Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm

Area of Science:

• Computer Science
• Data Science
• Mobile Computing

Background:

• Mobile crowd-sensing (MCS) systems leverage smart device sensors for data collection.
• Increasing sensing tasks and participants necessitate efficient task allocation strategies.
• Worker incentives are crucial for ensuring task completion in MCS.

Purpose of the Study:

• To develop an efficient task allocation approach for mobile crowd-sensing (MCS).
• To assist workers in selecting multiple tasks considering time constraints and task requirements.
• To maximize worker welfare through a novel pricing and reward mechanism.

Main Methods:

• The study addresses task allocation as a non-deterministic polynomial (NP)-complete problem.
• A Worker Multi-task Allocation-Genetic Algorithm (WMTA-GA) is proposed.
• A pricing mechanism determines task budgets and worker payments based on willingness.

Main Results:

• The WMTA-GA effectively solves the NP-complete task allocation problem.
• Theoretical analysis confirms the algorithm's effectiveness.
• The proposed algorithm demonstrates superior performance compared to state-of-the-art methods in average performance, worker welfare, and task assignment.

Conclusions:

• The WMTA-GA offers an effective solution for mobile crowd-sensing task allocation.
• The approach successfully balances worker time constraints with task requirements.
• This method enhances overall system efficiency and worker satisfaction in MCS.