Maximizing Data Credibility Under Budget Constraint for Participatory Sensing

Participatory sensing campaigns encourage ordinary people to collect and share sensing data by using their portable smart devices like smartphones, iPad and iWatch. However, compared with static sensors that are deployed in the particular places by people that can provide high-quality data, the quality of sensing data that are collected from smart devices is uncontrollable, and it is costly to pay data usage without knowing the credibility of the collected data. In this paper, we propose to leverage the participant´s reputation to maximize total data credibility under budget constraint for participatory sensing systems. Specifically, we formally define a maximum sensing data credibility problem and present a trustable participants selection algorithm to obtain a near optimal solution. Provided the real data set, it has been shown by simulation results that our proposed algorithm selects more credible sensing data than that of the other two comparison algorithms.