Blockchain-Based On-Demand Computing Resource Trading in IoV-Assisted Smart City

• Published in: IEEE transactions on emerging topics in computing Vol. 9; no. 3; pp. 1373 - 1385
• Main Authors: Lin, Xi, Wu, Jun, Mumtaz, Shahid, Garg, Sahil, Li, Jianhua, Guizani, Mohsen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-07-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blockchain; Consortium blockchain; Cryptography; Edge computing; Game theory; Idling; Intelligent vehicles; Internet of Vehicles; Load management; Mobile computing; mobile edge computing; Onboard equipment; Optimization; Quality of service; Radio equipment; Security; Smart cities; smart city; Task analysis; Trusted third parties; Utilities; Vehicles
• ISSN: 2168-6750; 2168-6750
• DOI: 10.1109/TETC.2020.2971831

In a smart city, Mobile Edge Computing (MEC) are generally deployed in static fashion in base stations (BSs). While moving vehicles with advanced on-board equipment can be regarded as dynamic computing resource transporters ignoring geographical limitations. Thus Internet of Vehicle (IoV) could assist the smart city to achieve flexible computing resource demand response (DR) via paid sharing the idle vehicle computing resources. Motivated by this, we propose a Peer-to-Peer (P2P) computing resource trading system to balance computing resource spatio-temporal dynamic demands in IoV-assisted smart city. On one hand, to guarantee transaction security and privacy-preserving in our system, we employ a consortium blockchain approach and demonstrate the process of secure computing resource trading without involving a centralized trusted third-party. On the other hand, to encourage individual smart vehicles to participate in our system, we construct a two-stage Stackelberg game jointly optimizing the utilities of buyers and sellers. And we also derive the optimal computing pricing and trading amount strategies in this proposed game. Finally, security analysis shows the security performance of our system and numerical simulations show that our strategies can encourage the collaboration between the buyer and smart vehicles.