Self-Configuring Indoor Localization Based on Low-Cost Ultrasonic Range Sensors

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.

Range-free 3D node localization in anisotropic wireless sensor networks

“…Soft computing algorithms have plenteous source of ideas for optimization, due to which these algorithms are popular because of their accuracy, and their meek computational burden [18,19,20,21,22,23,24,25,26,27,28,29]. An improve version of PSO, i.e., HPSO and a recent optimization algorithm, i.e., BBO are applied in [9,10,11] to achieve better and faster solution for range-based node localization.…”

A Novel Passive Tracking Scheme Exploiting Geometric and Intercept Theorems

“…For active positioning schemes, the target is supposed to carry wireless-assisted equipment, such as RF tags, smartphones, wearable devices, or laptops, to interact with the anchor nodes in the WSNs. Then, the target can be located by measuring the RSS information between the assisted wireless equipment and the anchor nodes [ 3 , 4 , 5 , 6 ].…”

An Ultrasonic Sensor for Human Presence Detection to Assist Rescue Work in Large Buildings

“…An important quality of ultrasound is that it is not obstructed by smoke, retaining its localisation ability for a longer time during a fire. It has been shown that it is possible to create a selfconfiguring network of ultrasonic sensors that can track the location of a target with an average error of less than 18 cm (Basaran et al, 2014). Since the sensors work on the basis of echolocation, the people inside the building do not need to do anything in order to be tracked.…”