GRS: A Group-Based Recruitment System for Mobile Crowd Sensing

Mobile Crowd Sensing (MCS) is one of the most recent sensing paradigms that gained its popularity thanks to the advances in mobile computing. A high number of mobile users carrying powerful smart devices and willing to provide the public with updates about various community dynamics and areas of interest (AoI) have contributed immensely to boost the significance of this sensing paradigm. However, there are some challenges that hinder its ongoing advancements, one of them being the participants' recruitment. Ideally, the process of recruiting participants for a given sensing task should determine the optimal number of participants to answer the request and satisfying the quality needed with a certain guarantee on the confidence level in the sensing outcome. To address this challenge, we propose a novel group-based recruitment model based on genetic algorithm that selects the most appropriate group of participants using three types of parameters: AoI-related, device-related, and user-related. Furthermore, a new evaluation metric composed of the quality of information and the confidence level is suggested. The evolution of the provided quality by the available participants is analyzed and the least amount of resources to achieve the best possible quality is determined. Simulations are conducted using real-life dataset. The suggested system's performance is compared to an individual-based quality-assessment system that employs a greedy algorithm to recruit participants, which is the mostly used algorithm in the related work.