Distributed Angle Estimation for Localization in Wireless Sensor Networks

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.

The localization of wireless sensor nodes is one of the key technologies in wireless sensor networks (WSN). Recently, a distributed localization method has been proposed based on a novel concept of angle of departure (AOD) estimation, which can be employed by e.g., the single antenna sensors to estimate the angle information and achieve localization. Based on the AOD estimation, in this paper, we introduce a synthetic uniform circular array (SUCA) synthesized by a rotary antenna on anchor to get the distributed localization of the sensors in multi-path environments. The rotated operation of antenna could create the SUCA with a larger aperture to deal with AOD of multipath components (MPCs) and relax the condition that array aperture should be greater than the number of MPCs. This new localization method only relies on radio transceivers without requiring extra measuring equipments, and the sensor nodes conduct localization in a completely distributed manner instead of centralized processing, which is suitable for large-scale WSN. Meanwhile, the influence of carrier frequency offset (CFO) between the transceivers is also considered and it is eliminated by utilizing the signal emitted from an auxiliary antenna.

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.

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.

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.

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.

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.

A distributed localization method has been proposed recently based on a novel concept of angle of departure (AOD) to estimate the angle information and achieve localization. Based on the AOD estimation, we introduce a synthetic uniform circular array (SUCA) synthesized by a rotary antenna to get the distributed localization of the sensors in multi-path environments. The rotated operation of antenna can create the SUCA with a larger aperture to deal with AOD of multi-path components (MPCs) and relaxes the condition that array aperture should be greater than the number of MPCs. This method only relies on radio transceivers without requiring extra measuring equipments, and the sensor nodes conduct localization in a completely distributed manner instead of centralized processing.

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.

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.

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

Received Signal Strength Indicator (RSSI)-based indoor localization is attractive for its low cost and ease of deployment, yet its accuracy is often compromised by multipath propagation, non-line-of-sight conditions, and environmental noise. Traditional filtering approaches, such as Kalman and particle filters, rely on precise noise models and struggle under sparse sensing or model mismatch. In this study, we recast RSSI localization as a sequential state estimation problem and propose a reinforcement learning-based reduced-order estimator (RL-ROE). By combining model-driven dynamics with data-driven policy correction, the method achieves improved accuracy and robustness under noisy indoor environments, demonstrating its potential as a scalable solution for real-time localization in wireless sensor networks.

Wireless sensor networks have important military and civilian applications where determining the geographical location of a specific event is crucial. High-accuracy event localization is a challenging task that depends on the type of event to track. Hence, this work proposes a general approach for achieving event localization in wireless sensor networks, keeping independence from the kind of signals emitted by such events. The proposed approach estimates the distance between an event and a sensor node according to the intensity of the signal perceived by that sensor. In addition, lateration and fingerprinting techniques are implemented to determine the location of particular events. In order to evaluate our proposal, acoustic event localization is implemented in a real wireless sensor network based on the Arduino hardware. Results from this specific implementation show an event localization error of approximately 0.2 m inside a 2×2 m area.

Localization is one of the most challenging and important issues in wireless sensor networks (WSNs), especially if cost-effective approaches are demanded. In this paper, we present intensively discuss and analyze approaches relying on the received signal strength indicator (RSSI). The advantage of employing the RSSI values is that no extra hardware (e.g. ultrasonic or infra-red) is needed for network-centric localization. We studied different factors that affect the measured RSSI values. Finally, we evaluate two methods to estimate the distance; the first approach is based on statistical methods. For the second one, we use an artificial neural network to estimate the distance.