A Survey On Task Allocation In Mobile Crowd Sensing: Current State And Challenges

Chen, Yang; Zhao, Weiwei; Xu, Changjing

doi:10.1109/iaeac54830.2022.9929971

Cited by 2 publications

(1 citation statement)

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“…Task allocation in MCS is a crucial process that involves strategically assigning sensing tasks to a group of mobile workers to efficiently collect data from diverse urban environments [5]. The primary objective of task allocation is to optimize the selection of workers based on their mobility patterns, expertise, and availability, ensuring the timely and accurate completion of tasks.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Worker Recruitment for Mobile Crowd Sensing Based on Mobility Prediction

Ngo,

Yoon

2023

IEEE Access

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Opportunistic worker (OW) selection is a challenging problem in mobile crowd sensing (MCS), where tasks are assigned to individuals to be completed seamlessly during their daily routines without any deviation from their usual routes. In this paper, we propose a novel framework named context-aware worker recruitment based on a mobility prediction model (CAMP) to address the OW selection problem in MCS. Unlike previous approaches that relied on worker mobility prediction models with limited accuracy or utility-based selection methods neglecting task distribution differences across locations, CAMP introduces a two-phase strategy for OW selection. In the first phase, we leverage a recurrent neural network-based prediction model specifically designed to forecast volunteer workers' future locations with higher precision. This enhanced mobility prediction ensures more effective task assignments in the MCS system. In the second phase, CAMP employs a weighted-utility algorithm that takes into account the varying task distribution throughout the day across different locations. The key novelty of the CAMP framework lies in its combination of an accurate multi-output RNN model for predicting worker mobility and a unique weighted-utility worker selection algorithm that considers variations in task distribution across different locations and sensing cycles. To validate the effectiveness of the CAMP framework, we extensively evaluate it using real-world GPS data, specifically the Crawdad Roma/Taxi dataset. The results demonstrate that CAMP outperforms existing approaches, delivering a higher number of completed tasks while adhering to the same budget constraints.INDEX TERMS Human mobility prediction, opportunistic worker selection, mobile crowd sensing, task allocation.

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Section: Introductionmentioning

confidence: 99%