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Abstract:

In order to tackle the problem of climate change we crucially need to develop a sustainable society. This requires everyone to become aware of the precariousness of the situation, while at the same time a major scientific effort is needed. A core issue standing in the way of sustainability is that of environmental pollution. People-centric sensing is an increasingly popular approach for monitoring pollution in urban environments [5]. This model relies on mobile computing devices to enable ordinary citizens to assess their exposure to pollution factors in their everyday activities. A such, this sensing approach yields individualised data with a higher spatio-temporal granularity than systems based on sparse and stationary sensors [9], while the active involvement of citizens in the monitoring increases their awareness of environmental issues. However, people-centric sensing is hampered by the still limited capacities of current mobile devices, in terms of processing power and battery autonomy as well as the absence of specialised environmental sensors, limiting participation to those who have access to expensive or adapted devices. Our claim is that in an approach like people-centric sensing, where the participation of the people directly impacts the quality and quantity of the data collected, such restrictions should be alleviated. Indeed, given the urgency of the environmental challenges we face, we cannot wait for new generations of mobile devices to provide better hardware and software capacities, and become affordable for the public at large. In this work, we explore an extension to people-centric sensing that enables people to share and use resources available in range, in an opportunistic way [6]. The sensing process in this case is no longer constrained by the capacities of specific mobile devices, but instead can be distributed among the available resources (e.g. other mobile devices, sensors or indoor/outdoor appliances). Data gathering, processing and publishing can then be accomplished by resources that are dynamically discovered and selected according to their suitability, availability, and user preferences.

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Part of Onward!\ 2009, co-located with OOPSLA 2009