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IEEE Final Year Project Topics for CSE

Base Paper Title

Computing Resource Trading for Edge-Cloud-assisted Internet of Things

Our Title

IEEE Project Abstract

Optimal computing resource allocation for edge cloud-assisted internet of things (IoT) in block chain network is attracting increasing attention. Auction is a classical algorithm which guarantees that the computing resources are allocated to the buyers of the computing resource. However, the traditionalauction algorithm only guarantees the revenue gains for the sellers of the computing resource. How to guarantee the seller and the buyer of the computing resource are both willing to trade and moreover bid truthfully is still open problem in computing resource trading for edge-cloud-assisted IoT. In this paper, we introduce a broker with the sparse information to manage and adjust the trading market. We then propose an iterative doubles idedauction scheme for computing resource trading, where the broker solves an allocation problem to determine how much computing resource is traded and designs a specific price rule to induce the buyers and sellers of the computing resource to submit bids in a truthful way. Thus, the hidden information can be extracted gradually to obtain optimal computing resource allocation and trading prices. Hence, the proposed algorithm can achieve the maximum social welfare meanwhile protecting the privacies of the buyers and the sellers. Our theoretical analysis and simulations demonstrate that the proposed algorithm is efficient, i.e. achieving the maximum social welfare. In addition,the proposed algorithm can provide effective trading strategies for the buyers and sellers of the computing resource, leading to the proposed algorithm satisfying incentive compatibility,individual rationality, and budget balance.

Existing System

Drawback of Existing System

Proposed System

Advantage of Proposed System

Enhancement from Base Paper

Architecture

Technology Used : Hardware & Software

Existing Algorithm

Proposed Algorithm

Advantages of Proposed Algorithm

Project Modules

Literature Survey

Conclusion

Future Work

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