Crowdsensing in Urban Areas for City-Scale Mass Gathering Management: Geofencing and Activity Recognition

Abstract

The widespread availability of smartphones today equipped with several physical and virtual sensors allows to directly collect various information about surrounding physical and logical context for different purposes that range from detecting user's current physical activity and also user presence in a designated area, often referred to as geofencing, to determining current social pulse of individuals and entire communities. Mobile crowdsensing seems a promising solution for enabling the design/development and deployment of a wide range of advanced applications in various fields. In particular, public safety, transportation, and energy monitoring and management in urban environments can benefit from mobile crowdsensing in terms of advanced provisioned applications as well as savings of investments in the urban sensing infrastructure. However, enabling those advanced smart urban applications requires complex signal processing, machine learning, and resource management algorithms that are often beyond the skills of many mobile app developers. This paper describes the pivotal relevance of these facilities for mobile crowdsensing applications and presents our open-source solution, called Mobile Sensing Technology (MoST), for activity detection and geofencing, comparing it with the reference implementations provided by Google as part of the Google Play Services library. Experimental results within the testbed framework of a crowd-management application scenario validate MoST design guidelines and demonstrate the general-purpose, unintrusive, and power-efficient characteristics of MoST sensing capabilities.