Multi-worker multi-task selection framework in mobile crowd sourcing

Abououf, Menatalla; Mizouni, Rabeb; Singh, Shakti; Otrok, Hadi; Ouali, Anis

doi:10.1016/j.jnca.2019.01.008

Cited by 57 publications

(28 citation statements)

References 23 publications

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“…In the latter, the workers are assessed individually and are recruited if they meet the tasks' requirements and constraints. GRS has shown superiority when compared with IRS in both single-task and multiple-tasks assignments [11], [21].…”

Section: ) Multitask Allocationmentioning

confidence: 99%

“…Recent research is directed towards multi-worker multitask allocation due to its efficiency in terms of workers utilization [18], especially in subjective tasks. In [11], Group-based Multi-task Workers Selection (GMWS), which is a clusterbased multitasking approach, is proposed. In GMWS, a group of workers is allocated using genetic algorithm to perform all tasks in the cluster, such that the collective QoS of each task is maximized.…”

Section: ) Multitask Allocationmentioning

confidence: 99%

“…The QoS during selection commonly describes the confidence in the selected workers to truthfully perform the task. The existing allocation approaches, such as [2], [11], and [12] optimize the QoS of the tasks without assessing the contribution that each individual worker adds to the QoS. As a result, the task requesters tend to overpay for the selected workers using these selection models, thus compromising the payoff of the tasks.…”

Section: Introductionmentioning

confidence: 99%

“…In an untruthful environment, workers impersonate multiple identities using multiple devices, whereas in a truthful environment, each device is owned by a unique worker. Two benchmarks are used to assess the performance of MPWS: Group-based Multi-task Workers Selection (GMWS) [11] and Gale-Shapley Matching game Selection (GSMS) [12].…”

Section: Introductionmentioning

confidence: 99%

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A Misbehaving-Proof Game Theoretical Selection Approach for Mobile Crowd Sourcing

et al. 2020

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With the tremendous advances in ubiquitous computing, mobile crowd sourcing (MCS) has become an appealing part of the Internet of Things (IoT). In MCS systems, workers collect data with a certain quality, and get incentivized in return. However, MCS systems are vulnerable to misbehaving acts such as workers submitting multiple false or fake reports using multiple devices to affect the majority vote of the task. In addition, workers may try to maximize their profit by submitting multiple truthful data, a behavior that may prevent other potential workers to participate. The selection of such workers for a task has a negative impact on the decision making or the payoff of the task. Most of the current approaches aim to maximize the completion of tasks based on the reputation or the credibility of workers, but without consideration of tasks' payoff and the threat of misbehavior. In literature, a misbehaving act, where workers impersonate multiple identities using multiple devices to maliciously change the majority votes or selfishly increase their payment, has not been addressed during MCS recruitment. In this paper, a two-layer selection approach is proposed based on game-theory in which the payoff of the tasks is maximized based on the individual contributions of the workers. In addition, the proposed model detects and eliminates the misbehaving act where workers submit multiple reports using multiple devices, during the recruitment phase. Simulations using real-life datasets show that the proposed approach succeeds in detecting and eliminating misbehaving devices, and outperforms the benchmarks in terms of the payoff of the tasks.

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Section: ) Multitask Allocationmentioning

confidence: 99%

Section: ) Multitask Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Misbehaving-Proof Game Theoretical Selection Approach for Mobile Crowd Sourcing

et al. 2020

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“…Since the user's information needs are stored in the database of different underlying search engines, the choice of the search engine substantially improves the user's query efficiency. Abououf et al [29] address the problem of multi-worker multi-task selection allocation for mobile crowdsourcing, and use genetic algorithms to select the right workers for each task group, looking to maximize the QoS of the task while minimizing the distance traveled.…”

Section: Application Of a Genetic Algorithm In Combinatorial Optimizamentioning

confidence: 99%

Data Source Selection Based on an Improved Greedy Genetic Algorithm

Yang

Xing

2019

Symmetry

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The development of information technology has led to a sharp increase in data volume. The tremendous amount of data has become a strategic capital that allows businesses to derive superior market intelligence or improve existing operations. People expect to consolidate and utilize data as much as possible. However, too much data will bring huge integration cost, such as the cost of purchasing and cleaning. Therefore, under the context of limited resources, obtaining more data integration value is our expectation. In addition, the uneven quality of data sources make the multi-source selection task more difficult, and low-quality data sources can seriously affect integration results without the desired quality gain. In this paper, we have studied how to balance data gain and cost in the source selection, specifically, maximizing the gain of data on the premise of a given budget. We proposed an improved greedy genetic algorithm (IGGA) to solve the problem of source selection, and carried out a wide range of experimental evaluations on the real and synthetic dataset. The empirical results show considerable performance in favor of the proposed algorithm in terms of solution quality.

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