an adaptive particle swarm optimization (APSO) particle filter algorithm. This algorithm uses particle filters to predict the target location in a particular area and introduces the particle swarm optimization (PSO) algorithm, of which both the evolutionary speed and the convergence accuracy are further improved by investigating the particle distribution through an entropy analysis, employing three different inertial weighting strategies and dynamic double mutation strategy, and exploiting the capabilities of the adaptive balancing algorithm in global and local searching. The simulation results show that the improved algorithm has a reduced root mean square error, shorter time consumption, faster speed, reduced target tracking error, and higher average success rate, so this algorithm exhibits sound real‐time performance and accuracy in terms of occlusion handling and tracking loss.

Hybrid Energy Regulated Constant Gain Kalman-Filter for Optimized Target Detection and Tracking in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are one of the most researched areas worldwide as the wide-scale networks possess low cost, are small in size, consume low power, and can be deployed in various environments. Among various applications of WSNs, target tracking is a highly demanding and broadly investigated application of wireless sensor networks. The parameter of accurate tracking is restricted because of the limited resources present in the wireless sensor networks, noise of the network, environmental factors, and faulty sensor nodes. Our work aims to enhance the accuracy of the tracking process as well as energy utilization by combing the mechanism of clustering with the prediction. Here, we present a hybrid energy-regulated constant gain Kalman filter-based target detection and tracking method, which is an algorithm to make the best use of energy and enhance precision in tracking. Our proposed algorithm is compared with the existing approaches where it is observed that the proposed technique possesses efficient energy utilization by decreasing the transference of unimportant data within the sensor network, achieving accurate results.

Sensor Management with Dynamic Clustering for Bearings-Only Multi-Target Tracking via Swarm Intelligence Optimization

Sensor management is a crucial research subject for multi-sensor multi-target tracking in wireless sensor networks (WSNs) with limited resources. Bearings-only tracking produces further challenges related to high nonlinearity and poor observability. Moreover, energy efficiency and energy balancing should be considered for sensor management in WSNs, which involves networking and transmission. This paper formulates the sensor management problem in the partially observable Markov decision process (POMDP) framework and uses the cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter for tracking. A threshold control method is presented to reduce the impact on tracking accuracy when using bearings-only measurements for sequential update. Moreover, a Cauchy–Schwarz divergence center is defined to construct a new objective function for efficiently finding the optimal sensor subset via swarm intelligence optimization. This is also conducive to dynamic clustering for the energy efficiency and energy balancing of the network. The simulation results illustrate that the proposed solution can achieve good tracking performance with less energy, and especially that it can effectively balance network energy consumption and prolong network lifetime.

Target Positioning and Tracking in WSNs Based on AFSA

In wireless sensor networks (WSNs), the target positioning and tracking are very important topics. There are many different methods used in target positioning and tracking, for example, angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and received signal strength (RSS). This paper uses an artificial fish swarm algorithm (AFSA) and the received signal strength indicator (RSSI) channel model for indoor target positioning and tracking. The performance of eight different method combinations of fixed or adaptive steps, the region segmentation method (RSM), Hybrid Adaptive Vision of Prey (HAVP) method, and a Dynamic AF Selection (DAFS) method proposed in this paper for target positioning and tracking is investigated when the number of artificial fish is 100, 72, 52, 24, and 12. The simulation results show that using the proposed HAVP total average positioning error is reduced by 96.1%, and the positioning time is shortened by 26.4% for the target position. Adopting HAVP, RSM, and DAFS in target tracking, the positioning time can be greatly shortened by 42.47% without degrading the tracking success rate.

PSO-Based Target Localization and Tracking in Wireless Sensor Networks

Research of target localization and tracking is always a remarkable problem in the application of wireless sensor networks (WSNs) technology. There are many kinds of research and applications of target localization and tracking, such as Angle of Arrival (AOA), Time of Arrival (TOA), and Time Difference of Arrival (TDOA). The target localization accuracy for TOA, TDOA, and AOA is better than RSS. However, the required devices in the TOA, TDOA, and AOA are more expensive than RSS. In addition, the computational complexity of TOA, TDOA, and AOA is also more complicated than RSS. This paper uses a particle swarm optimization (PSO) algorithm with the received signal strength index (RSSI) channel model for indoor target localization and tracking. The performance of eight different method combinations of random or regular points, fixed or adaptive weights, and the region segmentation method (RSM) proposed in this paper for target localization and tracking is investigated for the number of particles in the PSO algorithm with 12, 24, 52, 72, and 100. The simulation results show that the proposed RSM method can reduce the number of particles used in the PSO algorithm and improve the speed of positioning and tracking without affecting the accuracy of target localization and tracking. The total average localization time for target localization and tracking with the RSM method can be reduced by 48.95% and 34.14%, respectively, and the average accuracy of target tracking reaches up to 93.09%.

Energy efficient target tracking in wireless sensor network using PF-SVM (particle filter-support vector machine) technique

When using a Wireless Sensor Network (WSN) for target tracking applications, optimum selection of right functioning nodes can reduce the number of active nodes and also ensuring tracking reliability requirement. Due to the limitations of the WSN's sensing range, it is crucial to create a mechanism that allows nodes to coordinate in order to follow the target reliably and with a high probability. By doing this, the network's overall energy consumption can be decreased, resulting in a longer network lifetime. Target tracking (TT) is a well observed and significant application of WSNs. In simple words, it maintains a proper trade-off between tracking quality and energy consumption. In the proposed work, Particle Filter (PF) with a machine learning technique called Support Vector Machine (SVM) based energy efficient target tracking used in WSN's. PF is considered to be the most accepted filtering algorithm in various tracking and localization problems. Simulation results show greater performance in determining the target location and maintain lower energy consumption.

Moving Vehicle Tracking under Measurement Uncertainties, Multi-step Random Delays, and Packet Dropouts

The unbiased finite impulse response (UFIR) filter, Kalman filter (KF), and game theory <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty} $ </tex-math></inline-formula> filter are developed for vehicle tracking in wireless sensor networks (WSNs) with multi-step random delays and multiple dropouts. The problem with data delays is solved for given delay probabilities by converting a model with delays to another without delays. The dropouts are detected at the receiver by a data sensor, and lost data are compensated for by prediction. The filters developed are experimentally tested for accuracy and robustness using the Global Positioning System-based vehicle tracking database, where the measured coordinates are transmitted over WSN with delays. It is shown that the UFIR filter is the most robust to uncertain delays and the KF is the most accurate when the delay probabilities are known exactly. The game theory <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> filter is the most accurate under ideal conditions and prone to divergency otherwise.

Nonlinear autoregressive neural network with exogenous input for an energy efficient non-cooperative target tracking in wireless sensor network

Nonlinear autoregressive neural network with exogenous input for an energy efficient non-cooperative target tracking in wireless sensor network

Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

The amount of needed control messages in wireless sensor networks (WSN) is affected by the storage strategy of detected events. Because broadcasting superfluous control messages consumes excess energy, the network lifespan can be extended if the quantity of control messages is decreased. In this study, an optimized storage technique having low control overhead for tracking the objects in WSN is introduced. The basic concept is to retain observed events in internal memory and preserve the relationship between sensed information and sensor nodes using a novel inexpensive data structure entitled Ordered Binary Linked List (OBLL). Whenever an object passes over the sensor area, the recognizing sensor can immediately produce an OBLL along the object’s route. To retrieve the entire information, the OBLL can be traversed with logarithmic complexity which is much less than the traversing complexity of existing linked list structures. Performance evaluation and simulations were carried out to ensure that the suggested technique minimizes the number of messages and thus saving energy and extending the network life.

Dynamic Sensor Scheduling for Target Tracking in Wireless Sensor Networks With Cost Minimization Objective

Wireless sensor networks (WSNs) are demonstrated to be the increasingly essential systems for various Internet of Things (IoT)-based sensing applications. This article proposes a cost-aware dynamic sensor scheduling (CADSS) framework for WSNs with multiple tasks. At its core, a system cost function is designed to quantify the expenses of the WSNs due to task executions, and a task quality function is modeled to indicate the performance of the corresponding tasks. The proposed CADSS is further formulated as an optimization problem to minimize the system cost while maintaining the desired task qualities. In this way, a comprehensive task utility evaluation methodology for self-organized WSNs is constituted. Furthermore, by modeling the posterior Cramér–Rao lower bound (PCRLB) as the task quality function and a weighted sum of the communication and sensor scheduling cost as the system cost, the CADSS is instantiated into a multitarget tracking (MTT) application. It is shown that the formulated CADSS is a nonconvex optimization problem involving two coupled binary variables that, respectively, correspond to the scheduling of sensor and cluster head. We then propose a parallel convex relation approach to solve it effectively. Numerical results verify the effectiveness of the proposed CADSS by comparing it with state-of-the-art strategies.

Intelligent Sensing Scheduling for Mobile Target Tracking Wireless Sensor Networks

Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in mobile target tracking wireless sensor networks (MTT-WSNs). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is intractable to execute dynamic and critical missions in the MTT-WSN network due to static property. Besides, the network cannot ensure consecutive tracking with limited energy. To address the problems, this article proposes a new hierarchical tracking structure based on the edge intelligence (EI) technology. The structure can integrate the computing resource of both mobile nodes and edge servers to provide high-efficient computing for real-time tracking. Based on the proposed structure, we propose a long-term dynamic resource allocation algorithm to obtain the optimal resource scheduling solution for accurate and consecutive tracking. Simulation results demonstrate that our algorithm outperforms the deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -learning over 14.5% in terms of systematic energy consumption. It can also obtain a significant enhancement in tracking accuracy compared with the noncooperative scheme.

Sensor Selection for Maneuvering Target Tracking in Wireless Sensor Networks With Uncertainty

Maneuvering target tracking in wireless sensor networks (WSNs) has been a prominent research topic over the past twenty years. In many situations, WSNs are vulnerable to natural or man-made interference, resulting in uncertainty problems. In this paper, we propose an information-quality-based sensor selection method with estimation feedback for maneuvering target tracking in uncertain WSNs. First, based on the Generalized Pseudo-Bayesian estimator of first order (GPB1) and the unscented information filter (UIF), a GPB1-UIF algorithm with estimation feedback is presented. Inspired by the theory of information quality, an information-quality metric framework is proposed for sensor selection in uncertain WSNs. And the information-theoretic selection metrics are redesigned based on consistency measuring operator. Moreover, without knowing the number of selected sensors a priori, a multi-objective optimization problem is considered to determine the final selection strategy. Simulation results demonstrate that the proposed approach outperform the previous sensor selection methods.

A fault-tolerant sensor scheduling approach for target tracking in wireless sensor networks

Target tracking is a critical application in wireless sensor networks (WSNs). Existing target tracking algorithms basically perform node scheduling based on trajectory prediction. Once the target is lost due to prediction errors, the target recovery mechanism performs a search to find the target, which may require the system to activate a large number of nodes and cause additional energy consumption. Moreover, the target data may be lost due to the time consuming of target recovery. To solve this problem, we propose a fault-tolerant sensor scheduling (FTSS) approach to reduce the target loss probability as much as possible. Furthermore, we design a low-power scheduling mechanism in FTSS to reduce energy consumption. In FTSS, we first design a fault-tolerant domain to expand the scheduling range of candidate nodes. Then, we consider remaining energy, sensing coverage, and overlapping coverage to activate as few sensors as possible to cover the fault-tolerant domain. Moreover, we employ an improved binary Grey Wolf Optimizer (bGWO) in the optimization process to speed up the convergence. Numeric evaluations indicate that, compared to DPT, HCTT, GBRHA, and E2DR-MCS, our method achieves 11%, 2.3%, 7.8%, 4.6% reduction in target loss probability, and improves network lifetime by 138%, 49%, 31%, 19%, respectively.

Measurement outlier-Resistant target tracking in wireless sensor networks with energy harvesting constraints

In this paper, the measurement outlier-resistant target tracking problem is investigated in wireless sensor networks (WSNs) with energy harvesting constraints. Each WSN node can acquire energy stochastically from surroundings. No matter whether the WSN node acquires energy or not, the WSN node’s measurement can be transmitted if the energy amount of the WSN node is greater than zero. In such a case, the sensor energy-induced missing measurement (SE-IMM) phenomenon may occur. The objective of this paper is to develop a solution for the considered target tracking problem by devising the filter including a saturation constraint such that, in the simultaneous presence of outliers and the SE-IMM phenomenon, the tracking performance can meet the given performance index. Firstly, the relation between the energy level of the WSN node and its probability distribution is computed recursively. Then, an upper bound of the tracking error covariance is derived which is minimized by appropriately choosing the filter parameter. Finally, the feasibility of the proposed target tracking scheme is validated by conducting a set of comparative experiments and the relationship between the energy of the WSN node and the tracking performance is also disclosed.

Energy Efficient Routing Design for Target Tracking in Wireless Sensor Network

The wireless sensor networks (WSNs) provides an advance way for connection of various applications. Most of the applications use the wireless sensor network to transmit the information from the device to the main base station. The main operation of the wireless sensor network is to first sense the data using the sensor, then collect the data and finally transmit to the required base station. The data is transmitted in a timely manner such that the other wireless sensor network transmitting the information should not have any problem. During the transmission of the data the main objective of the wireless sensor network device is to provide reliability to the information which is being sent with less latency and to reduce the energy consumption in order to increase the lifespan of the wireless sensor network. Therefore, this paper presents an Energy Efficient Routing for the Target Tracking (EER-TT) in the wireless sensor network which provides reliability, less latency and reduces the energy consumption during the transmission of the information to the base station. Proposed model also provides a cluster selection method for the routing of the wireless sensor network devices. The results attained show that our model EER-TT shows better results when compared with the existing routing-based models.

A cascaded algorithm based on GMM, GKF, and data fusion for mobile tracking in wireless sensor network

SummaryWireless sensor network (WSN) is formed with numerous communication nodes, which plays an important role in the Internet of Things (IoT). Location‐based service is critically important for WSN; however, the nonline of sight (NLOS) condition can deteriorate the positioning precision significantly. In this paper, a robust localization algorithm based on the range measurements to deal with the mixed line of sight (LOS)/NLOS environment is proposed. Firstly, a hypothesis testing based on the data deviation is used to detect the transmission condition. For the identified LOS propagation, the measured data are processed by Gaussian mixture model (GMM) to calculate the mean and standard deviation, which can update the likelihood probability and residual by data fusion. Moreover, for the identified NLOS propagation data, the mean of every Gaussian component is processed by gray Kalman filter (GKF) to discard large outliers. Finally, maximum likelihood (ML) is adopted to derive final coordinates. The simulation results have shown that the proposed algorithm has great advantages in dealing with severe NLOS interference and has higher accuracy compared with existing classic algorithms.

Short Word-Length Entering Compressive Sensing Domain: Improved Energy Efficiency in Wireless Sensor Networks

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R ≥ 1, where R is the sample reduction ratio of compressive sensing, and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise and transmission noise, owing to the noise shaping property of sigma-delta modulation.

Dynamic Nodes Collaboration for Target Tracking in Wireless Sensor Networks

In target tracking WSNs, the tree-based and cluster-based network should be rebuilt when the location of target changes, which involves energy and time overheads. Although the chain structure solves this problem, it involves lots of unnecessary nodes and unacceptable transmitting delay. Therefore, we propose a dynamic chain-based collaboration (DCBC) approach for efficient target tracking and data gathering in this paper. DCBC completes the target tracking task by forming a dynamic tracking chain around the target. With the target moving, the dynamic tracking chain is adjusted by pruning some nodes as long as they are out of the range of the target and adding the nodes which entered the range of the target, so that the chain can adapt to the changing location of the target. The structure of the dynamic tracking chain does not need to be re-established when the target position changes. Furthermore, the sensing data are fused at every step when they are transmitted along the dynamic tracking chain. Using this way, the sensing data can be collected by tracking nodes and fused locally. In addition, we propose a dynamic and energy-efficient sleep scheduling for data transmission in the target tracking chain. The nodes in the tracking chain send their own data in turn. Thus it improves the energy utilization and data transmission efficiency of the network. The experimental results verify that DCBC can reduce and balance the average energy consumption of the network compared with the state-of-the-art approaches, and prolong the lifetime of networks.

Target Tracking in Wireless Sensor Networks

Target Tracking in Wireless Sensor Networks