A hybrid tracking system for ZigBee WSNs

In the wireless sensor network (WSN) localization methods based on Received Signal Strength Indicator (RSSI), it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.

We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.

SummaryIndoor localization in wireless sensor networks is a challenging task. Static localization and moving target monitoring are addressed using ensemble learning method and received signal strength indicator. The suggested strategy combines several regression trees to have better performance. This solution has been experimentally evaluated using real measurements in an office room. The performance results have been analyzed through a comparison with learning‐based localization algorithms currently available in the literature. The analysis shows that the adopted solution is simple in term of computation, accurate and robust to environmental variation.

Localization technology based on received signal strength indication (RSSI) is a low cost and low complexity technology, and it is widely applied in distance-based localization of wireless sensor networks (WSNs). The error of existed localization technologies is great. This paper presents an N-times trilateral centroid weighted localization algorithm (NTCWLA), which reduces the error greatly. Considering the instability of RSSI, we use the weighted average of many RSSIs as an indicator of the current RSSI. To improve the accuracy we select n (n=3) reliable beacon nodes to increase the localization times. Then we calculate the distances between reliable beacon nodes and the mobile node using an empirical formula. This new algorithm locates the mobile node N times with the trilateral centroid algorithm. Finally, we take the weighted average of the filtered reference coordinates as the mobile node's coordinates. We experiment with the STM32W108 chip, which supports the IEEE802.15.4 protocol. Our experimental results indicate that this new algorithm is more accurate than the trilateral centroid algorithm.

In this paper, we design a new distributed angle estimation method for localization in wireless sensor networks (WSNs) under multipath propagation environment. We employ a two-antenna anchor that can emit two linear chirp waves simultaneously, and propose to estimate the angle of departure (AOD) of the emitted waves at each receiving node via frequency measurement of the local received signal strength indication (RSSI) signal. An improved estimation method is further proposed where multiple parallel arrays are adopted to provide the space diversity. The proposed methods rely only on radio transceivers and do not require frequency synchronization or precise time synchronization between the transceivers. More importantly, the angle is estimated at each sensor in a completely distributed manner. The performance analysis is derived and simulations are presented to corroborate the proposed studies.

An accurate prediction method for moving target localization and tracking in wireless sensor networks

Wireless Sensor Network (WSN) localization has been bloomed as an active area of research in this era. In fact, WSN differs from the traditional network in diverse aspects and therefore requires novel algorithms for addressing specific challenges like the identification of the unknown node location in hazardous environments. In this paper, a new localization model is introduced by the range-based localization approach via an optimization-assisted deep learning model. The proposed work undergoes two major phases: (a) training phase and (b) localization phase. The trained Deep Neural Network (DNN) with the measured distance-based features like the “Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI)” find out the place of the unknown node more precise. Further, to enhance the localization accuracy, the weight of DNN is tuned via a novel hybrid optimization algorithm named as Lion-Assisted Firefly Algorithm (LAFA) model. The proposed LAFA is the concept of both the Lion Algorithm (LA) and Firefly Algorithm (FF). Finally, the evaluation of the presented work is done in terms of error measures.

The localization of wireless sensor networks (WSN) is an important field of research study, that is gaining considerable attention. This growth in wireless sensor network installations is expected to expand more in the future. Indoor localization based on the Received Signal Strength Indicators (RSSI) is a well-known localization technique used in WSN systems due to its availability and simplicity. Radio location fingerprinting is one of the most effective of the many indoor positioning techniques due to its accuracy and low cost. In this study, we investigate the possibility of applying RSSI fingerprinting based Artificial Neural Network (ANN) jointly to determine the correct location of an object or person in indoor environments. According to our analysis and experiment result, Fingerprinting techniques can achieve accurate localization with high interference in a computer laboratory. In addition, this research uses a small-structure neural network (one hidden layer) to provide efficient computation with restricted resource requirements.

This paper studies the problem of mobile target localization in wireless sensor networks and proposes a mobile target localization algorithm based on dynamic ring structure. Firstly, a multiple rings structure is constructed in the network. To obtain the optimal division, a Genetic Algorithm based on elitist preservation strategy is used to set the ring width. Then, a binary coding algorithm is proposed to assign binary code for each ring and determine the target appearing area using binary code sequence. Finally, a comprehensive set of simulations are presented. We conclude that the proposed localization algorithm yields excellent performance.

Indoor localization is an important issue in wireless sensor networks for a very large number of applications. Recently, localization techniques based on the received signal strength indicator (RSSI) have been widely used due to their simple and low cost implementation. In this paper, we propose an algorithm for localization in wireless sensor networks based on radio-location fingerprinting and kernel methods. The proposed method is compared to another well-known localization algorithm in the case of real data collected in an indoor environment where RSSI measures are affected by noise and other interferences.

This paper considers the problem of decentralized localization in wireless sensor networks (WSNs). The problem is set by combining both radio-fingerprints and accelerometer information. It is then resolved in a decentralized manner where the surveillance area is divided into several zones equipped with calculators. The proposed algorithm consists of computing first estimates using received signal strength indicator (RSSI)-fingerprints, then correcting them using accelerometer information leading to more accurate estimates. Computations are performed using the interval analysis and the Kalman filter in a simulated environment.

Based on the requirements of low energy consumption and high bandwidth efficiency, a primary area of study is how to improve the estimation accuracy and tracking performance for target localization in wireless sensor networks. In this paper, we propose a weighted sign algorithm (WSA) for positioning and tracking in wireless sensor networks. In terms of the reliability of distance estimation, the location estimation of a target is formulated as a weighted least squares (WLS) problem, which is solved based on the WSA in an iterative and decentralized manner. We also propose a cooperative message-passing algorithm for inter-sensor information exchange, adaptively picking the participating sensor nodes, and making the paths for cyclic routing in the network. On each routing path, a target's location estimate is updated on each cycle and continues among the participating sensor nodes that surround the target. To ensure that the WSA-based scheme has a convergence behavior, a convergence analysis is given. Computer simulation results demonstrate that, as compared with previous methods, our proposed scheme has a faster convergence rate, better location accuracy, and improved tracking performance.

Accurate localization of nodes in a Wireless Sensor Network (WSN) is imperative for several important applications. The use of Global Positioning Systems (GPS) for localization is the natural approach in most domains. In WSN, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes’ deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of Received Signal Strength Indicator (RSSI) exist in literature but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a Machine Learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up.KeywordsLocalizationMultilaterationRandom forestRSSIWSN

Wireless sensor network localization is an important area that attracted significant research interest. Recently, received signal strength indicator (RSSI)-based range measurement technology is widely used in sensor network for its low-cost. But the radio signal transmission is influenced by the factors of channel attenuation and shielding obstacles, etc. Therefore, the accuracy of RSSI-based localization algorithm is not high. In this paper, a new localization algorithm which is based on the probability distribution model is proposed. Firstly, the anchor node which is nearest to the unknown node is made as the reference node in order to correct the RSSI-based ranging distance. Secondly, the common communication area where the unknown nodes may exist is divided into a number of spatial grids. And then, the confidence degree of each grid is calculated out according to the probability model of radio signal space transmission. Finally, based on the centroids of spatial grids, the coordinates of the unknown nodes are calculated out. Through this algorithm, the localization accuracy is improved greatly compared with conventional RSSI-based localization algorithms, which has been demonstrated by the simulating results.

This chapter describes one of the major challenges in technology advancement of Wireless Sensor Networks (WSNs), i.e., Localization in WSNs. In recent years, sensor node localization is an emerging research area in WSNs. The sensor data become useless, if we do not know the location of the reporting node. Coordinates determination of the sensor node is a challenging problem and it is referred as localization problem. The nodes which has unknown coordinates is termed as target Nodes. Various localization methods can be utilized to find out the location of sensor nodes, those coordinates are not known in a system/network. Efficient WSN localization can be treated as multi-dimensional optimization problem which can be addressed through population based stochastic techniques, which involves the minimization of a function of differences between Euclidean and measured distance between sensor nodes. In this chapter various connectivity, range information and mobility based localization algorithms have been discussed. For optimizing the results of these algorithms, various computational intelligence (CI) based optimizing algorithms like Particle Swarm Optimization, Biogeography Based Optimization, Firefly Algorithm and Genetic Algorithm have been discussed. A choice between these algorithm is influenced by the localization accuracy expected to be and convergence rate.