Back
Texte intégral
Doctoral thesis

SLS: Smart localization service : human mobility models and machine learning enhancements for mobile phone’s localization

    17.03.2015

177 p

Thèse de doctorat: Università della Svizzera italiana, 2015

Localization
Mobile computing
Mobile-context based applications
Mobile resources usage optimization
Mobility prediction
Mobility modeling
Activity inference
Points of interest
Point of interest classification
User behaviour inference
English In recent years we are witnessing a noticeable increment in the usage of new generation smartphones, as well as the growth of mobile application development. Today, there is an app for almost everything we need. We are surrounded by a huge number of proactive applications, which automatically provide relevant information and services when and where we need them. This switch from the previous generation of passive applications to the new one of proactive applications has been enabled by the exploitation of context information. One of the most important and most widely used pieces of context information is location data. For this reason, new generation devices include a localization engine that exploits various embedded technologies (e.g., GPS, WiFi, GSM) to retrieve location information. Consequently, the key issue in localization is now the efficient use of the mobile localization engine, where efficient means lightweight on device resource consumption, responsive, accurate and safe in terms of privacy. In fact, since the device resources are limited, all the services running on it have to manage their trade-off between consumption and reliability to prevent a premature depletion of the phone’s battery. In turn, localization is one of the most demanding services in terms of resource consumption. In this dissertation I present an efficient localization solution that includes, in addition to the standard location tracking techniques, the support of other technologies already available on smartphones (e.g., embedded sensors), as well as the integration of both Human Mobility Modelling (HMM) and Machine Learning (ML) techniques. The main goal of the proposed solution is the provision of a continuous tracking service while achieving a sizeable reduction of the energy impact of the localization with respect to standard solutions, as well as the preservation of user privacy by avoiding the use of a back-end server. This results in a Smart Localization Service (SLS), which outperforms current solutions implemented on smartphones in terms of energy consumption (and, therefore, mobile device lifetime), availability of location information, and network traffic volume.
Language
  • English
Classification
Computer science and technology
License
License undefined
Identifiers
Persistent URL
https://n2t.net/ark:/12658/srd1318735
Statistics
Document views: 59 File downloads:
  • 2015INFO014.pdf: 39