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.

The performance evaluation of mobile wireless networks requires a representation of user mobility. Mobility models are the essential part of such evaluation because they assure examples of mobile nodes movement for typical scenarios. Mobility models provide synthetic traces adjusted to user's needs. We propose a novel RLMM model that produces synthetic traces of a working human and includes clear division on working days and weekend activities. We describe the model and evaluate it using simulation. The model provides better representation of changes in human behavior on different days than the models available in the literature.

In this article, we present new indexing and balancing methods used in parallel spatio-temporal data warehouse (B-PSTDW). Main motivation for designing the B-PSTDW system are unsatisfying results from exploitation of spatial data warehouse with cascaded star model indexed with aR-tree, which was used for distributed telemetric data processing (DSDW(t)). The DSDW(t) and B-PSTDW(t) services data from media counters (electricity, gas, heat and water). We present results of research on B-PSTDW system with new indexing structures: R-MVB, STCAT, and BITMAP