Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks.
The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown.
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Baltycka 5, Gliwice44-100, Poland. pjf@iitis.pl.
Paweł
P
Foremski
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Baltycka 5, Gliwice44-100, Poland. mgorawski@iitis.pl.
Michał
M
Gorawski
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Baltycka 5, Gliwice44-100, Poland. kgrochla@iitis.pl.
Krzysztof
K
Grochla
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Baltycka 5, Gliwice44-100, Poland. kpolys@iitis.pl.
Konrad
K
Polys
crowdsensing; energy efficiency; mobility measurements; mobility models; network modeling