Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

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.

There has been an explosion in the number of wireless devices and deployment of wireless networks. Successful deployment of wireless networks depends on proper planning and design. One of the important aspects during the planning phase is to determine the extent at which diffraction (scatter) and reflection of propagated signals has an influence on path loss. The best way to do so, is to model the signal's path loss as it travels from the emitter to the receiver. In this work we propose a model to predict path loss for wireless networks based on the radiosity method commonly implemented in the quest for realism in representing images in the computer graphics field. We created the radiosity path loss model and used it to compute I signal strength and path loss values at a varying distances. We also validated the model using an experiment carried out using inSSIDer software. The experiment was repeated twice to ascertain the consistence of results. At 95% level of confidence and 58 degrees of freedom, a t-student test revealed that the difference between obtained values for both experiments was insignificant. Hence the study showed that all values that were captured during each experiment were stable and reliable. The results further showed that our model accurately predicted path loss. This was demonstrated through curve fitting of the model results and the experimental result, which proved to be a best fit.

Optimizing AP and Beacon Placement in WiFi and BLE hybrid localization

It has been observed that the existing path loss models such as Hata, Okumura, and COST-231-Walfisch-Ikegami do not help in accurately predicting path loss for Indian terrain conditions. Since path loss values impacts the cost of operation along with Quality of Services (QoS) significantly, a new path loss model is desired. Modelling an accurate path loss model for Indian terrain requires terrain/city planning along with the building data, extensive drive tests for radio signal characterization, wireless telecom operator data related to Base Station (BTS) location, transmission power, height, tilt, etc. All these factors make the path loss modeling difficult and challenging. In this direction, we have proposed Tata Dense Urban Path loss (T-DUP) model which is a non-deterministic statistical model applicable for Indian cities. The proposed path loss model is validated through extensive drive tests at Bangalore with the help of a major Indian Telecom Operator operating at 1800 MHz. We have observed that the T-DUP model is statistically robust and is closely matching to the drive test results. We have also conducted statistical analysis and have illustrated the impact of various city planning parameters such as building heights, building separation, road widths, etc., on path loss model. We believe that T-DUP model can be extended to other frequency bands operating in India with appropriate tuning of parameters.

Towards a better indoor positioning system: A location estimation process using artificial neural networks based on a semi-interpolated database

Introduction:An important and complicated problem related to the multilateration of Wi-Fi or Bluetooth Low Energy signals as well as Wi-Fi fingerprinting is the procedure of infrastructure adjustment which includes Wi-Fi radio map construction and Wi-Fi or Bluetooth Low Energy radio signal path loss model calibration.Purpose:Developing a method for navigation and Wi-Fi radio map construction, which would provide user’s indoor navigation, Bluetooth Low Energy path loss model calibration and Wi-Fi radio map collaborative semi-automatic construction.Results:The paper presents a collaborative semi-automatic Wi-Fi radio map construction method based on the combination of Bluetooth Low Energy multilateration, Wi-Fi fingerprinting, Wi-Fi radio map collaborative semiautomatic construction procedure and semi-automatic Bluetooth Low Energy path loss model calibration. For the semi-automatic calibration procedure of the Bluetooth Low Energy signal propagation model and for the method of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation, a calibration algorithm and an algorithm of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation were proposed, respectively. A mobile application has been developed which implements the proposed algorithms in order to test them and analyze the results.Practical relevance:The proposed method allows you to avoid time-consuming procedures of constructing a map of Wi-Fi radio signals and semi-automatic calibration of Bluetooth Low Energy signal propagation in the offline phase.

Radio channel state information (CSI) measured with many receivers is a good resource for localizing a transmitter device with machine learning with a discriminative model. However, CSI localization is nontrivial when the radio map is complicated, such as in building corridors. This article introduces a view-selective deep learning (VSDL) system for indoor localization using CSI of WiFi. The multiview training with CSI obtained from multiple groups of access points (APs) generates latent features on a supervised variational deep network. This information is then applied to an additional network for dominant view classification to minimize the regression loss of localization. As noninformative latent features from multiple views are rejected, we can achieve a localization accuracy of 1.28 m, which outperforms by 30% the best known accuracy in practical applications in a complex building environment. To the best of our knowledge, this is the first approach to apply variational inference and to construct a practical system for radio localization. Furthermore, our work investigates a methodology for supervised learning with multiview data where informative and noninformative views coexist.

The pervasive diffusion of smartphones is boosting indoor positioning solutions and location-based services. We propose a novel methodology to perform indoor positioning of mobile users by the estimation of angles of arrival from access points whose locations are known. Angles of arrival are estimated by correlating WiFi RSSI measurements with data coming from a digital compass, which is provided by most current handsets. Our system has minimal requirements in terms of infrastructure and mobile hardware. The system neither needs calibration, nor radio maps but requires the user to perform a gesture when an estimation is needed. The resulting on-demand localization has advantages in terms of privacy and power efficiency. Initial experimental results, even under severe multipath conditions, show good accuracy in terms of angle of arrival estimation and promising results on localization.

A novel indoor localization method for Wi-Fi devices such as smart phones is presented. With an indoor path loss model, the distance between a mobile device (MD) and an access point (AP) can be estimated form the received signal strength (RSS). Then, least-squared-error (LSE) triangulation yields the position of the MD. Though indoor channel characteristics are highly fluctuating with perturbations, a reference point (RP) can be used to compensate for channel instability. We present two contributions: cross monitoring AP and weighted triangulation. Cross monitoring AP removes the need of deploying RPs since it can play the dual role of a regular AP and a RP. Weighted triangulation yields improvement in the accuracy of LSE estimation of the MD position. Experimental results show weighted triangulation with cross monitoring AP provides indoor localization with an error less than 2 meters.

This paper introduces novel schemes for indoor localization, outlier detection, and radio map interpolation using wireless local area networks. The localization method consists of a novel multicomponent optimization technique that minimizes the squared $\ell _{2}$ -norm of the residuals between the radio map and the online received signal strength (RSS) measurements, the $\ell _{1}$ -norm of the user's location vector, and weighted $\ell _{2}$ -norms of layered groups of reference points (RPs). RPs are grouped using a new criterion based on the similarity between the so-called access point (AP) coverage vectors. In addition, since AP readings are prone to containing inordinate readings, called outliers, an augmented optimization problem is proposed to detect the outliers and localize the user with cleaned online measurements. Moreover, a novel scheme to record fingerprints from a smaller number of RPs and estimate the radio map at RPs without recorded fingerprints is developed using sparse recovery techniques. All localization schemes are tested on RSS fingerprints collected from a real environment. The overall scheme has comparable complexity with competing approaches, while it performs with high accuracy under a small number of APs and finer granularity of RPs.

One of the problems associated with the implementation of indoor location detection systems is the time-consuming procedure of equipment adjustment, which includes indoor map construction, radio signal map creation and calibrating signal propagation model. Thus, the equipment adjustment is a time-consuming and expensive process to be perform every time when there are changes in equipment configuration and allocation. The developed indoor localization system provides navigation of the user inside a room and allows to building radio map and putting Bluetooth Low Energy (BLE) beacons on the map of a room by the efforts of a number of users walking indoors. The architecture of the system is developed so that the different indoor localization techniques can be used and different services can be requested by the user’s mobile application. The user’s navigation inside the room is a combination of PDR based on the built-in smartphone sensors, multilateration and fingerprinting. The indoor navigation ontology is implemented to make decision which of these methods should be used. The key feature of the system is determining the location of BLE beacon. For this purpose the Random Forest algorithm is used, which uses signal levels, user rotation angles and distance to Bluetooth beacon as a training dataset. The geometric parameters of a room are estimated by the radio map and Bluetooth beacon locations.

In indoor positioning system, the user’s location is estimated based on user fingerprints database collected. The received signal from a user at a certain location is compared with all database entries and the best match is selected as the correct estimation. For a given number of access points (APs), optimizing the locations of APs can significantly improve the performance of user location estimation. In this work, the probability of correct estimation is analyzed based on APs location. Both analytical expressions and numerical results are presented and discussed. It has been shown that the probability of correct estimation is highly affected by the APs locations. Furthermore, a new technique is proposed to remarkably reduce the search-cost when comparing a received user signal to the data-base. The performance of the system is analyzed considering different APs locations, different user locations, and different noise variances. It is shown that user’s location estimation is significantly improved when compared to existing techniques.

The idea of smart and sustainable cities is incomplete without using information and communication technologies. Wireless Local Area Network (WLAN) plays a vital role in connecting different services, transferring data and providing indoor localization. WLAN fingerprinting technique has become the most common approach for indoor localization in smart cities. This technique utilizes received signal strength (RSS) and channel status from the installed WLAN Access Points (AP). Currently, WLAN planning relies on the site survey, which is laborious and uneconomical for large areas. The WLAN has a limited number of channels assigned in the frequency reuse strategy. An inefficient channel assignment strategy will lead towards increased interference, thus degrading the overall service. This paper proposes a WLAN channel assignment strategy for large and crowded indoor areas. The proposed scheme assigns the channels to installed APs for indoor localization in fingerprinting framework. The proposed technique ensures that the proposed AP channel assignment algorithm in our network-planning scheme mitigates interference.

This paper presents robust empirical path loss models to characterize indoor propagation for access point (AP) deployed at different heights. The proposed models are developed with wireless local area network infrastructure at 2.4 GHz. The models are backed by extensive received signal strength (RSS) measurements acquired in line of sight and obstructed line of sight regions. The models are developed for two conditions, viz; quasi realistic and realistic RSS measurements. The quasi realistic measurements are taken after suppressing human intervention and electrical interferences to minimum. While the realistic RSS measurements are made in presence of all the human interventions and electrical interferences. The shadow fading component for both quasi realistic and realistic conditions is statistically modeled with the dependency on AP height. The proposed technique can be applied with higher confidence level to the buildings with similar construction features where RSS measurements are made upon. The results reveal that the performance of the proposed propagation models is significantly higher than the existing International Telecommunication Union-path loss model. The results also demonstrate that the realistic path loss model is more robust than the quasi realistic model.

The signal power in wireless communication systems is influenced by various factors, including the environment. These factors include path differences, operational frequency, and environmental conditions. Consequently, designing a communication system that generates a stronger signal is highly challenging. To address this, large-scale path-loss models are employed to estimate the path loss and signal power across different frequencies, distances, and environments. In this paper, we focused on the urban micro, urban macro, and rural macro environments to estimate path loss and signal power at millimeter wave frequencies. We compared the path loss and received power among different path-loss models developed by standard organizations. Simulation results indicate that the fifth-generation channel model provides enhanced path loss and signal power in urban micro environments, while the third-generation partnership project model performs well in urban macro and rural macro environments when compared to other path-loss models.