Device-Free Localization Using Enhanced Channel Selection and a Distance-Based Elliptical Model

Device-free localization (DFL), which can detect and locate a person by measuring the changes in received signals, is one of the primary techniques in wireless sensor networks. Recently, research on fingerprint-based localization in changing environments has been receiving increasing attention. However, when the environment changes due to furniture or other objects are moved, there is still much room for localization accuracy improvement in fingerprint-based DFL. In this paper, we propose a novel DFL algorithm for changing environments: this algorithm features an enhanced channel-selection method and adopts the logistic regression classifier to improve the localization accuracy. The proposed frequency channel-selection method selects two correlated channels with higher Pearson correlation coefficient both in the training and testing procedures, which would be more robust to the environmental change. Meanwhile, the logistic regression classifier could counteract the negative influence on the localization accuracy, without the need for rebuilding the database in fingerprint-based DFL. Experimental results demonstrate that the logistic regression classifier has the lowest error rate among three related methods (k-nearest neighbours classifier, linear discriminant analysis classifier, and random forests classifier). In addition, the localization accuracy has been further improved by the proposed DFL algorithm than by the other state-of-the-art fingerprint-based methods.

Device-free localization (DFL) systems that rely on the wireless received signal strength indicator (RSSI) metric have been reported in literature for almost a decade. Histogram distance-based DFL (HD-DFL) techniques that operate by constructing RSSI histograms are highly effective as they can localize stationary and moving people in both outdoor and complex indoor environments. A key step in the histogram approaches is the estimation of the difference between the “long-term” and “short-term” histograms. The existing HD-DFL methods use either Kullback–Leibler or the subsequent improvement, kernel distance, to measure this difference. This paper is the first known work to compare an extensive range of histogram distance metrics within a DFL context and demonstrate how a judicious selection of a distance metric can significantly increase the performance of an HD-DFL system. The results from practical implementation in two different environments show that some distance metrics perform considerably better than the kernel distance when used for existing DFL techniques, such as radio tomographic imaging (RTI) and SpringLoc, with the overall median tracking error reducing by up to 25%.

Radio-based device-free localization (DFL) for indoor environments are based on signal anomalies due to obstacles on the line-of-sight (LOS) between previously applied reference nodes. They take use of radio signal strength (RSS) attenuation (radio tomographic imaging, RTI) or variations caused by shadowing and small-scale fading effects. An advanced methodology using redundant RSS information for each pair of reference nodes is proposed. The method uses a moving RSS variance estimator and antenna diversity with four uncorrelated 2.4 GHz radio channels to predict the user's position. With the redundant RSS values the accuracy of user detection is increased by more than 50 % while the number of false positives is reduced to zero. Tracking results in a hallway test bed illustrate the behavior and improvements of the applied redundant sensor technique.

An enhanced RSS-distance-angle weighted geometric filter for device-free localization

With the success of wireless sensor networks (WSNs), traditional engineering and infrastructure industries are starting to develop solutions using WSN technologies. One of the main challenges of designing and developing WSNs for industrial monitoring and control is satisfying their strict reliability requirements. In this paper, we present a network-level reliability model, namely, end-to-end data delivery reliability (E2E-DDR), for estimating and optimizing the reliability performance of WSNs. In the E2E-DDR model, a framework is presented for capturing the mapping function between the packet reception ratio, background noise, and received signal strength (RSS). We use an alpha-stable distribution to accurately represent the background noise and a modified log-normal path loss model to more realistically describe the RSS. We also report a comprehensive performance evaluation performed by applying the E2E-DDR model in a real-world case study to estimate the network-level reliability and optimize the WSN deployment parameters. Note to Practitioners —The goal of this paper is to improve the estimation and optimization of the network-level reliability performance of industrial wireless sensor networks (WSNs) to satisfy their strict control requirements. In harsh industrial application scenarios, many factors affect radio link quality, such as RF transmit power, communication distance, and random background noise. Existing link quality estimation approaches mainly focus on providing a smoothed estimation of radio link quality without any solution for optimizing communication reliability to satisfy certain requirements. This paper suggests a new approach in which WSN nodes are used to measure and estimate the parameters of the scenario in which the nodes will be deployed and then to estimate and optimize the worst case reliability (which is a lower-bound value rather than a smoothed value) to ensure that the network is qualified. Through a real-world case study, we demonstrate how to estimate the lower bound on reliability and how to optimize the reliability by computing the maximum deployment distance between nodes as an example. The experiments suggest that this approach is feasible.

Localization is a key issue for wireless sensor network. The traditional localization methods achieve high location accuracy using received signal strength (RSS) in line of sight (LOS) environment. But the localization accuracy degrades significantly in nonline of sight (NLOS) environment. So the localization in NLOS environment is one of the most challenging for wireless sensor network. In this paper, we propose an RSS based localization algorithm in NLOS environment. A NLOS identification algorithm is firstly proposed. This algorithm does not need the parameters of the NLOS in prior. Then we correct the NLOS measurements by subtracting the mean of NLOS errors. Finally, the Kalman filter is employed to mitigate the process noise. In simulations, we demonstrate that the proposed algorithm achieves higher localization accuracy compared with other methods.

As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.

The growing awareness of the strategic value of service along with higher product performance expectations has directed many industries to adopt service centric competitive strategies in managing their supply chains. Service innovation based on currently available advanced technologies has become critical in improving enterprises’ competitive strengths. Wireless sensor network (WSN) technology, which can provide a mobile, scalable, and reliable monitoring solution, is emerging as a tool for service innovation in supply chain management. This paper provides an overview of wireless sensor network technology and discusses how it can benefit modern industries. The fundamental theories of WSN are introduced as well as an overview of the development. The impact of information technologies on supply chain management and service innovation is then briefly discussed. Much emphasis is placed on the feasibility, procedures, and critical challenges of implementing WSN in supply chain management innovation. Current and future applications of WSN are also provided, followed by a case study demonstrating the application potentials of WSN for service innovation in the healthcare industry.

Device-free localization (DFL) is a technique of locating targets via wireless sensor networks (WSNs) without carrying any electronic devices. This article introduces a device-free vehicle localization system that can determine the position and 2-D size of a dynamic vehicle in a monitoring region via ultra wide band (UWB) networks. Specifically, our system involves an obstructed recognition model for detecting the state of links, a state-input radio tomography imaging (SI-RTI) algorithm for reconstructing a high-resolution image, a contour threshold method for predicting the size of a vehicle target, and a gradient-advance-retreat (GAR) optimization algorithm for improving accuracy. Field experiments show that our system can estimate the position and size of a moving vehicle with a location root-mean-square error (RMSE) of less than 1.1 m and a root-mean-square overlap degree of more than 75% between the prediction and the real target.

Due to the wide deployment of wireless local area networks (WLANs) and the easy acquirement of received signal strength (RSS), indoor localization based on RSS has attracted considerable attention in both academia and industry. In this paper, we extract the scatter factor from RSS and then propose a novel indoor localization scheme based on location fingerprinting and curve fitting techniques. The scheme is set up in two phases. In the offline phase, we create a fingerprint and apply the curve fitting technique to construct fitted RSS-distance functions in LOS (Line Of Sight) and NLOS (None Line Of Sight) condition, respectively. In online positioning phase, the proposed optimized fingerprinting-based localization algorithm (OFPL) uses trilateration technique to assist the fingerprinting localization in choosing the proper location of the receiver. Through conducting field and extensive experiments, we can draw the conclusion that our proposed algorithm can obtain some improvement in localization accuracy compared with the classical fingerprinting-based localization.

Device-free passive (DFP) localization systems are a key solution for location-based services because they do not require any wireless device on a human body. Most of the existing DFP localization systems are based on the received signal strength (RSS) measurement only. However, the localization accuracy of RSS only-based systems is easily affected by the spatial and temporal variances of RSS due to multipath fading and noise, even in a static environment. In this paper, we propose a novel localization system for DFP using signal subspace eigenvectors from an antenna array. We present a fingerprinting technique using multiclass support vector machines (SVMs) based on a combination of array signal features with spatial and temporal averaging. We then evaluate the localization accuracy of our proposed system in different propagation environments: line-of-sight (LOS) and non-line-of-sight (NLOS). In addition, we analyze two types of receive antenna placement: centralized and distributed antennas. The experimental results show that the localization accuracy can be improved by the proposed system, particularly in the centralized antenna case. Moreover, they show that the proposed system can improve localization accuracy compared to the conventional RSS-only based system.

Wireless Sensor Network (WSN) have been attracting increasing interest for supporting a new generation of ubiquitous computing systems with great potential for many applications. However, the communication paradigms in WSNs differ from the ones associated to traditional wireless networks, triggering the need for efficient wireless communication technology. Several wireless technologies have emerged ranging from short and medium distance. Bluetooth, ZigBee and Impulse Radio Ultra Wide Band (IR-UWB) are three popular short range wireless communications. Due to its various features and advantages (especially low power consumption and low complexity), IR-UWB is a very promising wireless communication technology for WSN. In this paper, we evaluate the main features and advantages of this new technology (IR-UWB) for WSN in terms of transmission time, data coding and power consumption compared to Bluetooth and ZigBee. To analyze and evaluate the main features and advantages of IR-UWB as an efficient short range wireless communication technology for WSN, we used MiXiM platform under OMNet++ simulator.

This paper mainly introduces the application of multichannel communication technology in wireless sensor network. The corresponding solutions are proposed based on the analysis of the structure and characteristics of wireless sensor, and the multi-channel MAC protocol in wireless sensor network. In this paper, multi-channel communication technology in wireless sensor network (WSN) is studied, the existing problems and reasons are analysed, and some solutions to further promote the further development of the technology are put forward. With the wide application of WSN, the security of WSN has been paid more and more attention. In this paper, the existing time synchronization protocol, from the security point of view, and the fault tolerance design of the fits protocol. The protocol has an intrusion detection mechanism, which can detect the attack of malicious nodes in time synchronization. Aiming at the small deviation of slow attack and the problem of time synchronization error, information fusion is based on the quality principle, and according to the time synchronization information of different nodes, we make random weighted average correction for local time nodes. The simulation results show that the fits time synchronization protocol has good intrusion detection and fault tolerance capability.

This work presents a solar power based water quality monitoring wireless sensor network (WSN) technology, to screen water quality over various destinations as an ongoing application, an astounding framework engineering comprised by disseminated sensor nodes and a base station is proposed. The model and base station are associated utilizing WSN innovation like Zig-Bee. Modeling and implementation of a proposed model using one node powered by solar cell and WSN technology is the challenging work. Data collection by different sensing element at the node side like pH, turbidity conductivity, saltiness and temperature is sent by means of WSN to the base station. Information gathered from the distant site can be shown in visual organization too as it very well may be examined utilizing diverse reproduction apparatuses at base station. This original framework has benefits like no fossil fuel byproduct, low power utilization, more adaptable to send data at far off site, etc.

This paper focuses on the spatial inhomogeneity of shadow fading in radio tomographic imaging. An enhanced multi-scale model is developed to characterize the spatial inhomogeneity of shadow fading, which aims at offering an insight into the correlation between the spatial scales and the fading levels, thus enhances the capability of capturing the shadow fading components. It should be highlighted that, when the RSS value of a link increases significantly, the proposed model can predict the impossibility of LOS targets with high probability, which can benefit the accuracy improvement of localization. This is our new contribution to the existing multi-scale shadow fading models. The experimental results are reported to validate the proposed model in the context of target localization with radio tomographic imaging.