Bargaining based design mechanism for delay sensitive tasks of mobile crowdsensing in IoT

Ahmad, Waqas; Ullah, Ata; Sheharyar; Jhanjhi, N. Z.; Ghoniem, Rania M.; Brohi, Sarfraz Nawaz

Abstract

Internet of Things (IoT) is getting growing interest to offer great opportunities in combination with Mobile Crowd Sensing for real-time applications. Existing approaches motivate mobile workers (MWs) for approaching the distant locations to receive attractive incentives for traveling. The main question addressed is that a number of tasks remain incomplete out of total al-located tasks. Moreover, the profitability and feasible budget constraints of the platform is also not considered. This paper presents Bargaining based Design Mechanism (BDM) to involve the nearest located MWs to improve the completion of tasks. The main method involves a bargaining based game model that increases the task completion ratio while considering the feasible budget constraint, platform profitability and social welfare. The proposed approach comprises of two algorithms: one for the selection of optimal MWs with low cost and less delay. Second is to organize bargaining for rewarding the platform on social welfare. Our work is validated by developing a testbed on Windows Azure cloud. Results prove that proposed BDM out-performs the counterparts in terms of decay coefficient, task completion ratio, participant's winning ratio, fraction of task incompletion and social welfare.

Citation

Ahmad, W., Ullah, A., Sheharyar, Jhanjhi, N. Z., Ghoniem, R. M., & Brohi, S. N. (2024). Bargaining based design mechanism for delay sensitive tasks of mobile crowdsensing in IoT. ICT Express, 10(1), 111-117. https://doi.org/10.1016/j.icte.2023.07.006

• Journal Article Type: Article
• Acceptance Date: Jul 11, 2023
• Online Publication Date: Jul 15, 2023
• Publication Date: Feb 28, 2024
• Deposit Date: Sep 11, 2023
• Publicly Available Date: Apr 11, 2024
• Journal: ICT Express
• Electronic ISSN: 2405-9595
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 1
• Pages: 111-117
• DOI: https://doi.org/10.1016/j.icte.2023.07.006
• Public URL: https://uwe-repository.worktribe.com/output/11018304