Wi-Fi Fingerprinting Systems Research Articles

Automatic Update for Wi-Fi Fingerprinting Indoor Localization via Multi-Target Domain Adaptation

Wi-Fi fingerprinting system in the long term suffers from gradually deteriorative localization accuracy, leading to poor user experiences. To keep high accuracy yet at a low cost, we first study long-term variation of access points (APs) and characteristics of their Wi-Fi signals through over-one-year experiments. Motivated by the experimental findings, we then design MTLoc, a Multi-Target domain adaptation network-based Wi-Fi fingerprinting Localization system. As the core, MTDAN (Multi-Target Domain Adaptation Network) model adopts the framework of generative adversarial network to learn time-invariant, time-specific, and location-aware features from the source and target domains. To enhance the alignment among the source and targets, two-level cycle consistency constraints are proposed. Hence, MTDAN is able to transfer location knowledge from the source domain to multiple targets. In addition, domain selection and outlier detection are designed to avoid explosive growth of storage for targets and to limit the impact of random variations of Wi-Fi signals. Extensive experiments are carried out on five datasets collected over two years in various real-world indoor environments with a total area of 8, 350 m2. Experimental results demonstrate that MTLoc retains high localization accuracy with limited storage and training cost in the long term, which significantly outperforms its counterparts. We share our dataset to the community for other researchers to validate our results and conduct further research.

Crowdsensing Influences and Error Sources in Urban Outdoor Wi-Fi Fingerprinting Positioning

Wi-Fi fingerprinting positioning systems have been deployed for a long time in location-based services for indoor environments. Combining mobile crowdsensing and Wi-Fi fingerprinting systems could reduce the high cost of collecting the necessary data, enabling the deployment of the resulting system for outdoor positioning in areas with dense Wi-Fi coverage. In this paper, we present the results attained in the design and evaluation of an urban fingerprinting positioning system based on crowdsensed Wi-Fi measurements. We first assess the quality of the collected measurements, highlighting the influence of received signal strength on data collection. We then evaluate the proposed system by comparing the influence of the crowdsensed fingerprints on the overall positioning accuracy for different scenarios. This evaluation helps gain valuable insight into the design and deployment of urban Wi-Fi positioning systems while also allowing the proposed system to match GPS-like accuracy in similar conditions.

Mobile Crowdsensing Framework for a Large-Scale Wi-Fi Fingerprinting System

Although Wi-Fi fingerprinting systems enable location-based services in indoor environments, the service coverage isn't scalable due to costly site surveys. Mobile crowdsensing (MCS), where casual smartphone users conduct the site survey, has recently emerged as a promising solution. Applying MCS to a system introduces new challenges: motivating active participation, inferring location information of unlabeled fingerprints, and managing a large amount of fingerprints. The proposed MCS framework obtains fingerprints from users by exploiting social network service applications and using the pedestrian dead reckoning technique. MCS accurately infers the location information of unlabeled fingerprints using physical-layout and signal-strength measurements. The framework also selects an optimal set of fingerprints, which introduces high accuracy with a slightly increased database size. This case study shows the feasibility of the proposed MCS framework.

CSI-MIMO: An efficient Wi-Fi fingerprinting using Channel State Information with MIMO

The paper presents a novel Wi-Fi fingerprinting system CSI-MIMO that uses a fine-grained information known as Channel State Information (CSI). The proposed CSI-MIMO exploits the frequency diversity and spatial diversity using Multiple Input Multiple Output (MIMO) system. The proposed CSI-MIMO uses either magnitude or complex CSI based on the mobility in an indoor environment. The experimental performance of CSI-MIMO is compared with Fine-grained Fingerprinting system (FIFS), CSI with Single Input Single Output (SISO) and a simple CSI with MIMO. The experimental result shows a significant improvement with an accuracy of 0.98 m in static environment and 0.31 m in dynamic environment with optimal war-driving over existing systems.

가시광 통신을 이용한 실내 사용자 단말 탐지 시스템

Wi-Fi fingerprinting system is a very popular positioning method used in indoor spaces. The system depends on Wi-Fi Received Signal Strength (RSS) from Access Points (APs). However, the Wi-Fi RSS is changeable by multipath fading effect and interference due to walls, obstacles and people. Therefore, the Wi-Fi fingerprinting system produces low position accuracy. Also, Wi-Fi signals pass through walls. For this reason, the existing system cannot distinguish users` floor. To solve these problems, this paper proposes a LED fingerprinting system for accurate indoor positioning. The proposed system uses a received optical power from LEDs and LED-Identification (LED-ID) instead of the Wi-Fi RSS. In training phase, we record LED fingerprints in database at each place. In serving phase, we adopt a K-Nearest Neighbor (K-NN) algorithm for comparing existing data and new received data of users. We show that our technique performs in terms of CDF by computer simulation results. From simulation results, the proposed system shows that a positioning accuracy is improved by 8.6 % on average.

Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem

The Wi-Fi fingerprinting (WF) technique normally suffers from the Received Signal Strength (RSS) variance problem caused by environmental changes that are inherent in both the training and localization phases. Several calibration algorithms have been proposed but they only focus on the hardware variance problem. Moreover, smartphones were not evaluated and these are now widely used in WF systems. In this paper, we analyzed various aspects of the RSS variance problem when using smartphones for WF: device type, device placement, user direction, and environmental changes over time. To overcome the RSS variance problem, we also propose a smartphone-based, indoor pedestrian-tracking system. The scheme uses the location where the maximum RSS is observed, which is preserved even though RSS varies significantly. We experimentally validate that the proposed system is tolerant to the RSS variance problem.