Joint Resource Allocation and Incentive Design for Blockchain-Based Mobile Edge Computing

Mobile edge computing (MEC), as a promising technology, provides proximate and prompt computing service for mobile users on various applications. With appropriate incentives, profit-driven users can offload multi-task requests across heterogeneous edge servers. However, such incentive trade lacks a trustworthy platform. Due to the decentralized nature of MEC, trading information from players is easily tampered with by edge servers, which poses a threat to cross-server resource allocation. In this paper, we jointly consider incentives and cross-server resource allocation in blockchain-driven MEC, where the blockchain prevents malicious edge servers from tampering with player information by maintaining a continuous tamper-proof ledger database. Particularly, we propose two double auction mechanisms, namely a double auction mechanism based on breakeven (DAMB) and a more efficient breakeven-free double auction mechanism (BFDA), in which users request multi-task service with claimed bids and edge servers cooperate with each other to serve users. A delegated proof of stake (DPoS) based blockchain technology is leveraged to realize decentralized, untampered, safe and fair resource allocation consensus mechanism. The simulation results show that the proposed DAMB and BFDA can significantly improve the system efficiency of MEC.

[1]  Jiajia Liu,et al.  2-to- $M$ Coordinated Multipoint-Based Uplink Transmission in Ultra-Dense Cellular Networks , 2018, IEEE Transactions on Wireless Communications.

[2]  Wenzhong Li,et al.  Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing , 2015, IEEE/ACM Transactions on Networking.

[3]  Wei Song,et al.  Auction Mechanisms Toward Efficient Resource Sharing for Cloudlets in Mobile Cloud Computing , 2016, IEEE Transactions on Services Computing.

[4]  Hongke Zhang,et al.  Incentive mechanism for computation offloading using edge computing: A Stackelberg game approach , 2017, Comput. Networks.

[5]  Yuqing Chen,et al.  Energy-Optimal Scheduling of Mobile Cloud Computing Based on a Modified Lyapunov Optimization Method , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[6]  Ke Zhang,et al.  Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks , 2016, IEEE Access.

[7]  Jiajia Liu,et al.  Stochastic Geometric Analysis of Multiple Unmanned Aerial Vehicle-Assisted Communications Over Internet of Things , 2019, IEEE Internet of Things Journal.

[8]  Jiajia Liu,et al.  Coordinated Multipoint-Based Uplink Transmission in Internet of Things Powered by Energy Harvesting , 2018, IEEE Internet of Things Journal.

[9]  Lei He,et al.  Multiple-Jammer-Aided Secure Transmission With Receiver-Side Correlation , 2019, IEEE Transactions on Wireless Communications.

[10]  Igor Bisio,et al.  Blind Detection: Advanced Techniques for WiFi-Based Drone Surveillance , 2019, IEEE Transactions on Vehicular Technology.

[11]  Yanlin Yue,et al.  AI-Enhanced Offloading in Edge Computing: When Machine Learning Meets Industrial IoT , 2019, IEEE Network.

[12]  Dusit Niyato,et al.  Social Welfare Maximization Auction in Edge Computing Resource Allocation for Mobile Blockchain , 2017, 2018 IEEE International Conference on Communications (ICC).

[13]  Qianbin Chen,et al.  Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing , 2017, IEEE Transactions on Wireless Communications.

[14]  Shengli Xie,et al.  Computing Resource Trading for Edge-Cloud-Assisted Internet of Things , 2019, IEEE Transactions on Industrial Informatics.

[15]  Nuo Xu,et al.  Optimal Power Allocation for SCMA Downlink Systems Based on Maximum Capacity , 2019, IEEE Transactions on Communications.

[16]  Haibin Zhang,et al.  Double Auction-Based Resource Allocation for Mobile Edge Computing in Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[17]  Hong Ji,et al.  Combinational Auction-Based Service Provider Selection in Mobile Edge Computing Networks , 2017, IEEE Access.

[18]  Dusit Niyato,et al.  Optimal Auction for Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach , 2017, 2018 IEEE International Conference on Communications (ICC).

[19]  Yanlin Yue,et al.  Social-Aware Incentive Mechanisms for D2D Resource Sharing in IIoT , 2020, IEEE Transactions on Industrial Informatics.

[20]  Zhang Zhe,et al.  A review on consensus algorithm of blockchain , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[21]  Sergio Barbarossa,et al.  Small Cell Clustering for Efficient Distributed Fog Computing: A Multi-User Case , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[22]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[23]  Zhong Fan,et al.  Emerging technologies and research challenges for 5G wireless networks , 2014, IEEE Wireless Communications.

[24]  Prasanna Chaporkar,et al.  Auction Based Resource Allocation and Pricing for Heterogeneous User Demands in eMBMS , 2019, 2019 IEEE Wireless Communications and Networking Conference (WCNC).

[25]  Dusit Niyato,et al.  Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks , 2018, IEEE Transactions on Parallel and Distributed Systems.

[26]  Khaled Ben Letaief,et al.  Delay-optimal computation task scheduling for mobile-edge computing systems , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

[27]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[28]  Michael Till Beck,et al.  Mobile Edge Computing: A Taxonomy , 2014 .