A Novel Target Movement Model and Energy Efficient Target Tracking in Sensor Networks

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Energy awareness is a crucial component in the design of wireless sensor networks at all layers. This paper looks into efficient energy utilization of a target-tracking sensor network by predicting a target's trajectory through experience. While this is not new, the chief novelty comes in conserving energy through both dynamic spatial and temporal management of sensors while assuming minimal locality information. We adapted our target trajectory model from the Gauss–Markov mobility model, formulated the tracking problem as a hierarchical Markov decision process (HMDP), and solved it through neurodynamic programming. Our HMDP for target-tracking (HMTT) algorithm conserves energy by reducing the rate of sensing (temporal management) but maintains an acceptable tracking accuracy through trajectory prediction (spatial management) of multiple targets. We derived some theoretical bounds on accuracy and energy utilization of HMTT. Simulation results demonstrated the effectiveness of HMTT in energy conservation and tracking accuracy against two other predictive tracking algorithms, with accuracy of up to 47% higher and energy savings of up to 200%. </para>

In this paper, we consider a target-tracking sensor network and improve its energy awareness through predicting a target trajectory and decreasing sampling rate of sensors while maintaining an acceptable tracking accuracy. The tracking problem is formulated as a hierarchical Markov decision process (MDP) and is solved through neurodynamic programming. Though this is not new, improvements in performance of the network are achieved by use of a reinforcement learning algorithm to solve the MDP that converges faster than the preceding used methods, since the energy efficiency and speed of convergence of the solution are tightly coupled. Simulation results show the effectiveness of our algorithm against other known target tracking algorithms.

The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information and significant errors in forecasting fast-moving targets&#x2019; trajectories through traditional distributed tracking methods in sensor networks. Initially, the average consistency algorithm is used to average the local measurements of each node to achieve global consistency. Then, semantic moving computing of the Internet of Things calculates and analyzes the node movement to support the subsequent movement guidance of nodes and target movement prediction. Finally, the simulation experiment is carried out to evaluate the commonly used target trajectory prediction model. The simulation results show that the node movement algorithm by average consistency can effectively improve the positioning accuracy of the network for moving targets. Besides, the positioning error decreases with the increase of the sensing radius R, the number of moving nodes nm, and the total number of nodes ns deployed in a particular range in a two-dimensional (2D) space. The positioning error after node movement in 2D space is about 20&#x0025;&#x2013;30&#x0025;R lower than that in a static state. After node movement in a three-dimensional (3D) space, the positioning error is about 40&#x0025;&#x2013;50&#x0025;R lower than in a dormant state. When the target moves at a speed greater than 7m/s, the consistency-based moving computing algorithm&#x2019;s target loss rate and tracking errors are about 0&#x007E;10&#x0025; and 1.5&#x0025;&#x007E;2&#x0025; lower than the target tracking algorithm via Kalman Filter. Therefore, the algorithm reported here can precisely track the high-speed moving target. The existing research on point target tracking has problems of insufficient accuracy and robustness. The algorithm proposed here has stronger robustness, reduced data error in multi-node, and more flexible node movements, providing a reference for the subsequent research on distributed point target tracking.

Target tracking in wireless sensor networks (WSN) has brought up new practical problems. The limited energy supply and bandwidth of WSN have put stringent constraints on the complexity and inter-node information exchange of the tracking algorithm. In this paper, we propose a binary variational algorithm outperforming existing target tracking algorithms such as Kalman and Particle filtering. The variational formulation allows an implicit compression of the exchanged statistics between leader nodes, enabling thus a distributed decision-making. Its binary extension further reduces the resource consumption by locally exchanging only few bits.

In this paper, we address the problem of genetic algorithm optimization for jointly selecting the best group of candidate sensors and optimizing the quantization for target tracking in wireless sensor networks. We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering best group of candidate sensors selection problem. The main objective of this paper is twofold. Firstly, the quantization level and the group of candidate sensors selection are to be optimized in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor network. Secondly, the target position is to be estimated using quantized variational filtering (QVF) algorithm. The optimization of quantization and sensor selection are based on the Fast and Elitist Multi-objective Genetic Algorithm (NSGA-II). The proposed multi-objective (MO) function defines the main parameters that may influence the relevance of the participation in cooperation for target tracking and the transmitting power between one sensor and the cluster head (CH). The proposed algorithm is designed to: i) avoid the problem lot of computing times and operation counts, and ii) reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The computation of these criteria is based on the predictive information provided by the QVF algorithm. The simulation results show that the NSGA-II -based QVF algorithm outperforms the standard quantized variational filtering algorithm and the centralized quantized particle filter.

Sensor networks have provided a technology base for distributed target tracking applications among others. Conventional centralized approaches to the problem lack scalability in such a scenario where a large number of sensors provide measurements simultaneously under a possibly non-collaborating environment. Therefore research efforts have focused on scalable, robust, and distributed algorithms for the inference tasks related to target tracking, i.e. localization, data association, and track maintenance. Graphical models provide a rigorous tool for development of such algorithms modeling the information structure of a given task and providing distributed solutions through message passing algorithms. However, the limited communication capabilities and energy resources of sensor networks pose the additional difficulty of considering the trade-off between the communication cost and the accuracy of the result. Also the network structure and the information structure are different aspects of the problem and a mapping between the physical entities and the information structure is needed. In this paper we discuss available formalisms based on graphical models for target tracking in sensor networks with a focus on the aforementioned issues. We point out additional constraints that must be asserted in order to achieve further insight and more effective solutions.

This paper addresses the problem of coordinated target tracking in sensor networks. For a typical target tracking scene with nonlinear bearing-only measurements, we first investigate the mutual information between multiple sensors and the target state. To improve the performance of target tracking, we analyzed the relative positions between sensor agents and the target to be tracked and derived the optimal positions for sensors in the network by mutual information maximization. Simulation results are presented and discussed to demonstrate that the performance of estimated target states could be improved by the proposed method.

Classical tracking methods are not concerned with energy efficiency and require precise localisation. We addressed these in our previous work through HMTT (hierarchical Markov decision process for target tracking) that tracks single targets at location granularity. HMTT conserves energy by reducing the rate of sensing but preserves acceptable tracking accuracy through trajectory prediction. In this paper, HMTT is extended for the multiple targets case where the state of clusters could be affected by multiple incoming targets and where multiple updates are required at the lower level. The theoretical performance of HMTT in the multiple targets case is derived and simulations demonstrate its effectiveness against 2 other predictive tracking algorithms with up to 200% improvement.

Particle filters (or PFs) are widely used for the tracking problem in dynamic systems. Despite their remarkable tracking performance and flexibility, PFs require intensive computation and communication, which are strictly constrained in wireless sensor networks (or WSNs). Thus, distributed particle filters (or DPFs) have been studied to distribute the computational workload onto multiple nodes while minimizing the communication among them. However, weight normalization and resampling in generic PFs cause significant challenges in the distributed implementation. Few existing efforts on DPF could be implemented in a completely distributed manner. In this paper, we design a completely distributed particle filter (or CDPF) for target tracking in sensor networks, and further improve it with neighborhood estimation toward minimizing the communication cost. First, we describe the particle maintenance and propagation mechanism, by which particles are maintained on different sensor nodes and propagated along the target trajectory. Then, we design the CDPF algorithm by adjusting the order of PFs' four steps and leveraging the data aggregation during particle propagation. Finally, we develop a neighborhood estimation method to replace the measurement broadcasting and the calculation of likelihood functions. With this approximate estimation, the communication cost of DPFs can be minimized. Our experimental evaluations show that although CDPF incurs about 50% more estimation error than semi-distributed particle filter (or SDPF), its communication cost is lower than that of SDPF by as much as 90%.

We consider the problem of target tracking in a wireless sensor network (WSN) that consists of randomly distributed range-only sensors. Quantized measurements are usually adopted in such a network to attack the problem of limited power supply and communication bandwidth. Assuming that local sensor noises are mutually independent, we derive the posterior Cramer-Rao lower bound (CRLB) on the mean squared error (MSE) of target tracking in WSNs with quantized range-only measurements. Recursion of posterior CRLB on tracking based on both constant velocity (CV) and constant acceleration (CA) model for target dynamics and a general range-only measuring model for local sensors are obtained. Due to the analytical difficulties, particle filter is applied to approximate the theoretical bounds. To illustrate the posterior CRLB, an example on tracking a target with noisy circular trajectories is given.

The sensor scheduling for energy-efficient target tracking with high performance in wireless sensor networks (WSNs) is a dilemma problem. By analyzing the intrinsic relationship between tracking performance and energy consumption, we cast the scheduling problem of WSN as the optimal policy problem of partially observable Markov decision process (POMDP), and propose a dynamic cluster members scheduling (DCMS) algorithm to solve the tradeoff between tracking performance and energy consumption. First, we exploit an election method, based on the optimal mixed weights of the signal strength and the residual energy of node, to choose the cluster head node. Then, we seem each cluster members an agent, and model the scheduling problem of cluster members by POMDP. At last, a point-based online value iteration algorithm is presented to solve the DCMS to generate the collaboration strategy of sensor cluster members dynamically. The simulation results show that the proposed approach can improve the accuracy of target tracking, decrease the energy consumption of sensor nodes, and prolong the lifetime of sensor networks.

This paper addresses the problem of sensor selection in a sensor network for tracking a moving target. By considering network uncertainties and unpredictable movements of the target, reliable sensor selection approaches such as sigma points probability and target trajectory are proposed. An updated unscented Kalman filter is proposed to achieve effective tracking of the target through the sensor selection. A multialgorithm genetically adaptive multiobjective is utilized to have a selection strategy without knowing the number of sensors to be selected. Extensive experiments are conducted to evaluate the effectiveness of the proposed approach both in simulation and practical experimentation. The proposed algorithm is also tested in the industrial setting where providing safety is of great importance for a human worker who walks in a potentially dangerous workplace. The results confirm the effectiveness and utility of the proposed scheme.

The authors propose a sequential quasi-Monte Carlo (SQMC)-based algorithm for joint estimation of sensor-node locations and target trajectory in a wireless sensor network. The sensor nodes are randomly deployed with no prior knowledge about their positions. A predictive entropy-based information utility is used to select the leader node at each stage, and all other nodes are kept in standby mode to save power. The Bayesian estimates required to track the systems's non-linear dynamics are computed using the powerful SQMC method, which naturally integrates sensor collaboration with optimal leader node selection. Extensions of the algorithm to other interesting scenarios such as missing observations and non-Gaussian noise are also presented, which are very relevant to the unreliable environments encountered in hostile territories. The authors demonstrate through simulations that even with a very small fraction of the total number of nodes acting as beacon nodes, the proposed method can not only track the moving target, but can also obtain fairly accurate estimates of the (unknown) locatp(z(t)|z(i(j))(t − 1))ions of the sensor nodes.

The increasing deployment of underwater vehicles demands accurate and energy-efficient target tracking in sensor networks. However, existing approaches have largely addressed tracking accuracy and energy efficiency in isolation, and a system-level framework that jointly optimizes both remains lacking. To address this gap, this paper proposes a joint optimization framework with two main contributions. First, to improve tracking accuracy under complex maneuvering conditions, we develop an Interactive Multi-Model using Long Short-Term Memory Classification (IMM-LSTM-C) algorithm, which integrates multi-step model likelihoods into an LSTM network for precise motion classification, achieving a 7.1% accuracy improvement over IMM-BP. Second, to reduce network energy consumption while maintaining tracking performance, we introduce an Improved Binary Prairie Dog Optimization (IBPDO) algorithm for node selection, enhanced with Cauchy mutation and opposition-based learning. Simulation results show that IBPDO achieves 6.1-8.2% higher accuracy than BWOA and reduces energy consumption by 12% compared to LNS. Furthermore, the complete joint framework demonstrates synergistic effects, reducing tracking error by 19.3% and energy consumption by 15.4% over the IMM + LNS baseline. The proposed framework provides an effective balance between tracking accuracy and energy efficiency in underwater acoustic sensor networks.

In this paper, we investigate a moving-target tracking problem with sensor networks. Each sensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is "smart" enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore, we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result.