Incentive mechanism for participatory sensing under budget constraints

Abstract

Incentive strategy is important in participatory sensing, especially when the budget is limited, to decide how much and where the samples should be collected. Current auction-based incentive strategies purchase sensing data with lowest price requirements to maximize the amount of samples. However, such methods may lead to inaccurate sensing result after data interpolation, particularly for participants that are massing in certain subregions where the low-price sensing data are usually aggregated. In this paper, we introduce weighted entropy as a quantitative metric to evaluate the distribution of samples and find that the distribution of data samples is another important factor to the accuracy of sensing result. We further propose a greedy-based incentive strategy which considers both the amount and distribution of samples in data collection. Simulations with real datasets confirmed the impact of samples distribution to data accuracy and demonstrated the efficacy of our proposed incentive strategy.