A Survey of People Localization Techniques Utilizing Mobile Phones

A Survey of People Localization Techniques Utilizing Mobile Phones

Levent Bayındır (Ataturk University, Turkey)
DOI: 10.4018/978-1-5225-7598-6.ch082

Abstract

With the ongoing diffusion of mobile computing and context-aware applications, knowledge of the current location of an individual can be leveraged in a number of different domains, from personal diaries and fitness-related applications to human behavior analysis and targeted advertising. This chapter presents a review of past research works describing techniques for utilizing smartphone sensors to identify the environment where a smartphone user is located. The review focuses on studies where user location can be computed autonomously and continuously by a smartphone without the need for an active involvement of the user, and where issues such as power consumption and dependence of sensor readings from the on-body position of the phone are addressed.
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Background

In the last few decades there has been an increasing interest in positioning technologies. The deployment of a number of satellites in the Earth’s orbit enabled satellite-based positioning, whose main use case was vehicle navigation, but due to poor performance of this technology in indoor areas, indoor location methods have to rely on other means. The first indoor location techniques required carrying specialized devices and/or deploying ad-hoc hardware in the environment; then, the continuous enhancement of mobile phone sensing and computation capabilities, and the widespread deployment of infrastructure for wireless communication opened new frontiers for indoor localization; now, an increasing number of location-based services are made possible by different technologies for locating people in indoor environments.

Many pervasive computing applications are enabled or can be enhanced by knowledge of the current user context; while the exact definition of user context can vary between applications, physical location is an important piece of information in defining the context for many applications. Thus, methods for automatic localization of users can be considered as part of the more general issue of user context recognition (Hoseini-Tabatabaei, Gluhak, & Tafazolli, 2013).

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