Differential radio map-based robust indoor localization

The indoor localization systems based on wireless local area network received signal strength (RSS) have been widely applied due to the simplicity of system deployment as well as easy implementation on various mobile devices like the smartphones. However, they are often suffered by the major drawback of the extensive effort for location fingerprinting which is significantly labor-intensive and time-consuming. In response to this compelling problem, we design an improved manifold alignment approach to construct a cost-efficient radio map which consists of the sparsely collected location fingerprints and crowdsourcing RSS data with the purpose of reducing the overall fingerprints calibration effort. A new graph construction scheme which is proved to be the optimal choice to model the smoothness assumption in semi-supervised learning is proposed to explore the informativeness conveyed by location fingerprints during the process of radio map construction. In addition, the concept of execution characteristic function is considered to minimize the RSS sample capacity at each reference point to reduce fingerprints calibration effort further. Finally, the extensive experimental results demonstrate the performance improvement by the proposed system with the probability of localization errors within 3 m, 79.60%, which is at most 26.30 percentages higher than the one by the existing systems using location fingerprints solely.

With growing technology, Location Based Services (LBSs) are becoming a vital part of life. The rapid development of mobile internet has offered an opportunity for Wi-Fi indoor positioning to come under the spotlight due to its low cost. Here, indoor positioning is performed using Wi-Fi. The earlier solutions like GPS does not provide efficient positioning in indoor environment due to weak signal reception which makes Wi-Fi more reliable. However, nowadays the accuracy of the traditional indoor positioning algorithms like trilateration cannot meet the practical requirement. This paper proposes the performance analysis of indoor positioning algorithms using Wi-Fi technology based on Received Signal Strength (RSS) from the access points in the network. The positioning algorithms are based on location fingerprinting consisting of two stages: The offline database creation and the online positioning. The RSS values obtained by the mobile device at reference points are sent to the server where all the location fingerprints of all reference points are stored in a database. Coordinates of user position are calculated using positioning algorithms and the position is displayed in terms of 2Dcoordinates of the room and indoor map. Basic Fingerprint algorithm, RSS Fingerprint with minimum Euclidean Distance algorithm and RSS Fingerprint with Euclidean Distance and k-nearest neighbors' algorithm were implemented. Moreover comparison of their performance was carried out in terms of accuracy of position. From the result, it is found that an average accuracy of 0.4962 meters was achieved by RSS Fingerprint with minimum Euclidean distance algorithm.

Recently, machine learning (ML) has been widely adopted for fingerprint-based indoor localization because of its potency in delineating relationships between received signal strength (RSS) information and labels accurately. Existing ML-based indoor localization systems are less robust because they only adopt the output with the highest probability. This affects the final location estimate, hence compromising accuracy due to the severity of RSS fluctuations. Since different ML algorithms (MLAs) yield different performances, it is therefore intuitive to fuse predictions from multiple MLAs to improve the positioning performance in the presence of signal fluctuation. In this article, we propose SmartLoc, a smart wireless indoor localization framework to enhance indoor localization. In the offline phase, multiple MLAs are trained by utilizing an offline database. We further apply probability alignment to guarantee the predicted probabilities of each MLA at the same confidence level. In the online phase, given a testing RSS sample of a user at an unknown location, we extract the labels with probabilities greater than a certain threshold from each MLA to construct the space of candidate labels (SCL). The size of SCL can be adaptively determined by using our proposed dynamic size determination algorithm. Based on the SCL, we propose a probabilistic model to intelligently estimate the user's location by evaluating the label credibility simultaneously. A high label credibility indicates that the frequently occurred label is more likely to be true. Experimental results in a real changing environment verify the superiority of SmartLoc, outperforming the best among comparative methods by 10.8% in 75th percentile accuracy.

The most viable strategy to establish a dependable indoor positioning system is by employing the received signal strength (RSS) based fingerprinting technique, which encompasses both the offline and online phases. The offline phase involves constructing a radio map, which can be arduous in vast indoor environments. To tackle this, radio map interpolation is often used to interpolate RSS by utilizing the RSS recorded at a coarser level of known reference points (RPs). This paper proposes a novel RSS-based radio map interpolation to enhance the existing inverse distance weighting (IDW) interpolation technique. The method divides the deployment area into zones and optimizes the density of known RPs in each zone based on the number of access points (APs) with average RSS exceeding the threshold. It allocates higher RP density for the zones with poor AP coverage and reduces it for well-covered zones. Results demonstrates that the proposed method achieves substantial improvements over the baseline IDW scheme in average positioning error of up to 6.58% at the floor level and 3.77% overall.

With the proliferation of the Internet of Things (IoT), employing Received Signal Strength (RSS) as a metric to determine the location of a target (e.g., person or mobile device) is of great interest in terms of cost and ease of implementation. Indeed, RSS measurements can be easily obtained for most off-the-shelf devices, such as WiFi- or ZigBee compatible devices or sensors. This paper deals with the indoor localization problem in wireless sensor networks (WSNs) and proposes a new approach for radio signal propagation modelling and localization estimation, that accounts for the imperfection of RSS measurements and the reliability of RSS sources by using the Dempster-Shafer Theory (DST). In the signal propagation modelling, key information regarding the geometry of indoor environment that is divided into zones separated by walls (zoning), are considered. Based on the number of walls, the RSS irregularities are estimated using different distance intervals, which are weighted by a probability density determined experimentally. To estimate the location of a target node, the PCR6 rule is used to combine the belief masses of the positions obtained from the probability density. In order to evaluate the performance of the proposed approach, an experimental WSN has been deployed in a living apartment. The obtained results demonstrate that the proposed approach improves the localization accuracy compared to the case without zoning. Moreover, the obtained localization mean error proves the feasibility of a precise localization of humans in indoor environments in the case of Ambient Assisted Living and Social Robotics applications.

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.

Indoor localization of mobile nodes is emerging as a promising field in the past decades due to the advances in mobile devices and the increasing number of wireless services and applications. Wi-Fi fingerprints which is a vector of Received Signal Strength (RSS) values in a specific location are considered a good feature for indoor localization due to their simplicity. However, the existing methods such as decision tree (DT) and Naïve Bayes (NB) techniques have some drawbacks. DT has low efficiency because it uses the Boolean function, and NB provides a low accuracy of prediction for the sparse dataset. In this research, fingerprint referencing with two important methods, Support Vector Machine (SVM) and Long-Short Term Memory (LSTM) machine learning algorithms, are used to enable accurate indoor localization. Furthermore, data normalization is implemented to reduce positioning error by increasing the inconsistency of the RSS values. Further, several experiments have been conducted by using three mobile devices in the area of 280 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to create our dataset by taking 480 RSS records for both training and testing phases. The results show that the accuracy LSTM algorithm is 0.9 m, whereas the SVM algorithm accuracy is 1.1 m. Both experimental results show better accuracy for indoor environments can be achieved by the proposed approach than results recorded by several prior studies.

In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.

Radiofrequency positioning problem within indoor space becomes significant with respect to the specific area for which the radiolocation is required. The area of interest may be an apartment, shop or storage area of any shape and size. The specificity of the space defines various requirements on the radiolocation system and the need for software that would facilitate the planning of such a system is obvious. Using well-known empirical and deterministic models for propagation of electromagnetic waves in enclosed spaces and precise analysis of specific closed areas, localization based on the RSS (Received Signal Strength) method should give good positioning results in any enclosed space. Software solution that was developed in this work allows you to determine the optimal position of the receiver on the walls of any space that is selected as an area in which implementation of a system of RSS indoor localization is required. The optimal position of the receiver provides precise and reliable localization within the indoor enclosure. The software can be used to optimize the indoor localization of people or objects, with the requirement that special radio frequency tags or radio frequency transmitters are mounted on them. The software supports the ability to import an arbitrary layout of indoor space over which optimization will be carried out from AutoCAD or a CAD tool.

Hybrid Range Based Indoor Wireless Localization using Unconstrained Nonlinear Optimization for UWB Applications

Fingerprint based Wi-Fi localisation system often takes a lot of human efforts to measure the received signal strength (RSS) of dense grid in an indoor environment. In this paper, we propose a novel fingerprint database construction method with reduced human effort to obtain optimal length of RSS time series and grid division. We verify the chaotic characteristics of RSS time series, and use phase space reconstruction algorithm to calculate the optimal length of RSS data to be collected at each reference point. Then Gaussian process regression (GPR) for fingerprinting based indoor localisation is used to construct the database with information of limited reference points. The hyper-parameters of GPR is calculated by conjugate gradient descent algorithm. The performance of the proposed radio map construction framework is validated in real indoor environment, and with using Bayesian positioning method, the localisation error mean can be 1.5 m while the construction time of radio map is greatly reduced with ensuring accuracy.

Floor localization is at the heart of indoor positioning systems (IPSs) in multi-storey buildings with a variety of commercial, industrial, and health and safety applications. The prevalence of wireless technologies along with the integration of micro electro-mechanical sensors (e.g. barometers) in handheld devices and wearable gadgets of current vintage have prompted a surge in research and development efforts in the IPS area. Received signal strength (RSS), barometric altimetry (BA), and differential barometric altimetry (DBA) are three well-known methods of floor localization. However, the RSS-based methods lack the required accuracy, BA-based methods are prone to random errors due to local changes in the air pressure, e.g. from approaching weather systems, and DBA-based methods require installation of additional infrastructure (e.g. reference nodes and ad-hoc network for real-time information exchange). Fusion of BA and RSS is a viable solution for floor localization; nevertheless, available fusion algorithms are rather heuristic. In this dissertation, a theoretical framework is developed for fusing BA and Wi-Fi RSS measurements. The proposed framework involves a novel Monte Carlo Bayesian inference algorithm, for processing RSS measurements, and then fusion with BA using a Kalman Filter scheme. As demonstrated by our experimental results, the proposed sensor fusion algorithm achieves floor localization accuracy of 97% on average. The algorithm does not require new infrastructure, and has low computational complexity, hence, can be readily integrated into various state-of-the-art mobile devices.

With the WiFi becoming more and more popular over the last few years, the study on the technology of indoor positioning based on WiFi is given more and more attention. Being specific to the problem that not all the clustered reference points(RPs) of the traditional clustering algorithm have geometric proximity in indoor positioning, this paper puts forward the affinity propagation presentation(AP) which bases on the combination of geometry and received signal strength (RSS). Being different from the traditional clustering algorithm, this kind of algorithm can endow the RPs with geometrical and endow the RSS with corresponding weight according to the positioning conditions, it can use the features of the geometry and RSS to restrain the sort of RPs, and that can make the sort of RPs more reasonable. After that it will use k-Nearest Neighbor Algorithm(KNN) to have accurate positioning, which not only can reduce the calculation, but also improve the localization accuracy. Simulation shows that, this kind of algorithm possesses the advantage of fast location and higher localization accuracy.

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.

Currently, an indoor positioning system based on a fingerprint technique for wireless networks under IEEE 802.11 standard uses a method to collect a received signal strengths (RSS) to create a radio map in an offline phase. Then, it detects the RSS in an online phase to compare and find the position. However, while detecting and gathering the RSS, there are some variations of the RSS that affect the accuracy of position estimation. Therefore, there are several methods used to estimate the position in order to improve the accuracy, but the one focused in this paper is a decision tree based classification. The decision tree based classification method is found that it can provide better improvement in accuracy than the others, e.g., K-Nearest Neighbor (K-NN), Bayesian, and Neural networks. However, the techniques used to construct the decision tree are varied depending on the algorithm used to implement. Therefore, this paper is a comparison of decision tree based techniques using typical decision tree (DT) and Gradient boosted tree algorithms for estimating the position indoor. In the study, the RSSs collected from access points in the experimental area are used as the training and testing data. The decision tree models are created by using typical DT and Gradient boosted algorithms based on the training data obtained. There are two factors to consider in the comparative study, i.e., the number of training data and the number of reference radio signals. The testing results from the experiment showed that the decision tree based on Gradient boosted algorithm yielded more accurate results than typical DT, where the amount of 19 reference radio signals and 50 samples of training data gave the best result.