Energy Efficient Target Tracking Research Articles

An Energy Efficient Prediction Based Protocol for Target Tracking in Wireless Sensor Networks

Target tracking is one of the most attractive applications of wireless sensor networks, used for estimating the moving target's position accurately. A primary challenge in this domain is achieving precise target path estimation while conserving energy resources. This paper introduces an energy-efficient target tracking protocol in wireless sensor networks, considering both accuracy and reduced energy consumption. The protocol uses the Kalman filter to estimate the target's position and predict the subsequent step of its path. In each step of target tracking, a selected sensor, named the ‘leader’ performs computations for position estimation and path prediction, while two other sensors, known as ‘assistants’ help the leader in the tracking process. Leader selection within the protocol is performed in two phases: an initial phase occurring upon the target's entry to the network and a subsequent phase named the forced handoff phase. The forced handoff phase performs the selection of a new leader when either the target exits the sensing range of the current leader or the leader's energy decreases significantly. Although the proposed protocol is a new work, it can be considered as an improvement of the PPCP protocol by adding several changes and also replacing the binary variational filter with the Kalman filter. The efficiency of the proposed protocol is evaluated through simulations in Matlab. Results demonstrate the protocol's ability to achieve high-precision target tracking while maintaining low energy consumption. Comparative analysis shows its energy efficiency, which significantly increases the network lifetime.

Energy Efficient Target Tracking in WSN : An Adaptive Approach

Energy Efficient Target Tracking in WSN : An Adaptive Approach

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.

Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network

Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

A non-conventional lightweight Auto Regressive Neural Network for accurate and energy efficient target tracking in Wireless Sensor Network

The design of an energy-efficient tracking framework is a well-investigated issue and a prominent sensor network application. The current research state shows a clear scope for developing algorithms that can work, accompanying both energy efficiency and accuracy. The prediction-based algorithms can save network energy by carefully selecting suitable nodes for continuous target tracking. However, the conventional prediction algorithms are confined to fixed motion models and generally fail in accelerated target movements. The neural networks can learn any non-linearity between input and output as they are model-free estimators. To design a lightweight neural network-based prediction algorithm for resource-constrained tiny sensor nodes is a challenging task. This research aims to develop a simpler, energy-efficient, and accurate network-based tracking scheme for linear and non-linear target movements. The proposed technique uses an autoregressive model to learn the temporal correlation between successive samples of a target trajectory. The simulation results are compared with the traditional Kalman filter (KF), Interacting Multiple models (IMM), Current Statistical model (CSM), Long Short Term Memory (LSTM), Decision Tree (DT), and Random Forest (RF) based tracking approach. It shows that the proposed algorithm can save up to 70% of network energy with improved prediction accuracy.

Energy Efficient Target Tracking Method for Multi-Sensory scheduling in Wireless Sensor Networks

Data collection utilizing wireless sensors networks (WSNs) has been utilized for surveillance, monitoring environment, animal etc. Target tracking of maneuvering objects is an essential need of modern life. Nonetheless, because of diverse nature of sensor and complex environment, sensors measurement errors need to be minimized considering diverse motion states in process of tracking (sensing) operation. Enhancing network lifetime (i.e., reducing energy dissipation of sensor nodes) and improving tracking quality are major concern of target tracking using WSN. Form improving network energy efficiency, multi-sensory target tracking method has been modelled using Kalman Filter (KF) by existing target tracking method. The KF based model are affected due to presence of noise or missing data. For overcoming research issues this paper present an H-infinity filter (HF) to evaluate fusion for maneuvering target tracking in WSN. Further, to minimize the estimation errors and reduces/controlling the effects of outliers fuzzy H-infinity (FHF) filter for target tracking WSN is presented. Experiment outcome shows proposed HF and FHF fusion model attain better performance than existing KF based method for clustered based WSN in terms of positional and velocity root mean square error and energy dissipation.

Particle swarm optimization-based energy efficient target tracking in wireless sensor network

Wireless Sensor Network plays a vital role in tracking the mobility of targets like animals for habitat monitoring, vehicle monitoring etc. Many researches have been carried out for the precise identity of the target. In this research work, a distributed energy optimization method for target tracking is carried out using Particle Swarm Optimization. The proposed work is comprised of estimation phase and prediction phase. Here, clustering is performed using maximum entropy method. Grid exclusion is used for the coverage of nodes in the network. Performance evaluation is carried out for the proposed target tracking method with the existing systems using network simulator software.

A type of energy-efficient target tracking approach based on grids in sensor networks

To enhance the reliability as well as the value of sensing data in Wireless Sensor Networks (WSNs), a type of Energy-efficient Target Tracking Approach (ETTA) is proposed in this paper. The sensor network is divided into several virtual grids for distributed tracking and three kinds of states (tracking state, prepared-tracking state and preparing-tracking state) of these grids are also proposed to reduce energy consumption and enhance the accuracy of node localization. Moreover, a tracking recovery strategy is also described in this paper that effectively enhance the robustness of the tracking system. Experiment results show that ETTA has a good performance on target tracking in sensor networks compared to BPS and EMTT.

Energy-Efficient Target Tracking With UASNs: A Consensus-Based Bayesian Approach

Target tracking has been considered as one of the most important applications of underwater acoustic sensor networks. However, the long propagation delay, high-energy consumption, and strong noise properties of the underwater environment make target tracking more challenging as compared with terrestrial sensor networks. This article is concerned with an energy-efficient tracking issue for underwater targets, subject to an asynchronous clock, power restriction, and noise measurement constraints. The tracking process can be divided into two phases, i.e., position acquisition and persistent tracking. In the first phase, we establish the relationship between propagation delay and position, through which an asynchronous localization algorithm is developed for sensor nodes to estimate the position of target. Based on the estimated position, a consensus-based Bayesian filter is designed for sensor nodes in the second phase to enable persistent tracking. In particular, the consensus fusion strategy and duty-cycle mechanism are jointly adopted to improve the tracking accuracy and prolong the network lifetime. Moreover, the convergence analyses for the proposed approach are also presented. Finally, simulation and experimental results reveal that the proposed tracking approach can reduce the influence of malicious measurements, while the energy efficiency can be significantly improved as compared with the other works. Note to Practitioners —Underwater target tracking is aimed to acquire precise location and achieve sustainable monitoring for a target. It has important practical significance to enhance marine observing ability. Currently, the most favorable carrier for underwater communication is still the acoustic wave. However, due to the unique characteristics of acoustic communication, the tracking schemes developed for the terrestrial environment cannot be directly applied to underwater acoustic sensor networks. With this as motivation, we develop an energy-efficient tracking approach for underwater targets. The tracking approach developed in this article exploits timestamp measurement, consensus fusion strategy, and duty-cycle mechanism to capture the unique characteristics of acoustic communication. To better use this approach for target tracking, two factors should be highlighted: 1) the clocks between target and sensors are not required to be synchronized and 2) the sensor nodes are not always demanded to retain the active mode. Simultaneously, it is more meaningful from the view of marine engineering to design the control and communication platforms. Simulation and experimental results suggest that the proposed tracking method is feasible and has superior performance as compared with the existing methods. Hopefully, our tracking strategy can provide valuable theoretical and technical support guidance to the practicing marine engineer for the co-design of control and communication.

Energy Efficient Target Tracking with Collision Avoidance in WSNs

Wireless sensor network (WSN) is one of the most evolving technologies. WSN involves collecting, processing, transferring and storing information about objects with the help of sensor nodes. Tracking and detection of targets is one of the most attractive applications of WSN in surveillance systems. To resolve the problem of target tracking, it is essential to deploy a system model. It has been observed that clustering algorithms play an important role in cluster head selection, but they consume significant amount of energy. In this paper an energy efficient system model is deployed with a novel target tracking algorithm to track the target around the vicinity of the WSN. As there is more possibility of collision proximate to the base station, a new collision avoidance method is introduced. The lifetime of the network on the basis of congestion around the sink node, packet density and path loss are also measured efficiently.

POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors.

This paper presents a distributed supervisory control algorithm that enables opportunistic sensing for energy-efficient target tracking in a sensor network. The algorithm called Prediction-based Opportunistic Sensing (POSE), is a distributed node-level energy management approach for minimizing energy usage. Distributed sensor nodes in the POSE network self-adapt to target trajectories by enabling high power consuming devices when they predict that a target is arriving in their coverage area, while enabling low power consuming devices when the target is absent. Each node has a Probabilistic Finite State Automaton which acts as a supervisor to dynamically control its various sensing and communication devices based on target's predicted position. The POSE algorithm is validated by extensive Monte Carlo simulations and compared with random scheduling schemes. The results show that the POSE algorithm provides significant energy savings while also improving track estimation via fusion-driven state initialization.

Trajectroy prediction for target tracking using acoustic and image hybrid wireless multimedia sensors networks

Wireless multimedia sensor networks (WMSN), with self-organizing and high fault tolerant characteristics, have achieved great advantages in target tracking region. However, the capabilities of these tiny devices are limited by their battery power, storage capacity, computational ability and communication bandwidth. In this paper, hybrid wireless multimedia sensors networks composed of acoustic and image sensors are proposed for target tracking. When the target appears in the detection area, it may change the environment parameters nearby, so acoustic sensors are used to gather target signal firstly. Then, a target location method is executed based on the strength of the received acoustic signal. Furthermore, to achieve energy-efficient target tracking with high reliability and robust, image sensors are used as supplements to the acoustic sensors. This approach also reduces the power consumption communication burden of the whole networks. In order to decrease the number of active nodes, Gauss Markov mobility model is also adopted to predict the target trajectory and minimize the tracking region with considering of vehicular kinematics. Simulation results verify that, compared with other algorithms, our scheme can reduce the energy consumption and improve tracking accuracy.

An Artificial Measurements-Based Adaptive Filter for Energy-Efficient Target Tracking via Underwater Wireless Sensor Networks.

We study the problem of energy-efficient target tracking in underwater wireless sensor networks (UWSNs). Since sensors of UWSNs are battery-powered, it is impracticable to replace the batteries when exhausted. This means that the battery life affects the lifetime of the whole network. In order to extend the network lifetime, it is worth reducing the energy consumption on the premise of sufficient tracking accuracy. This paper proposes an energy-efficient filter that implements the tradeoff between communication cost and tracking accuracy. Under the distributed fusion framework, local sensors should not send their weak information to the fusion center if their measurement residuals are smaller than the pre-given threshold. In order to guarantee the target tracking accuracy, artificial measurements are generated to compensate for those unsent real measurements. Then, an adaptive scheme is derived to take full advantages of the artificial measurements-based filter in terms of energy-efficiency. Furthermore, a computationally efficient optimal sensor selection scheme is proposed to improve tracking accuracy on the premise of employing the same number of sensors. Simulation demonstrates that our scheme has superior advantages in the tradeoff between communication cost and tracking accuracy. It saves much energy while loosing little tracking accuracy or improves tracking performance with less additional energy cost.

Energy-Efficient Target Tracking by Mobile Sensors With Limited Sensing Range

The recent advancements of technology in robotics and wireless communication have enabled the low-cost and large-scale deployment of mobile sensor nodes for target tracking, which is a critical application scenario of wireless sensor networks. Due to the constraints of limited sensing range, it is of great importance to design node coordination mechanism for reliable tracking so that at least the target can always be detected with a high probability, while the total network energy cost can be reduced for longer network lifetime. In this paper, we deal with this problem considering both the unreliable wireless channel and the network energy constraint. We transfer the original problem into a dynamic coverage problem and decompose it into two subproblems. By exploiting the online estimate of target location, we first decide the locations where the mobile nodes should move into so that the reliable tracking can be guaranteed. Then, we assign different nodes to each location in order that the total energy cost in terms of moving distance can be minimized. Extensive simulations under various system settings are employed to evaluate the effectiveness of our solution.

Dynamic Cluster Members Scheduling for Target Tracking in Sensor Networks

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.

Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks.

Target tracking is a critical wireless sensor application, which involves signal and information processing technologies. In conventional target position estimation methods, an estimate is usually demonstrated by an average target position. In contrast, this work proposes a distributed information compression method to describe the measurement uncertainty of tracking problems in cluster-based wireless sensor networks. The leader-based information processing scheme is applied to perform target positioning and energy conservation. A two-level hierarchical network topology is adopted for energy-efficient target tracking with information compression. A Level 1 network architecture is a cluster-based network topology for managing network operations. A Level 2 network architecture is an event-based and leader-based topology, utilizing the concept of information compression to process the estimates of sensor nodes. The simulation results show that compared to conventional schemes, the proposed data processing scheme has a balanced system performance in terms of tracking accuracy, data size for transmission and energy consumption.

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.

An Energy-Efficient Collaborative Target Tracking Framework in Distributed Wireless Sensor Networks

Energy consumption and tracking accuracy are two significant issues for collaborative tracking in distributed wireless sensor networks (DWSNs). To obtain a benefit from those issues, most of the recent work tends to reduce the spatial redundancy, while ignoring utilizing the attribute of time redundancy. In this paper, a novel energy-efficient framework of collaborative signal and information fusion is proposed for acoustic target tracking. The proposed fusion algorithm is based on neural network aggregation model and Gaussian particle filtering (GPF) estimation. And the neural network based aggregation (NNBA) can reduce spatial and time redundancy. Furthermore, a fresh cluster head (CH) selection method demanding less task handover is also presented to decrease energy consumption. The analyzed framework coupled with simulations demonstrates its excellent performance in tracking accuracy and energy consumption.

Energy Efficient Target Tracking Using Sleep Scheduling Mechanism for Wireless Sensor Network

In the development of various large-scale Wireless Sensor Network systems, a particular challenging problem is how to dynamically organize the sensors network and route sensed information from the field sensors to a target system. A target tracking system is often required to ensure continuous monitoring, there always exist nodes that can detect the target along its trajectory (e.g., with low detection delay or high coverage level). Therefore, the most stringent criterion of target tracking is to track with zero detection delay or 100 percent coverage. Since nodes often run on batteries that are generally difficult to be recharged once deployed, energy efficiency is a critical feature of WSNs for the purpose of extending the network lifetime.The prime motivation of the work proposed to develop a energy efficient target tracking schemes. Toward this objective, the project uses a new energy-efficient dynamic optimization-based sleep scheduling and target prediction technique for large scale sensor networks. A probability-based prediction and optimization-based sleep scheduling protocol (PPOSS) is proposed to improve energy efficiency. A cluster-based scheme is exploited for optimization-based sleep scheduling. At every sampling instant, only one cluster of sensors that located in the proximity of the target is activated, whereas the other sensors are inactive. To activate the most appropriate cluster a non myopic rule is used based on not only the target state prediction but also its future tendency. Finally, the effectiveness of the proposed approach is evaluated and compared with the state-of-the-art protocols in terms of tracking accuracy, inter-node communication, and computation complexity.

Selective forwarding for energy-efficient target tracking in sensor networks

Selective communication (censoring) strategies allow nodes in a sensor network to discard low importance messages with the purpose of saving energy that can be used for transmitting more important messages later. In this paper we apply simple selective policies based on Markov Decision Processes to a distributed target tracking scenario based on particle filters and the Information-Driven Sensor Querying (IDSQ) scheme. The resulting algorithm is combined with other energy-efficient schemes, such as data aggregation or data fusion, extending the action space of these techniques. Our simulation work shows that the network lifetime can be substantially increased while keeping a low tracking error. And even more, selecting the sampling rate properly, lifetime is prolonged without increasing the tracking error.