Privacy Preserving Task Allocation with Multi-objectives in Edge Computing Enhanced Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) uses participants' computing resources to collect and analyze data and it has been applied in several areas to bring the convenience to people's lives. In MCS, the minimization of travel distance with location privacy is a common objective but should not be the only one practically. Different from the single objective of travel distance minimization, in this paper we formulate a multi-objective optimization model based on bit flipping mechanism, i.e., travel distance minimization and sensing quality score maximization, which is more suitable for a practical scenario. In order to solve the large-scale optimization problem, a Multi-Objective Simulated Annealing approach (MOSA) is utilized to derive a Pareto solution for decision makers. Extensive simulation results illustrate the feasibility and effectiveness of the proposed scheme.