Efficient WiFi-Based Indoor Localization Using Particle Swarm Optimization

Abstract

Location based services are rapidly gaining popularity in various mobile applications. Such services rely particularly on the capability to accurately determine the location of the user. Several techniques are already available to provide localization for static or mobile applications, GPS being the most popular. However, due to some limitations of GPS such as low accuracy, unavailability in indoor environments and lower signal quality in urban areas with high rise buildings, complementary solutions are essential to offer satisfactory service at all places all the time. This paper demonstrates the use of a widely available WiFi networking infrastructure for accurate and low-cost indoor localization. Most existing WiFi-based localization approaches employ radio signal strength indicator (RSSI) fingerprinting technique, which requires a great deal of pre-deployment effort. Our swarm-inspired optimization algorithm applies a simpler and efficient technique based on the radio propagation model of the wireless signal. The proposed technique is evaluated in simulation and is demonstrated to achieve excellent average localization error of about 4 meters in an area of 50 x 50 square meters, under noisy RSSI measurements.