Maximizing Localization Accuracy via Self-Configurable Ultrasonic Sensor Grouping Using Genetic Approach

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 nonintrusive 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. To remedy these problems, we take a genetic approach to avoid collisions of ultrasonic waves, thereby maximizing the localization accuracy. Specifically, we propose a 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. Our comprehensive evaluation results on a real testbed demonstrate that it achieves very small localization errors of 20.6-32.6 cm, which is comparable with the size of target, i.e., human body, and detects any environmental change in 5.2-7 s followed by reconfiguring the sensing groups in 10.1-18.4 s.