Incentive Schemes for Community Sensing

Fig. 1. OpenSense air pollution sensors that could be used in community sensing. Top left: on top of a bus; bottom left: on top of a tram; top right: attached to a solar-powered weather station on a building; and bottom right: attached to a smartphone. Sensing is an important part of computational sustainability, where evidence are collected about important environmental phenomena that are not directly observable or quantifiable by humans. One example of such phenomena of interest is air pollution, where it was shown that exposure to air pollutants has a direct impact to human health, and urban outdoor air pollution was estimated to have caused up to 1.3 million deaths per year world wide [13]. As air quality sensors become smaller, more affordable and more connected, questions arise about how they can be used to minimize population exposure to important pollutants [7]. One of the first tasks is to ascertain a detailed, real-time map of the actual street-level pollution concentrations within a city. Such a map would allow us to make better decisions, from avoiding prolonged exposure to highly polluted areas, to producing health alerts to persons sensitive to high air pollution, analyzing total population exposure, and monitoring both regular and unusual emissions. As air pollution varies in both space and time, a single station is not sufficient to produce such a detailed map. Instead, we need a massive deployment of such sensors over many locations, including contributions by many private individuals. Such a community of sensors would allow us to have a far greater coverage of the spatial and temporal variations of the air pollution. Contrary to the traditional centralized sensing scenario where a center directly controls all the sensors about where and when to make measurements, in a community-sensing setting, agents submit reports to a center, and the quality of the pollution mapping is driven entirely by the utility of individual agents [1]. At any given time, the center publishes an estimation map that may be used as a public prior for individual agents, and later integrates the reports from the agents with an environmental model to produce a posterior map. The center has no control of where the agents would place their sensors, the accuracy of the sensors, or even if the agents are reporting actual measurements. Furthermore, because we are dealing with phenomena