Fault Detection in WLAN Location Fingerprinting Systems Using Smartphone Inertial Sensors

Sammanfattning: Indoor positioning is a rapidly growing research area, enabling new innovative location-aware applications and user-oriented services. Location Fingerprinting (LF) is the positioning technique of coupling a physical location with observed radio signal measurements. In the terms of indoor LF using Wireless Local Area Network (WLAN) it refers to the use of network measurements from the WLAN Access Points (APs) to tag known locations. A data set is created containing reference fingerprints for the area of interest and is known as a radio map. A radio map can later be used to find a user's location in the area of interest. WLAN infrastructures are vulnerable to many kinds of faults and malicious attacks, including, an attacker jamming the signal from an AP, or an AP becoming unavailable during positioning due to power outage. These faults can be collectively characterized as an AP-failure. In LF positioning systems, AP-failure faults can significantly degrade the performance of a LF system due to the difference between the current fingerprints and radio map created with all APs being available. It is desirable to detect such faulty APs, in order to take actions towards fault-mitigation and restoration, in case of a malicious attack. In this work, we have developed a fault detection algorithm that uses inertial sensors (i.e., accelerometer, magnetometer) available in smartphones to detect AP-failure faults in LF systems. Inertial Measurement Unit (IMU) has become an integral part of all high-end smartphones. IMU can be used to infer location information on the smartphone. The main idea is to have two parallel position streams, the LF positioning and the IMU positioning, and to compare the mean positioning error between the two. Since IMU positioning is fairly accurate once provided with starting coordinates, we use it to detect abnormal behaviour in LF positioning system, such as highly erroneous estimates signifying an AP-failure fault present in the system. The performance of the proposed detection algorithm is evaluated with several real-life AP-related faults. The proposed algorithm exhibits low probability of false alarms in the detection of faulty APs. The conclusion is that using IMU based positioning is an effective and robust solution in terms of fault detection in LF systems.