Deep learning for optimal dynamic control of the internet of things
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In recent years, industrial Internet of Things (IIoT) has gained considerable attention in both industry and academia. In manufacturing sector, factories use the real-time big data collected from their IoT-enabled machines to derive new insights, which can be used to optimize their supply chain dynamics. To this end, researches focused on fully utilizing the opportunities provided by the IIoT, to meet the stringent requirement of industry in terms of resource efficiency, and delay. However, to efficiently manage the IoT-enabled resources while guaranteeing a return of investment for the IoT service providers, is still challenging. In this proposal, I propose a control mechanisms using auctions to optimally manage the IoT-enabled physical resources, that not only meets the resource demands from users, it guarantees that the service providers can earn a profit. The proposed auction-based mechanisms are truthful, individually rational, prevents buyers from unfairly affecting the results, and its assignments satisfy physical resource capacity constraints. I conducted extensive simulations, and see that the proposed auction mechanism can find control decisions that both improve the utilization of IoT-enabled resources, and profit of service providers.
Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering & Computer Science