Self-configurable Indoor Localization Using Non-intrusive Distance Measurements

Abstract

In indoor localization, it is crucial to guarantee a high level of accuracy for various location-based services. An ultrasonic technique is one of the best candidates to meet this need because it is capable of performing precise distance measurements as well as enabling non-intrusive localization that requires no receiver to be carried. Nevertheless, its applicability is severely limited by the fact that ultrasonic waves are likely to collide with one another if a multiple access scheme is not equipped, as is usually the case for low-cost ultrasonic sensors. Also, environmental changes such as addition/removal of obstacles or dislocation of sensors themselves may further degrade the localization performance. In addition, the target tracking relies on sensors with known locations to estimate and keep track of the path taken by the target, and hence, it is crucial to have an accurate map of such sensors. However, the need for manually entering their locations after deployment and expecting them to remain fixed, significantly limits the usability of target tracking. So, precise location estimation of deployed sensors is essential, but many disturbances such as obstacles in indoors need to consider when determine the sensor location. In order to overcome aforementioned limitations of the ultrasonic distance measurement sensors, we introduce a genetic approach-based self-configurable, device-free, and low-cost ultrasonic sensor grouping technique for indoor localization that precisely quantifies the degree of collisions by using kernel distance and forms an optimal number of sensing groups to maximize the spatial reuse as well as to detect environmental changes in real time. After that, we present a self-configuring and device-free localization protocol based on genetic algorithms that autonomously identifies the geographic topology of a network of ultrasonic range sensors as well as automatically detects any change in the established network structure in less than a minute and generates a new map within seconds. And then, we suggest a cost-effective, scalable, asynchronous solution to estimate inter-sensor distances based solely on measurements of distances to a moving object is proposed which can estimates uncharted distances using trigonometry and processes these estimated distances with a distributed weighted multi-dimensional scaling algorithm for more precise localization of sensors. To verify the performance of proposed techniques, we conduct comprehensive experiments on the real testbed to demonstrate that our techniques achieve a high level of accuracy using off-the-shelf ultrasonic sensors. ⓒ 2016 DGIST