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In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results. [ABSTRACT FROM AUTHOR]

Due to the resource limitation and low performance of sensor node, research works of multi-target tracking became a hot spot in the applications of wireless sensor networks. Here, we propose an algorithm named augmented state-based multi-target tracking algorithm. To augment the state of the target tracking, augmented state-based multi-target tracking algorithm can effectively reduce the computational complexity of data association. Then, multi-target tracking in wireless sensor networks can be implemented by augmented state-based multi-target tracking algorithm as a simplified Bayesian estimation method is adopted. The simulation of multi-target tracking in wireless sensor networks demonstrates that augmented state-based multi-target tracking algorithm has less computation and higher accuracy than traditional method, especially in the implementation of maneuvering targets with intersection. [ABSTRACT FROM AUTHOR]

In this article, the target tracking problem in a wireless sensor network with nonlinear Gaussian signal intensity attenuation model is considered. A Bayesian filter tracking algorithm is presented to estimate the locations of moving source that has unknown central signal intensity. This approach adopts a measurement conversion method to remove the measurement nonlinearity by the maximum likelihood estimator, and a linear estimate of the target position and its associated noise statistics obtained by the Newton–Raphson iterative optimization steps are applied into the standard Kalman filter. The Monte Carlo simulations have been conducted in comparison with the commonly used extended Kalman filter with an augmented state that consists of both the original target state and the augmentative central signal intensity. It is observed that the proposed measurement-converted Kalman filter can yield higher accurate estimate and nicer convergence performance over existing methods. [ABSTRACT FROM AUTHOR]

We investigate the problem of tracking mobile targets in wireless sensor networks. We propose an advanced auxiliary delayed-weight particle filter algorithm (ADWPF). We make a deep study on the evolvement of particles and formally define the tree-like structure relationship among particles based on observations. Most importantly, we add some auxiliary particles to these structures formed by sampled particles in order to obtain more efficient structures. Based on the newly tree-like structures formed by auxiliary particles and sampled particles, we design a well efficient delayed-weight algorithm with linear computation cost. Experiment results demonstrate that our algorithm can greatly improve the tracking accuracy of a mobile target, compared with bootstrap filter, auxiliary particle filter, and another delayed-weight particle filter. [ABSTRACT FROM AUTHOR]

Currently, many services benefit greatly from the availability of accurate tracking. Tracking in wireless sensor networks remains a challenging issue. Most tracking methods often require a large number of anchors and do not take advantage of potential localization information, leading to poor accuracy. To solve this problem, this paper proposes a Novel Monte Carlo-based Tracking (NMCT) algorithm with area-based and neighbor-based filtering, which fully extracts the proximity information embedded in the neighborhood sensing. We describe the entire system design in detail and conduct extensive simulations. The results show that the proposed algorithm outperforms the typical schemes under a wide range of conditions. [ABSTRACT FROM AUTHOR]

The optimal distributed tracking control algorithms over nonlinear cooperative wireless sensor networks (WSNs) are presented in this paper. In order to solve transfer delay and packet loss problem, the architecture of wireless active sensor (WAS) is employed, where a state estimator is embedded, which can provide the needed state information. Furthermore, the optimal distributed tracking control algorithm is proposed. By solving the matrix equations and the adjoint difference equations, the optimal control law can be obtained easily, in which an increment integral regulator is designed to implement tracking target without steady-state error and a nonlinearity compensation term is designed to compensate for the effect produced by system nonlinearity. Moreover, the observer-based dynamical algorithm is given considering the physically unrealizable disturbance states and the unavailable sensor states. Finally, computer simulations are carried out with application to two nonlinear pendulums, which prove that the algorithm is effective and easy to implement, and the system achieves the desired performance based on tradeoff between tracking error and control energy consumption. [ABSTRACT FROM AUTHOR]

Target tracking is an important application of wireless sensor networks where energy conservation plays an important role. In this paper, we propose an energy-efficient sensor activation protocol based on predicted region technique, called predicted region sensor activation algorithm (PRSA). The proposed algorithm predicts the moving region of target in the next time interval instead of predicting the accurate position, by analyzing current location and velocity of the target. We take these nodes within the predicted region as waiting-activation nodes and establish activation strategy. The fewest essential number of sensor nodes within the predicted region will be activated to monitor the target. Thus, the number of nodes that was involved in tracking the target will be decreased to save energy and prolong the network's operational lifetime. The simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In ubiquitous healthcare systems, machine-to-machine (M2M) communication promises large opportunities as it utilizes rapidly developing technologies of large-scale networking of devices for patient monitoring without dependence on human interaction. With the emergence of wireless multimedia sensor networks (WMSNs), M2M communications improve continuous monitoring and transmission and retrieval of multimedia content such as video and audio streams, images, and sensor data from the patient being monitored. This research deploys WMSN for continuous monitoring of target patients and reports tracking for preventive ubiquitous healthcare. This study performs optimization scheme movement coordination technique and data routing within the monitored area. A movement tracking algorithm is proposed for better patient tracking techniques and aids in optimal deployment of wireless sensor networks. Results show that our optimization scheme is capable of providing scalable and reliable patient monitoring results. [ABSTRACT FROM AUTHOR]

Multi-target tracking is a representative real-time application of sensor networks as it exhibits different aspects of sensor networks such as event detection, sensor information fusion, multihop communication, sensor management, and real-time decision making. The task of tracking multiple objects in a wireless sensor network is challenging due to constraints on a sensor node such as short communication and sensing ranges, a limited amount of memory, and limited computational power. In addition, since a sensor network surveillance system needs to operate autonomously without human operators, it requires an autonomous real-time tracking algorithm which can track an unknown number of targets. In this paper, we develop a scalable real-time multi-target tracking algorithm that is autonomous and robust against transmission failures, communication delays, and sensor localization error. The algorithm is based on a rigorous probabilistic model and an approximation scheme for the optimal Bayesian filter. In particular, an extensive simulation study shows that there is no performance loss up to an average localization error of 0.7 times the separation between sensors and the algorithm tolerates up to 50% lost-to-total packet ratio and 90% delayed-to-total packet ratio. The proposed algorithm has been successfully applied to real-time multi-target tracking problems using wireless sensor networks. [ABSTRACT FROM AUTHOR]

Target tracking is an important application of wireless sensor networks where energy conservation plays an important role. In this paper, we propose an energy-efficient sensor activation protocol based on predicted region technique, called predicted region sensor activation algorithm (PRSA). The proposed algorithm predicts the moving region of target in the next time interval instead of predicting the accurate position, by analyzing current location and velocity of the target. We take these nodes within the predicted region as waiting-activation nodes and establish activation strategy. The fewest essential number of sensor nodes within the predicted region will be activated to monitor the target. Thus, the number of nodes that was involved in tracking the target will be decreased to save energy and prolong the network's operational lifetime. The simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are widely used in detecting, locating, and tracking moving objects. The cheap, low-powered, and energy-limited sensors that are set up in large areas may consume large portion of energy and disable the whole network. In this paper, a new energy-efficient method based on Distributed Incremental Gene Expression Programming (DI-GEP) is proposed to collaboratively mine moving patterns of moving targets in order to turn on/off some sensor nodes at certain time to save energy further. Meanwhile, an adjustable sliding window is designed to quickly train the latest collected location data in order to improve the efficiency of DI-GEP. The simulation results show that the proposed method effectively prolongs the network lifetime by around 25% compared with the EKF and ECPA. [ABSTRACT FROM AUTHOR]