A team-based multitask data acquisition scheme under time constraints in mobile crowd sensing

Mobile Crowd Sensing (MCS) typically assigns sensing tasks in the same target area to many participants considering data quality and diversity of devices. However, participant selection is based on individual research. The efficiency recruitment low. Individuals need higher transportation costs go task location alone, perceived by individuals difficult guarantee. This paper proposes a team-based multitask acquisition scheme under time constraints address these challenges. optimised number participants, traffic cost, designed four allocation algorithms MCS: T-RandomTeam, T-MostTeam, T-RandomMITeam, T-MostMITeam. team size associated with required for first or vehicle capacity perform task. We conducted extensive experiments real large-scale dataset evaluate algorithms' performances compared two baseline (T-Random T-most). has been significantly improved recruitment. cost can be multiplicatively reduced carpooling. Data at least 2% through reputation screening members' communication.