Indoor wireless localisation is a widely sought feature for use in logistics, health, and social networking applications. Low-powered localisation will become important for the next generation of pervasive media applications that operate on mobile platforms. We present an inexpensive and robust context-aware tracking system that can track the position of users in an indoor environment, using a wireless smart meter network. Our context-aware tracking system combines wireless trilateration with a dynamic position tracking model and a probability density map to estimate indoor positions. The localisation network consisted of power meter nodes placed at known positions in a building. The power meter nodes are tracked by mobile nodes which are carried by users to localise their position. We conducted an extensive trial of the context-aware tracking system and performed a comparison analysis with existing localisation techniques. The context-aware tracking system was able to localise a person's indoor position with an average error of 1.21？m. 1. Introduction The next generation of pervasive media applications, mobile social networking, and location-based services are increasingly reliant on accurate position localisation. Localisation for indoor environments has many applications for pervasive media. Low-powered or green efficient and inexpensive localisation will become important for the next generation of pervasive media applications that operate on battery-constrained mobile platforms. Current localisation techniques depend on using sensing infrastructure already present in the environment such as visual markers, wireless LAN hotspots, cellular networks, or Global Position Systems’ (GPS) satellite coverage. The popular use of GPS has led to a variety of mobile location-based services applications such as social networking, street map guide, or asset tracking. Recently, there has been great interest in localisation for indoor navigation applications. Indoor environments cause multipath interference to wireless communications because of the presence of physical obstacles such as metal beams or walls. Hence, this causes outdoor Radio-Frequency- (RF-) based localisation technologies such as GPS to function inaccurately indoors because of signal degradation. Other RF localisation methods such as Received Signal Strength or Time of Arrival also experience inaccuracies and reliability issues when operating indoors. Wireless infrastructure that is currently used for both indoor and outdoor localisation, tends to be computationally intensive with high power
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