Self-configuring indoor localization based on low-cost ultrasonic range sensors.

Can Basaran,Sang Hyuk Son,Taejoon Park,Jong-Wan Yoon

doi:10.3390/s141018728

Abstract

In smart environments, target tracking is an essential service used by numerous applications from activity recognition to personalized infotaintment. 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. To remedy this drawback, 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. The proposed protocol significantly reduces hardware and deployment costs thanks to the use of low-cost off-the-shelf sensors with no manual configuration. Experiments on two real testbeds of different sizes show that the proposed protocol achieves an error of 7.16∼17.53 cm in topology mapping, while also tracking a mobile target with an average error of 11.71∼18.43 cm and detecting displacements of 1.41∼3.16 m in approximately 30 s.

Highlights

Target tracking is a process of continuously recording the locations of a target, e.g., a human, in time.In smart environments, this is an essential service because most of the user interactions are through location-aware activities [1]
We further develop a heuristic-based target tracking system based on the genetic algorithm by employing the -designed self-configuring protocol, and show that our system is robust to measurement errors in contrast to the existing algorithms, e.g., those based on mathematical formulations, because it finds the best solution that fits the measurements
We evaluated our Genetic Algorithm (GA)-based self-configuring localization protocol on a six-node testbed deployed within a 6 m2 room

Summary

Introduction

Target tracking is a process of continuously recording the locations of a target, e.g., a human, in time.In smart environments, this is an essential service because most of the user interactions are through location-aware activities [1]. Health-care systems report the location of a user to medical professionals in the event of an emergency; activity recognition systems use the rooms that a user has occupied and the objects s/he has used at a certain point of time in order to infer the context; and infotainment systems use similar information to provide a user with personal feeds as s/he moves inside the environment. These interaction models, tightly coupled with location awareness, mean that the smart space experience is shaped by the existence and accuracy of a target tracking system. Once the user is identified, the system may start sampling the location of the user regularly in order to construct a track

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Results

Conclusion