History-based Incentive for Crowd Sensing

Abstract

An increasing popularity of smartphones affects interaction behaviors between people. The ubiquity of smartphones with embedded sensors can enable attractive sensing applications for collecting and reporting data in participatory sensing environment. However, time and monetary cost hinder people from providing sensing data using their smartphones. Smartphone users will probably not participate in sensing tasks without incentive in the real world. Therefore, we propose SenseUtil which provides participation-aware incentive for crowd sensing environment. The main objective of SenseUtil is to keep moderate payment. SenseUtil applies the concept of microeconomics, where demand and supply determine the value of sensed data. Several factors including sensing frequency, nearby sensing points and users' preference, affect the incentive which dynamically changes over time. We implemented a simulation to study the impact of participation-aware incentive mechanisms. The results demonstrate that historical data of participation activities help decrease payments moderately.