A new harmony search based allocation algorithm for location dependent tasks in crowdsensing

Crowdsensing has emerged as a compelling paradigm for collecting sensing data over a vast area. This paper studies the critical task allocation problem in crowdsensing, i.e., maximizing the net reward of the platform under the time budget constraints of smartphone users and different quality requirements of tasks. This problem is particularly challenging because of its NP-hardness. Traditional optimization methods cannot solve this problem efficiently in limited time. We propose a Harmony Search (HS) based meta-heuristic allocation algorithm to solve the problem quickly. Our experiments show that our allocation algorithm is better than other existing methods about the reward of the platform.