Popularity Balanced Multi-Task Bundling for Mobile Crowd Sensing

Mobile Crowd Sensing (MCS) is a data collection technology in which workers finish tasks and get payment. In MCS, some tasks are not preferred workers due to their remote locations or cheap prices, which leads to a huge proportion of unpopular tasks. Although increasing tasks payment is an effective to increase task popularity, however, it may decrease platform utility. In this work, we introduce bundling into MCS to solve this problem. Specially, a Task Bundling Reorganization Mechanism (TBRM) is proposed. In TBRM, unpopular tasks are properly bundled with popular tasks to maximize the minimum of both the number of task completions and expected profit. The TBRM is separated into two phases: the area selection phase and the rule selection phase. First, the randomly generated solution is input into the area selection phase, which selects the portion of the bundle that needs to be reorganized; then, the results of the area selection phase is regarded as input of the rule selection phase, which selects the appropriate task to reorganize; finally, the TBRM repeats this process until convergence. Experimental results demonstrate the effectiveness of the TBRM mechanism.