Section: New Results

Data offloading decision via mobile crowdsensing

Participants : Emanuel Lima [U. of Porto] , Aline Carneiro Viana, Ana Aguiar [U. of Porto] , Paulo Carvalho [Univ. Do Minho] .

According to Cisco forecasts, mobile data traffic will grow at a compound annual growth rate of 47 % from 2016 to 2021 with smartphones surpassing four-fifths of mobile data traffic. It is known that mobile network operators are struggling to keep up with such traffic demand, and part of the solution is to offload communications to WiFi networks. Mobile data offloading systems can assist mobile devices in the decision making of when and what to offload to WiFi networks. However, due to the limited coverage of a WiFi AP, the expected offloading performance of such a system is linked with the users mobility. Unveiling and understanding human mobility patterns is a crucial issue in supporting decisions and prediction activities for mobile data offloading.

Several studies on the analysis of human mobility patterns have been carried out focusing on the identification and characterization of important locations in users' life in general. We extended these works by studying human mobility from the perspective of mobile data offloading. In our study, offloading zones are identified and characterized from individual GPS trajectories when small offloading time windows are considered. The characterization is performed in terms availability, sojourn, transition time; type and spatial characteristics. We then evaluate the offloading opportunities provided to users while they are travelling in terms of availability, time window to offload and offloading delay. We also study the mobility predictability in an offloading scenario through the theoretical and practical evaluation of several mobility predictors. The results show that (i) attending to users mobility, ten seconds is the minimum offloading time window that can be considered; (ii) offloading predictive methods can have variable performance according to the period of the day; and (iii) per-user opportunistic decision models can determine offloading system design and performance.

This work was published at ACM CHANTS 2018 and its extension will be submitted to WoWMON 2020. This is an on-going work with the the PhD Emanuel Lima (one of my co-supervision), who spent 4 months as an intern in our team in 2018, and his advisors.