ACOMTA: An Ant Colony Optimisation based Multi-Task Assignment Algorithm for Reverse Auction based Mobile Crowdsensing

Abstract

Mobile Crowdsensing (MCS) systems take advantage of the ubiquity and sensing power of smartphones in data gathering. Reverse auction is a popular incentive mechanism framework for MCS wherein, the participants can determine their expected rewards for their contributions. In this paper, for the first time, we consider a multi-task location-dependent reverse auction based MCS setting wherein, each task requires a specific amount of contribution to be fulfilled, participants may need to move to the task locations in order to participate in them, and the goal is to assign each participant to at most one task in a way such that the cumulative contribution of the fulfilled tasks are maximised while not exceeding a limited budget. We show that this is an NP-hard optimization problem and propose Ant Colony Optimisation-based Multi-Task Assignment (ACOMTA) as an approximation algorithm for it. We uncover an issue with the basic instantiation of ACO and propose an approach called Valid Random Path Generator (VRPG) to avoid lack of or premature convergence of the algorithm. Through extensive experiments, we show that the proposed algorithm outperforms a greedy approach as well as a random-based solution.