Dos and Don'ts in Mobile Phone Sensing Middleware: Learning from a Large-Scale Experiment

International audienceMobile phone sensing contributes to changing the way we approach science: massive amount of data is being contributed across places and time, and paves the way for advanced analyses of numerous phenomena at an unprecedented scale. Still, despite the extensive research work on enabling resource-efficient mobile phone sensing with a very-large crowd, key challenges remain. One challenge is facing the introduction of a new heterogeneity dimension in the traditional middleware research landscape. The middleware must deal with the heterogeneity of the contributing crowd in addition to the system's technical heterogeneities. In order to tackle these two heterogeneity dimensions together, we have been conducting a large-scale empirical study in cooperation with the city of Paris. Our experiment revolves around the public release of a mobile app for urban pollution monitoring that builds upon a dedicated mobile crowd-sensing middleware. In this paper, we report on the empirical analysis of the resulting mobile phone sensing efficiency from both technical and social perspectives, in face of a large and highly heterogeneous population of participants. We concentrate on the data originating from the 20 most popular phone models of our user base, which represent contributions from over 2,000 users with 23 million observations collected over 10 months. Following our analysis, we introduce a few recommendations to overcome-technical and crowd-heterogeneities in the implementation of mobile phone sensing applications and supporting middleware

Energy models for wireless communication on mobile devices

With the evolution of wireless communication networks, the access to the internet became much faster for mobile devices. This enables new distribution strategies like offloading of complex tasks to a remote server. Nevertheless energy resources on mobile devices is limited by the battery capacity. In order to decide if calculations should be offloaded to a server ot executed on a mobile device, a prediction model for energy consumption is needed. This thesis proposes energy prediction models for wireless communication on mobile battery-powered devices. The prediction models cover the network types LTE and WiFi. The energy consumptions predicted by combining several linear regressions alongside statistical prediction intervals. The regression is based on energy measurements on a mobile device of 288 client-server communications. The prediction results can compared to other data to decide on the usage of the wireless network. Evaluating the prediction model with an additional set of measurements shows a relative error of 1.21% in case of LTE and 26.73% in case of WiFi