Research on Incentive Algorithm of Participatory Sensing System Based on Location

Abstract

At present, user participation as the main body of the perception system will bring the problems that include consuming user's time, energy and participation costs, and so on. Therefore, giving reasonable feedback and encouragement to user participation itself can effectively improve user's initiative and data quality. Combining data quantity, data distribution and budget constraint together, an improved incentive mechanism of reverse auction is proposed based on the structure of participatory sensing system in this paper. Firstly, to maximize the coverage rate and the number of samples as the optimization goal, a model combining the dynamic reverse auction incentive strategy is designed based on the limited budget of the task provider. Secondly, on the basis of optimizing the results of sample screening, the improved algorithm KDA incentive mechanism based on position information is proposed. The algorithm combines the greedy algorithm to gradually decompose the idea of subproblem optimization, in order to ensure that the optimization results are closer to the final goal. Finally, the algorithm is verified, the experimental results show that the proposed algorithm can improve the sample number and coverage under limited budget constraints, and improve the quality of the best sample set.