Received Signal Strength Indicator Distance Research Articles

WSN Localization Using Similarity-Based Ricci Flow Embedding

In low-cost Wireless Sensor Networks (WSN), node localization in a noisy environment is a significant problem as additional hardware like GPS is avoided due to high cost. The pairwise distances between sensor nodes are estimated using the Received Signal Strength Indicator (RSSI) because of the low computational complexity and cost-effectiveness. However, the RSSI distance estimates are rough and fluctuate owing to various environmental conditions like noise, interference, and fading. This results in non-Euclidean pairwise distance estimates. To compute the actual inter-node distances, we consider the manifold assumption that the nodes embedded in the low dimensional space apparently lie in an unknown high dimensional manifold. The inter-node distances can be estimated by reducing the dimension through manifold learning. This work proposes a localization technique based on Similarity-Based Ricci Flow Embedding (SBRFE), an unsupervised manifold learning method that reduces the non-Euclidean artefacts of a distance matrix and converts it into a Euclidean one. Each inter-node distance is considered independently, and the corresponding sectional curvature is updated iteratively till all the distances become Euclidean. Experiment results show that the SBRFE algorithm outperforms all the conventional manifold learning algorithms and can localize all the sensor nodes with an increased accuracy up to 94.55%.

Practical approximate indoor nearest neighbour locating with crowdsourced RSSIs

In the indoor space, finding the nearest neighbour is of great importance in location-based services. Received Signal Strength Indication (RSSI) has received much attention due to its simplicity and compatibility with existing hardware, which has been widely used for indoor localization. Existing indoor nearest neighbour search methods are based on the real walking distance, which need ground survey and much labor work to measure many real distances. Crowdsourcing is a low-cost and efficient way to collect the RSSI of indoor space without expert surveyors and designated coordinates for RSSI collection points. The crowdsourced RSSIs can reflect the location of indoor objects and RSSI-based localization method is the simplistic method as it needs low hardware requirements, low deployment cost and no survey indoor distance. So we study how to search the nearest neighbour of indoor objects with crowdsourced RSSIs. To address this problem, we propose a graph with interval weights, called I-graph, which can connect the RSSIs and represent the topology of indoor space. We also construct a search tree index D-tree, which can index the graph with interval weights and search the nearest neighbour objects efficiently. We also propose a novel distance metric for RSSI and study the relationship between the RSSI distance and the indoor distance. To locate nearest neighbour of indoor objects with crowdsourced RSSIs, we devise efficient search algorithms and pruning strategies for computing the nearest neighbour query. We demonstrate the efficiency and effectiveness of the proposed solution through extensive experiments with two real data sets.

IHRNL: Iterative Hessian-based manifold regularization mechanism for localization in WSN

In this paper, we propose an iterative Hessian regularization technique for node localization in wireless sensor network (WSN). The technique is based on the observation that received signal strength indicator (RSSI)-based node localization problem using a few location-aware anchor nodes, and a large number of location-unaware non-anchor nodes can be modeled as a manifold regularization-based regression problem. A signal transmitted from a node attenuates with distance. However, this attenuation is not uniform due to noise and other detrimental channel conditions. This apparently embeds the nodes in an unknown high-dimensional space. The proposed technique assumes that these nodes are the data points lying on a high-dimensional manifold and the locations of these nodes can be obtained accurately through an iterative Hessian regularized regression. In Hessian regularization, temporary location values are assigned to each non-anchor node, which are further refined in the subsequent iterations. This is followed by localized Procrustes analysis to offset the affine transformation on temporary location values. To validate the proposed technique, we deployed TelosB motes to form a WSN. The beacons broadcast from nodes were used for finding the RSSI distance estimates followed by the localization process. We observed that our proposed technique is able to localize the sensor nodes accurately and outperformed the baseline supervised learning, Hessian and iterative Laplacian regularization methods with an increase in accuracy of around $$70\%$$ over the baseline supervised method.

An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model

Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are driven from the correction formula, and by utilizing the previous factors in the unknown node, enhanced centroid positioning algorithm is applied to calculate the final node positioning coordinates. Moreover, in this study, we used Bluetooth Low Energy (BLE) beacons to meet the principle of low energy consumption. The experimental results of the proposed enhanced centroid positioning algorithm have a significantly lower average localization error (ALE) than the currently existing algorithms. Also, the proposed technique achieves higher positioning stability than conventional methods. The proposed technique was experimentally tested for different received RSSI samples’ number to verify its feasibility in real-time. The proposed technique’s positioning accuracy is promoted by 80.97% and 67.51% at the office room and the corridor, respectively, compared with the conventional RSSI trilateration positioning technique. The proposed technique also improves localization stability by 1.64 and 2.3-fold at the office room and the corridor, respectively, compared to the traditional RSSI localization method. Finally, the proposed correction model is totally possible in real-time when the RSSI sample number is 50 or more.

Multi-Stage Pedestrian Positioning Using Filtered WiFi Scanner Data in an Urban Road Environment.

Since widespread applications of wireless sensors networks, low-speed traffic positioning based on the received signal strength indicator (RSSI) from personal devices with WiFi broadcasts has attracted considerable attention. This study presents a new range-based localization method for outdoor pedestrian positioning by using the combination of offline RSSI distance estimation and real-time continuous position fitting, which can achieve high-position accuracy in the urban road environment. At the offline stage, the piecewise polynomial regression model (PPRM) is proposed to formulate the Euclidean distance between the targets and WiFi scanners by replacing the common propagation model (PM). The online stage includes three procedures. Firstly, a constant velocity Kalman filter (CVKF) is developed to smooth the real-time RSSI time series and estimate the target-detector distance. Then, a least squares Taylor series expansion (LS-TSE) is developed to calculate the actual 2-dimensional coordinate with the replacement of existing trilateral localization. Thirdly, a trajectory-based technique of the unscented Kalman filter (UKF) is introduced to smooth estimated positioning points. In tests that used field scenarios from Guangzhou, China, the experiments demonstrate that the combined CVKF and PPRM can achieve the highly accurate distance estimator of <1.98 m error with the probability of 90% or larger, which outperforms the existing propagation model. In addition, the online method can achieve average positioning error of 1.67 m with the much better than classical methods.

Learning RSSI Feature via Ranking Model for Wi-Fi Fingerprinting Localization

Wi-Fi fingerprinting is widely used in indoor localization due to the ubiquitous availability of Wi-Fi infrastructure in indoor environments. The basic assumption of fingerprinting localization is that the received signal strength indicator (RSSI) distance is consistent with the location distance. However, due to the fluctuation of Wi-Fi signals in indoor environments, the nearest neighbors selected using the RSSI distance may not be those whose corresponding locations are nearest to the target, which could lead to a large localization error. In this paper, we propose a novel fingerprinting method for indoor localization by transforming raw RSSI into features with a learned non-linear mapping function. To learn such mapping function, we design a triple loss function that measures the difference between the rank of RSSI distance and that of location distance. By minimizing the loss function iteratively, we can learn the non-linear mapping function with the gradient boosting regression forest (GBRF) method. Experiments have been conducted in a complex environment and experimental results show that our method outperforms the state-of-the-art methods.

Virtual fingerprint and two-way ranging-based Bluetooth 3D indoor positioning with RSSI difference and distance ratio

ABSTRACTThis paper proposes a new method of 3D indoor positioning based on Bluetooth devices. Virtual Received Signal Strength Indicator (RSSI) fingerprint is built on RSSI difference and distance ratio. The concepts and stability of 1D and 2D virtual fingerprints are introduced, and the property of uniqueness for virtual fingerprint is proved. 2D virtual fingerprint is more stable than that of 1D for indoor positioning. Various positioning algorithms, based on virtual fingerprint and one-way ranging, are discussed, such as NN, KNN, Multi-Step and nonlinear optimization, inference and regression. Multi-Step algorithm holds the best performance in time and error. The proposed method utilizes virtual fingerprint and two-way ranging to overcome the four evident shortcomings of the traditional RRSI fingerprint-based methods. Firstly, measured RSSI is calibration-less for different Bluetooth devices and varying battery status. Secondly, virtual fingerprint of training is obtained by computing, not measured point by point in whole 3D indoor environment. Thirdly, 3D indoor positioning method is employed to substitute for the classical 2D positioning methods. Finally, two-way ranging is adopted to take the place of the conventional one-way ranging and improve the positioning precision. The correctness and availability of the proposed method are testified by the simulation and hardware experiments.

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model.

The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method.

APs’ Virtual Positions-Based Reference Point Clustering and Physical Distance-Based Weighting for Indoor Wi-Fi Positioning

This paper first proposes a new clustering algorithm for selection of reference points (RPs) based on virtual positions of access points for indoor localization in area without linear constraints, which can not only cluster automatically but also guarantee the consistency of methods between the offline phase clustering and the online phase positioning. A new weighted algorithm based on physical distance is then presented for position determination. With angle velocity measurement provided such as by gyroscope, the weighted algorithm is particularly suited for scenarios where the mobile moves along a trajectory. The number of clusters in traditional RP clustering algorithms needs to be predefined, which means an unsuitable number of clusters would lead to poor estimation accuracy. Traditional weighted $\boldsymbol {K}$ -nearest neighbor (WKNN) algorithm weights the RPs’ coordinates by the inverse of the received signal strength indication (RSSI) difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of Wi-Fi RSSI. Experimental results show that the proposed weighted algorithm considerably outperforms the $\boldsymbol {K}$ -nearest neighbor (KNN), Euclidean-WKNN, Manhattan-WKNN, EWKNN, LiFS, and GPR in terms of positioning accuracy which is defined as the cumulative distribution function of position error. The results also demonstrate that RPs in indoor area without linear constraints can be clustered automatically by the proposed clustering algorithm, and cumulative distribution function of the proposed clustering algorithm outperforms KNN, WKNN, RP location clustered, and signal distance clustered.

Localization improvement in wireless sensor networks using a new statistical channel model

In this paper, a statistical channel model is proposed based on the second moment of Received Signal Strength Indicator (RSSI) in an outdoor communication channel. The medium under study is a grass field where the RSSI data are collected in different distances and orientations using a set of in-house built transmitter-receiver sensors. The validity of the constructed sensors is confirmed since the first moment of RSSI data follows the well-known Friis model. The proposed model presents an additional relationship between the variance of RSSI data and distance. To demonstrate the application of this statistical relationship, we have investigated the localization problem of a hidden node using extended Kalman filter (EKF). Compared to the conventional EKF in which the covariance matrix of measurement noise is fixed, this matrix can be updated online using the proposed model. The experimental and simulation results of two different scenarios, which are fixed hidden node and mobile hidden node, show that the proposed model improves the accuracy of RSSI localization from 10 to 22 percent in different situations.

A New Weighted Algorithm Based on the Uneven Spatial Resolution of RSSI for Indoor Localization

The weighted K-nearest neighbor (WKNN) algorithm is one of the most frequently used algorithms for indoor positioning. However, the traditional WKNN algorithm weights the reference points’ coordinates by the inverse of the received signal strength indication (RSSI) difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of the Wi-Fi RSSI. Therefore, in order to improve the positioning accuracy of traditional location algorithms, this paper proposes a new weighted algorithm based on the physical distance of the RSSI. Experiments were conducted in an office building and the results demonstrate that the proposed method considerably outperforms the KNN, Euclidian-W-KNN, Manhattan-W-KNN, EWKNN, LiFS, and GPR in terms of positioning accuracy, which is defined as the cumulative distribution function of position error.

Incremental LLE for Localization in Sensor Networks

Received signal strength indicator (RSSI) gives a coarse initial measure of the inter-node distance at a low cost without the need for additional equipment or complexity. This necessitates the need for a mechanism to obtain accurate node locations from the noisy RSSI distance estimates. In this paper, an iterative nonlinear manifold learning technique, incremental locally linear embedding (ILLE), has been proposed for accurate node localization. The ILLE considers the one-hop neighborhood around the anchor nodes, as a reference structure. This structure grows iteratively to localize all the remaining sensor nodes in the network. Simultaneous localization mechanism further reduces the computational complexity of localization. Experimental results show that the ILLE is able to localize the nodes accurately in both normal and simultaneous scenarios. The ILLE is found to have higher accuracy in the typical scenario as compared with the simultaneous scenario. Results also indicate that the ILLE is able to localize sensor nodes with an increased accuracy of around 12.36% as compared with the centralized LLE and also outperformed other existing similar localization techniques.

Adaptive Locally Linear Embedding for Node Localization in Sensor Networks

Received signal strength indicator (RSSI) gives a rough initial measure of the inter-node distances at low cost without the need of additional equipment or complexity. This necessitates the need for a mechanism to obtain accurate node locations from the noisy RSSI distance estimates. In this paper, a non-linear manifold learning technique, adaptive locally linear embedding (ALLE), is proposed for node localization using the noisy RSSI distance estimates. ALLE, a modified version of LLE, considers the neighborhood around a node to determine the neighbors to approximate the node optimally. Experimental and simulation results show that ALLE is able to localize the nodes accurately in both clustered and centralized wireless sensor network. The centralized mechanism is found to have higher accuracy as compared with ALLE running on different cluster heads. However, this increase in accuracy is at the cost of significant energy overhead required for information gathering at the base station. Results also indicate that the ALLE is able to localize sensor nodes with an increased accuracy of around 9.38% as compared with native LLE.

Adaptive RSSI-based localization scheme for wireless sensor networks

Range of applications for Wireless Sensor Networks (WSNs) is increasing rapidly. One class of such applications is Energy-Aware Wireless Positioning Systems for situation awareness. Localization deals with determining a target node’s position in WSN by analyzing signals exchanged between nodes. Received Signal Strength Indicator (RSSI) represents the ratio between received signal power and a reference power, and is typically used to estimate distances between nodes. RSSI distance estimations are affected by many factors. This paper aims to enhance the accuracy of RSSI-based localization techniques in ZigBee Networks through studying the communication channel status between two nodes. As the network nodes are exposed to high noise levels, position estimation accuracy deteriorates. A novel adaptive localization scheme is proposed; Two-State Markov model with moving average is employed to detect unpredictable RSSI readings that may reflect badly on the estimation. The proposed scheme achieves better estimation accuracy, for example, the estimation error was reduced from 11.7 m to just 3 m using the proposed scheme.

Distance Measurement Model Based on RSSI in WSN

The relationship between RSSI (Received Signal Strength Indication) values and distance is the foundation and the key of ranging and positioning technologies in wireless sensor networks. Log-normal shadowing model (LNSM), as a more general signal propagation model, can better describe the relationship between the RSSI value and distance, but the parameter of variance in LNSM is depended on experiences without self-adaptability. In this paper, it is found that the variance of RSSI value changes along with distance regu- larly by analyzing a large number of experimental data. Based on the result of analysis, we proposed the relationship function of the variance of RSSI and distance, and established the log-normal shadowing model with dynamic variance (LNSM-DV). At the same time, the method of least squares(LS) was selected to es- timate the coefficients in that model, thus LNSM-DV might be adjusted dynamically according to the change of environment and be self-adaptable. The experimental results show that LNSM-DV can further reduce er- ror, and have strong self-adaptability to various environments compared with the LNSM.