Sense-making from distributed and mobile sensing data: A middleware perspective

This paper presents a scalable and collaborative mobile crowdsensing framework for efficient collective understanding of users, contexts, and their environments. Collaborative mobile crowdsensing enables information to be gathered and shared by users who are directly involved (participatory sensing) or integrated seamlessly as needed (opportunistic sensing) through user mobile platforms. To address the scalability needs of the mobile ecosystem, we additionally employ compressive sensing techniques for approximate gathering and processing of sensor data - this requires new mechanisms for sensor data collection, tunable approximate processing, and mobile networking architecture, to create a compressive collaborative mobile crowdsensing platform called SenseDroid. The proposed framework is build using a multi-tired hierarchical architecture to sense spatial variations of a parameter of interest, perceive spatio-temporal fields, and enable energy efficient local mobile sensing with a small number of measurements. This approximate, yet tunable approach combines different sensing approaches opportunistically while trading scalability (and coverage) for data accuracy (and energy efficiency). In this paper we propose and discuss the framework and the challenges associated with compressive and collaborative mobile sensing for multi-tired hierarchical mobile network architecture for emerging mobile collaborative applications.