A Novel Approach for Selecting the Participants to Collect Data in Participatory Sensing

This paper works on the participatory sensing in the area of sensor network. One of the most important challenges in participatory sensing is selecting the participants to collect their sensed data to monitor the environment. The goal here is to obtain the data of high quality with low payment from a predefined number of participants. To achieve the goal, this paper proposes a novel approach using the techniques of evolutionary algorithm. It models the goal as a multi-objective Knapsack problem, which is resolved by the hybrid scheme of Univariate Model Distribution Algorithm (UDMA) and the enhanced Third Evolution step of Generalized Differential Evolution (EGDE3). The experiment results show the effectiveness of the proposed approach in terms of the ratio of the payment and data quality, and takeover time compared with the Genetic Algorithm (GA) and Reverse Auction based Dynamic Price (RADP) scheme.