Max-Min Fairness Multi-task Allocation in Mobile Crowdsensing

Yang, Shasha; Jiang, Wenchao; Duan, Jiaang; Huang, Zhengjie; Lu, Jianfeng

doi:10.1007/978-3-030-62460-6_15

Cited by 1 publication

(1 citation statement)

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“…In [35], a truthful incentive approach was introduced, which used the workers' sensing performance and reputation to determine the payment they would receive after completing the allocated tasks. In [36,37], the tasks were allocated such that the social welfare was maximized to ensure that there is fairness in the system. In [38], the semi-Markov model was used to obtain the positions of the workers, which was then used to determine workers with the least distance and bidding price.…”

Section: Incentive Mechanismmentioning

confidence: 99%

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

Ipaye

Chen

Asim

et al. 2022

Sensors

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Mobile crowd-sensing (MCS) is a well-known paradigm used for obtaining sensed data by using sensors found in smart devices. With the rise of more sensing tasks and workers in the MCS system, it is now essential to design an efficient approach for task allocation. Moreover, to ensure the completion of the tasks, it is necessary to incentivise the workers by rewarding them for participating in performing the sensing tasks. In this paper, we aim to assist workers in selecting multiple tasks while considering the time constraint of the worker and the requirements of the task. Furthermore, a pricing mechanism is adopted to determine each task budget, which is then used to determine the payment for the workers based on their willingness factor. This paper proves that the task-allocation is a non-deterministic polynomial (NP)-complete problem, which is difficult to solve by conventional optimization techniques. A worker multitask allocation-genetic algorithm (WMTA-GA) is proposed to solve this problem to maximize the workers welfare. Finally, theoretical analysis demonstrates the effectiveness of the proposed WMTA-GA. We observed that it performs better than the state-of-the-art algorithms in terms of average performance, workers welfare, and the number of assigned tasks.

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