Variable speed multi-task allocation for mobile crowdsensing based on a multi-objective shuffled frog leaping algorithm

Shen, Xipeng; Chen, Qingzhou; Pan, Hongli; Song, Liying; Guo, Yanqin

doi:10.1016/j.asoc.2022.109330

Cited by 8 publications

(1 citation statement)

References 44 publications

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“…The storage of data is overly reliant on third-party platforms, resulting in single-point failures. Furthermore, trust issues have a major influence on both user willingness to interact and data reliability [23]. To overcome these two challenges, this work presents a credible and distributed incentive system built on Hyper Ledger Fabric (HF-CDIM).…”

Section: Detailed Literaturementioning

confidence: 99%

Reputation and Quality Aware Incentive Mechanism for Mobile Crowd Sensing Using Smart Devices

Shahzadi,

Ashraf,

Ihsan

et al. 2023

Preprint

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The Internet of Things (also known as IoT), a revolution, has enhanced the relevance of mobile employees' job efficiency in mobile crowd sensing (MCS). Incentives based on reputation for mobile workers (MW) play an important role in increasing service utilization, motivating mobile employees, and developing confidence in the service. The cell phone is the most exploited entity in mobile crowd-sensing MCS and cloud computing. In the underlying investigation, we observed that they had not evaluated the complexity level of a task, resulting in a strong reputation for executing multiple basic tasks. A person who completes a tough task may have a lower reputation score. Complexity levels of tasks (CLT) were developed for reputation evaluation on a crowd-sensing network, which would be utilized to evaluate the MW reputation. We offered simple and complex levels of challenges to assess reputation scores. Tasks receive incentives based on their reputation. By measuring reputation on (CLT) complexity level of task, this research will help the system to Maintain the reputation of the mobile worker so that entities of the system get the maximum benefit out of it, by hiring well-reputed mobile workers and MW receive an incentive on it. Furthermore, we conduct a comparative analysis between our scheme and various machine learning algorithms to identify the algorithm that best performs in evaluating task complexity. Considering both the simple and complex reputation scores, the comparison suggests that after analyzing the data, it is clear that the CLT (proposed) scheme and the Linear regression scheme not only outperformed the Neural Network scheme in terms of Complex Reputation Scores but also in terms of Simple Reputation Scores. We also apply statistical tests, such as p-tests and t-tests, to determine the significance of the results obtained from different algorithms. Considering MW 308, MW 345, and MW 1045's efforts and abilities, the MCS system may offer incentives and awards to further motivate and consider their continuing contributions. This reputation-based incentivization technique not only encourages strong competition among mobile workers, but also makes sure the delivery of high-quality services inside the MCS system.

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Section: Detailed Literaturementioning

confidence: 99%