Indoor Positioning System Based on Fuzzy Logic and WLAN Infrastructure

Abstract

This paper deals with the issue of mobile device localization in the environment of buildings, which is suitable for use in healthcare or crisis management. The developed localization system is based on wireless Local Area Network (LAN) infrastructure (commonly referred to as Wi-Fi), evaluating signal strength from different access points, using the fingerprinting method for localization. The most serious problems consist in multipath signal propagation and the different sensitivities (calibration) of Wi-Fi adapters installed in different mobile devices. To solve these issues, an algorithm based on fuzzy logic is proposed to optimize the localization performance. The localization system consists of five elements, which are mobile applications for Android OS, a fuzzy derivation model, and a web surveillance environment for displaying the localization results. All of these elements use a database and shared storage on a virtualized server running Ubuntu. The developed system is implemented in Java for Android-based mobile devices and successfully tested. The average accuracy is satisfactory for determining the position of a client device on the level of rooms.

Mobile healthcare application on Android OS using cloud computing

“…Wearable fitness monitoring technology has been widely adopted during the past ten years as a result of the rise in demand for individualized health and home care, as demonstrated by products such as FitBit and Misfit [2] . Importantly, smartwatches enable user engagement, phone calls, and seamless connections with widely used cloud services, such as Amazon Web Services, AWS, and Microsoft Azure, in addition to facilitating fitness tracking [3,4] . Notably, these cloud services have forged alliances with reputable healthcare organizations [5,6] , laying the groundwork for the fusion of fitness data from wearable devices with electronic health records (EHR).…”

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

“…The indoor positioning system helps determine information about the movement patterns of customers. It also offers advanced customer service, such as the most visited places for shoppers in the mall and the reorganization of public facilities [3].…”