Scalable Indoor Localization Research Articles

Confidentiality Preserved Federated Learning for Indoor Localization Using Wi-Fi Fingerprinting

For the establishment of future ubiquitous location-aware applications, a scalable indoor localization technique is essential technology. Numerous classification techniques for indoor localization exist, but none have proven to be as quick, secure, and dependable as what is now needed. This research proposes an effective and privacy-protective federated architecture-based framework for location classification via Wi-Fi fingerprinting. The federated indoor localization classification (f-ILC) system that was suggested had distributed client–server architecture with data privacy for any and all related edge devices or clients. To try and evaluate the proposed f-ILC framework, different data from different sources on the Internet were collected and given in a format that had already been processed. Experiments were conducted with standard learning, federated learning with a single client, and federated learning with several clients to make sure that federated deep learning models worked correctly. The success of the f-ILC framework was computed using a number of factors, such as validation of accuracy and loss. The results showed that the suggested f-ILC framework performed better than traditional distributed deep learning-based classifiers in terms of accuracy and loss while keeping data secure. Due to its innovative design and superior performance over existing classifier tools, edge devices’ data privacy makes this proposed architecture the ideal solution.

Inaudible Sounds From Appliances as Anchors: A New Signal of Opportunity for Indoor Localization

The indoor localization still has challenges when compromising accuracy, cost, scalability, and so on. In recent years, signals of opportunity have been widely used for low-cost and scalable indoor localization services, especially in complex and changeable environments. In this article, a new opportunistic signal for indoor localization is proposed using the inaudible sound pattern from the appliances, which has superiority in long-term existence, stability, high availability, and privacy protection. The short-time Fourier transformed feature is exploited with a random forest model to recognize the appliance, and a promised matching algorithm is proposed to avoid any false detection. The recognized appliances can be used as standalone location indicators or supplements, such as loop closure pairs and additional landmarks, to the existed indoor localization system. The experimental results show that the appliance recognition achieves a recall rate of 79.66% under 100% precision, and a 95.43% improvement of localization accuracy is obtained to the pedestrian dead reckoning system over a 621.58-m trajectory.

IoT Based Indoor and Outdoor Localization Framework with WI-FI Fingerprinting Based on Scalable Resnet Models

A scalable indoor localization technique is a vital technology for future large-scale location-aware services covering a complex of multi-story buildings. Our research on the usage of ResNet for scalable building/floor categorization and floor-level position estimation based on Wi-Fi fingerprinting is presented in this publication. Building and floor-level coordinates are estimated using our new ResNet architecture, which utilizes a stacked autoencoder to reduce feature space and a feed-forward classifier to classify multiple labels of building/floor/location. This architecture is the foundation for our multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting. On the Jaume I University (UJI) campus in Spain, we test the accuracy of building/floor estimation and floor-level coordinates estimation for three buildings with four or five stories each. ResNet-based indoor localization using a single ResNet has been proven to be feasible, with results that are close to the state of the art. One ResNet is all that is needed in order for the proposed indoor localization system based on Wi-Fi fingerprinting to function at levels close to the current state of the art, allowing it to be implemented with less complexity and less energy consumption on mobile devices.

Privacy-preserving indoor localization based on inner product encryption in a cloud environment

How to utilize the resource-rich cloud to provide scalable indoor localization service to solve the problem of limited user equipment resources while preventing privacy leakage has become a hot topic. A novel privacy-preserving indoor localization scheme based on Inner Product Encryption in a cloud environment is proposed to address this issue. By using the computability of Inner Product Encryption on ciphertext without exposing plaintext information, an honest-but-curious cloud server estimates the users location without obtaining any private information. The least-squares estimation algorithm of the Time Difference location system is decomposed into the basic form of the inner product of two sets of vectors. One is the anchor information encrypted with Inner Product Encryption during the offline phase, the other is the users distance difference information encrypted and sent to the cloud server during the online phase. To hide the users real location, the users real distance difference information and several decoy information are encrypted and included in a request to achieve k-Anonymity. The location estimation algorithm operates on the cloud to locate the user from the ciphertext. In addition, three optimization methods applied to cloud server location estimation algorithm were designed to further improve the performance. The experimental results and theoretical analysis show that the proposed scheme consumes less time, and has a lower communication overhead, while maintaining positioning accuracy. At the same time, the scheme protects the users locational and data privacy of the indoor positioning service provider when migrating the indoor positioning services to the cloud environment.

A scalable indoor localization algorithm based on distance fitting and fingerprint mapping in Wi-Fi environments

With ever-increasing demands on location-based services in indoor environments, indoor localization technologies have attracted considerable attention in both industrial and academic communities. In this work, we propose a scalable indoor localization algorithm (SILA) consisting of two components, namely an annulus-based localization (ABL) component and a local search-based localization (LSL) component, with the objectives of enhancing localization accuracy and reducing online computational overhead. First, the ABL component is developed based on distance fitting using received signal strength indicator (RSSI) of Wi-Fi-based devices. In particular, a distance-RSSI fitting model is proposed based on multinomial function fitting, which is adopted to estimate the distance between the Wi-Fi access point (AP) and the mobile device. On this basis, an annulus construction scheme is proposed to confine the online searching space for possible locations of the mobile device. In addition, based on the observation of signal attenuation characteristics in different physical environments, we design a subarea division scheme, which not only enables the system to choose proper distance-RSSI fitting functions in different areas, but also reduces the overhead of distance fitting. Second, the LSL component is developed based on fingerprint mapping using RSSIs collected at APs. In particular, an RSSI distribution probability model is derived to better map the signal features of an online point (OP) with that of reference points (RPs). Then, an online localization algorithm is proposed, which selects a set of candidate RPs based on Bayes theorem and estimates the final location of an OP using K-nearest-neighbor (KNN) method. Finally, we implement the system prototype and compare the performance of SILA with two representative solutions in the literature. An extensive performance evaluation is conducted in real-world environments, and the results conclusively demonstrate the superiority of SILA in terms of both localization accuracy and system scalability.

A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

BackgroundOne of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural networks (DNNs) for the scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built.ResultsWe evaluate the performance of building/floor estimation and floor-level coordinates estimation of a given location using the UJIIndoorLoc dataset covering three buildings with four or five floors in the Jaume I University (UJI) campus, Spain. Experimental results demonstrate the feasibility of the proposed DNN-based indoor localization system, which can provide near state-of-the-art performance using a single DNN.ConclusionsThe proposed scalable DNN architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting can achieve near state-of-the-art performance with just a single DNN and enables the implementation with lower complexity and energy consumption at mobile devices.

Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process.

Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense signal database is labor intensive, expensive, and even impossible in some areas. Researchers are continually searching for better algorithms to create and update dense databases more efficiently. In this paper, we propose a scalable indoor positioning algorithm that works both in surveyed and unsurveyed areas. We first propose Minimum Inverse Distance (MID) algorithm to build a virtual database with uniformly distributed virtual Reference Points (RP). The area covered by the virtual RPs can be larger than the surveyed area. A Local Gaussian Process (LGP) is then applied to estimate the virtual RPs’ RSSI values based on the crowdsourced training data. Finally, we improve the Bayesian algorithm to estimate the user’s location using the virtual database. All the parameters are optimized by simulations, and the new algorithm is tested on real-case scenarios. The results show that the new algorithm improves the accuracy by 25.5% in the surveyed area, with an average positioning error below 2.2 m for 80% of the cases. Moreover, the proposed algorithm can localize the users in the neighboring unsurveyed area.