区块链技术具有透明性、防篡改、可追溯等特性，可以在非信任的环境下不依赖可信第三方实现点对点交易，因此区块链技术在金融、政务和无线通信等领域得到了广泛的应用。共识机制是区块链技术的核心，是保证分布式系统一致性的关键。然而，典型的公有链共识机制，如工作量证明机制，节点通过大量的哈希计算寻求满足条件的随机数，因此需要消耗大量的计算资源，严重阻碍了区块链在移动领域的发展。通过将计算资源下沉至用户端，边缘计算技术具有计算资源丰富、服务时延低等优势，为移动区块链发展提供可行的解决方案。本文系统研究了移动区块链系统的计算任务卸载方法，主要工作和创新点如下：

（1）针对非可信边缘服务器可能存在的虚假任务执行等问题，现有的解决方法主要是将节点的计算任务序列映射成随机序列，从并行计算变成串行计算，严重影响计算效率。本文提出提出了一种基于收益分享模型的移动区块链系统计算任务卸载与定价方法，区块链系统将一部分区块奖励分给服务器，以抑制其可能存在的虚假执行任务等恶意行为。利用两阶段Stackelberg博弈模型研究边缘服务器与区块链节点之间的收益关系，并求出纳什均衡解。仿真结果表明，本文提出的收益分享模型在合谋节点区块时延远大于正常节点区块时延时，能够实现服务器收益高于合谋收益，一定程度上可以抑制边缘服务器的恶意行为。

（2）从单服务器模型扩展到多服务器模型，针对多边缘服务器的移动区块链系统可信计算卸载问题，传统的计算卸载模型中只关注了最优计算卸载策略的求解，而忽略了边缘服务器的服务质量问题。为了保障区块链节点计算卸载过程的服务质量，提高节点满意度，研究了基于多边缘服务器的可信计算任务卸载方法。以最大化服务器和区块链节点各自的收益为目标，将优化问题建模为多领导者-多跟随者（Multi-leader Multi-follower, MLMF）的两阶段Stackelberg博弈模型，对纳什均衡点的存在性和唯一性进行证明，并利用拉格朗日系数法求解带约束的凸优化问题，得到其纳什均衡解。仿真结果表明，本文所提出的基于信誉机制的多边缘服务器计算卸载模型能够对服务质量差、信誉低的服务器进行监督，通过更新信誉值对服务器收益进行加权，一定程度上抑制边缘服务器的恶意行为，提高区块链节点满意度。

（3）针对多无人机协同感知任务场景，建立了基于区块链的多无人机任务协同与计算卸载模型，无人机节点通过区块链保证可信协同，提出了面向时延最小化的协同计算卸载方法，以减少无人机节点计算卸载与区块共识过程的总时延开销。以最小化节点的总时延为目标，利用拉格朗日乘数法求解带约束的时延函数优化问题，得到最优计算卸载策略。在此基础上，为了进一步减小共识时延，提出了基于信誉的共识节点选择方法，并改进股份授权证明机制（delegated proof of stake, DPoS）。仿真结果表明，与已有贪婪方法相比，提出的时延最小化计算任务卸载方法可使得无人机节点的总时延更小，计算资源利用率更高。基于信誉机制的DPoS机制也比传统DPoS机制共识时延更短。

       Blockchain technology is transparent, tamper-proof and traceable, and can realize peer-to-peer transactions in a non-trusted environment without relying on trusted third parties, so blockchain technology has been widely used in finance, government and wireless communication. The consensus mechanism is the core of blockchain technology and the key to ensure the consistency of distributed systems. However, typical public chain consensus mechanisms, such as the proof-of-work mechanism, where nodes seek random numbers that satisfy the conditions through a large number of hash calculations, thus consuming a large amount of computing resources, seriously hinder the development of blockchain in the mobile domain. By sinking computing resources to the user side, edge computing technology has the advantages of abundant computing resources and low service latency, providing a feasible solution for mobile blockchain development. In this thesis, we systematically study the offloading method of computing tasks for mobile blockchain system, and the main work and innovation points are as follows.

(1) To address the possible problems such as false task execution by non-trusted edge servers, existing solutions mainly map the user's computation task sequence into a random sequence, which turns from parallel to serial computation and seriously affects the computation efficiency. In this thesis, we propose a method for offloading and pricing computational tasks in mobile blockchain systems based on a revenue sharing model, where the blockchain system distributes a portion of the block rewards to the server to suppress its possible malicious behaviors such as false task execution. The two-stage Stackelberg game model is used to study the revenue relationship between edge servers and blockchain nodes and to find the Nash equilibrium solution. Simulation results show that the gain-sharing model proposed in this thesis can achieve higher server gain than collusion gain when the block delay of colluding nodes is much larger than the block delay of normal nodes, which can suppress the malicious behaviors of edge servers to a certain extent.

(2) Extending from single-server model to multi-server model for the trusted computation offloading problem of mobile blockchain systems with multiple edge servers, the traditional computation offloading model only focuses on the solution of the optimal computation offloading strategy, while ignoring the service quality of edge servers. In order to guarantee the service quality of blockchain users' computation offloading process and improve user satisfaction, a trusted computation task offloading method based on multiple edge servers is studied. With the objective of maximizing the respective gains of servers and blockchain users, the optimization problem is modeled as a two-stage Stackelberg game model with Multi-leader Multi-follower (MLMF), the existence and uniqueness of Nash equilibrium points are proved, and the Lagrange coefficient method is used to solve the convex optimization with constrained The Nash equilibrium solution is obtained by using the Lagrangian coefficient method to solve the convex optimization problem with constraints. The simulation results show that the proposed multi-edge server computational offloading model based on reputation mechanism can supervise the servers with poor service quality and low reputation, and weight the server revenue by updating the reputation value, so as to suppress the malicious behavior of edge servers to a certain extent and improve the blockchain user satisfaction.

(3) For the multi-UAV cooperative sensing task scenario, a blockchain-based multi-UAV task cooperation and computation offloading model is established, where UAV nodes ensure trusted cooperation through blockchain, and a delay minimization-oriented cooperative computation offloading method is proposed to reduce the total delay overhead of the UAV nodes' computation offloading and block consensus process. With the objective of minimizing the total latency of nodes, the Lagrange multiplier method is used to solve the optimization problem of the latency function with constraints to obtain the optimal computational offloading strategy. Based on this, we propose a reputation-based consensus node selection method and improve the delegated proof of stake (DPoS) mechanism to further reduce the consensus latency. Simulation results show that the proposed delay minimization computational task offloading method can result in smaller total delay and higher computational resource utilization of UAV nodes compared with existing greedy methods. The DPoS mechanism based on the reputation mechanism also has a shorter consensus latency than the traditional DPoS mechanism.

[1]Jiang C, Ru C. Application of blockchain technology in supply chain finance[C]. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), IEEE, 2020: 1342-1345.

[2]Li Z, Wang W, Guo J, et al. Blockchain assisted dynamic spectrum sharing in the CBRS band[C]. 2021 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2021: 864-869.

[3]Yu Y, Li Q, Zhang Q, et al. Blockchain-based multi-role healthcare data sharing system[C]. 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM). IEEE, 2020: 1-6.

[4]Nakamoto S. Bitcoin: A peer-to-peer electronic cash system[J]. Decentralized Business Review. 2008: 21260.

[5]Pass R, Shi E. Fruitchains: A fair blockchain [C]. Proceedings of the ACM symposium on principles of distributed computing. 2017: 315-324.

[6]赵铖泽, Meng LI, Enchang SUN, 等.Computation offloading and resource allocation for UAV-assisted IoT based on blockchain and mobile edge computing[J]. High Technology Letters, 2022, 28(01): 80-90.

[7]张延华, 赵铖泽, 李萌, 等. MEC和区块链赋能无人机辅助的物联网资源优化[J].北京工业大学学报, 2022, 48(09): 935-943.

[8]Babaioff M, Dobzinski S, Oren S, et al. On bitcoin and red balloons[C]. Proceedings of the 13th ACM conference on electronic commerce. 2012: 56-73.

[9]门瑞,樊书嘉,阿喜达,等.物联网中结合计算卸载和区块链的综述[J].计算机应用, 2023: 1-10.

[10]孙俨,熊翱,蒋承伶,等.基于区块链的计算与无线通信资源联合管理双向拍卖模型[J].通信学报, 2022, 43(11): 14-25.

[11]梁宇翔. 基于移动边缘计算的区块链网络计算任务卸载研究[D].青岛大学, 2022.

[12]金志锋. 基于区块链的移动边缘计算系统中的计算资源分配方法研究[D].合肥工业大学, 2022.

[13]Bhat E , Bashir I . Edge Computing and Its Convergence With Blockchain in 5G and Beyond: Security, Challenges, and Opportunities[J]. IEEE Access, 2020, 8: 205340-205373.

[14]赵铖泽, Meng LI, Enchang SUN,等.Computation offloading and resource allocation for UAV-assisted IoT based on blockchain and mobile edge computing[J]. High Technology Letters, 2022, 28(01): 80-90.

[15]韩晓非,宋青芸,韩瑞寅,等.移动边缘计算卸载技术综述[J].电讯技术, 2022, 62(09): 1368-1376.

[16]姜伟. 云计算环境下可信服务动态保障模型及方法研究[D]. 哈尔滨工程大学, 2021.

[17]陈韩. 移动边缘计算系统任务卸载优化研究[D]. 南京邮电大学, 2022.

[18]刘耀,何岳园,周红静等. 移动边缘计算中基于资源联合分配的部分计算卸载方法[J]. 物联网学报, 2023, 7(01): 140-148.

[19]黄晓舸,邓雪松,陈前斌等. 边缘计算网络中区块链赋能的异步联邦学习算法[J]. 电子与信息学报, 2023: 1-9.

[20]Xiong Z, Zhang Y, Niyato D, et al. When mobile blockchain meets edge computing[J]. IEEE Communications Magazine, 2018, 56(8): 33-39.

[21]Xu F, Yang F, Zhao C, et al. Edge computing and caching based blockchain IoT network. [C]. 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN). IEEE, 2018: 238-239.

[22]Chang Z, Guo W, Guo X, et al. Incentive mechanism for edge-computing-based blockchain. [J].IEEE Transactions on Industrial Informatics, 2020, 16(11): 7105-7114.

[23]Ding X, Guo J, Li D, et al. An incentive mechanism for building a secure blockchain-based internet of things[J]. IEEE Transactions on Network Science and Engineering (TNSE), 2020, 8(1): 477-487.

[24]Jiao Y, Wang P, Niyato D, et al. Auction mechanisms in cloud/fog computing resource allocation for public blockchain networks[J]. IEEE Transactions on Parallel and Distributed Systems (TPDS), 2019, 30(9): 1975-1989.

[25]夏承鹏. 面向边缘计算的移动区块链任务迁移技术研究[D]. 广东工业大学, 2020.

[26]陈相旭. 基于边缘计算的区块链计算资源优化管理策略研究与实现[D]. 浙江工业大学, 2020.

[27]Luong N C, Xiong Z, Wang P, et al. Optimal auction for edge computing resource management in mobile blockchain networks: A deep learning approach[C]. 2018 IEEE international conference on communications (ICC). IEEE, 2018: 1-6.

[28]Jiao Y, Wang P, Niyato D, et al. Social welfare maximization auction in edge computing resource allocation for mobile blockchain[C]. 2018 IEEE international conference on communications (ICC). IEEE, 2018: 1-6.

[29]Alwarafy A, Al-Thelaya K A, Abdallah M, et al. A survey on security and privacy issues in edge-computing-assisted internet of things[J]. IEEE Internet of Things Journal (IoTJ), 2020, 8(6): 4004-4022.

[30]Zuo Y, Jin S, Zhang S. Computation offloading in untrusted MEC-aided mobile blockchain IoT systems[J]. IEEE Transactions on Wireless Communications (TWC), 2021, 20(12): 8333-8347.

[31]Zuo Y, Zhang S, Han Y, et al. Computation resource allocation in mobile blockchain-enabled edge computing networks[C]. 2020 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2020: 617-622.

[32]Zhang H, Xiao Y, Cai L X, et al. A multi-leader multi-follower Stackelberg game for resource management in LTE unlicensed[J]. IEEE Transactions on Wireless Communications (TWC), 2016, 16(1): 348-361.

[33]Xiong Z, Feng S, Wang W, et al. Cloud/fog computing resource management and pricing for blockchain networks[J]. IEEE Internet of Things Journal (IoTJ), 2018, 6(3): 4585-4600.

[34]Xiong Z, Kang J, Niyato D, et al. Cloud/edge computing service management in blockchain networks: Multi-leader multi-follower game-based ADMM for pricing[J]. IEEE Transactions on Services Computing (TSC), 2019, 13(2): 356-367.

[35]Ramamoorthy K M K, Wang W, Sohraby K. NOMAP: A Pricing Scheme for NOMA Resource Block Selection and Power Allocation in Wireless Communications[C]. 2021 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN). IEEE, 2021: 1-6.

[36]Abdellatif K, Abdelmouttalib C. Graph-based computing resource allocation for mobile blockchain[C]. 2018 6th international conference on wireless networks and mobile communications (WINCOM). IEEE, 2018: 1-4.

[37]Qiu X, Liu L, Chen W, et al. Online deep reinforcement learning for computation offloading in blockchain-empowered mobile edge computing[J]. IEEE Transactions on Vehicular Technology (TVT), 2019, 68(8): 8050-8062.

[38]Asheralieva A, Niyato D. Learning-based mobile edge computing resource management to support public blockchain networks[J]. IEEE Transactions on Mobile Computing (TMC), 2019, 20(3): 1092-1109.

[39]Xiong Z, Feng S, Niyato D, et al. Optimal pricing-based edge computing resource management in mobile blockchain[C]. 2018 IEEE International Conference on Communications (ICC). IEEE, 2018: 1-6.

[40]Xiong Z, Zhang Y, Niyato D, et al. When mobile blockchain meets edge computing[J]. IEEE Communications Magazine, 2018, 56(8): 33-39 .

[41]Guo S, Qi Y, Yu P, et al. Edge network resource synergy for mobile blockchain in smart city[C]. 2020 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2020: 1272-1277.

[42]Guo S, Dai Y, Guo S, et al. Blockchain meets edge computing: Stackelberg game and double auction based task offloading for mobile blockchain[J]. IEEE Transactions on Vehicular Technology (TVT), 2020, 69(5): 5549-5561.

[43]刘娅利. 基于区块链的车联网智能计算卸载模型研究[D]. 南京邮电大学, 2022.

[44]王裕鑫. 基于区块链的可信分布式计算平台的设计与实现[D]. 哈尔滨工业大学, 2019.

[45]戴亚盛. 边缘计算可信协同服务机制研究[D]. 江西理工大学, 2019.

[46]景天一. 基于区块链的边缘计算服务迁移安全机制研究[D]. 南京邮电大学, 2022.

[47]柴浩野. 基于区块链的安全高效车联网数据共享策略研究[D]. 电子科技大学, 2022.

[48]谢登科,张赫. 电子数据区块链存证的理论反思[J]. 重庆大学学报(社会科学版), 2022: 1-14.

[49]张成虎,李鹏旭,王琪. 基于区块链和边缘计算的网络金融犯罪预警系统研究[J]. 情报杂志, 2022: 1-7.

[50]刘雪娇,宋庆武,夏莹杰. 基于区块链的车联网矩阵计算安全卸载方案[J]. 浙江大学学报(工学版), 2023, 57(01): 144-154.

[51]王利朋,关志,李青山,等. 区块链数据安全服务综述[J]. 软件学报, 2022: 1-32.

[52]董仲华. 区块链网络中基于边缘计算的资源分配和计算卸载研究[D]. 北京邮电大学, 2021.

[53]张婷. 基于区块链的动态频谱共享关键技术设计与实现[D]. 北京邮电大学, 2021.

[54]赵磊. 面向移动边缘计算的边缘服务器部署及资源分配研究[D]. 西安电子科技大学, 2018.

[55]张先超,任天时,赵耀等.移动边缘计算时延与能耗联合优化方法[J].电子科技大学学报,2022,51(05): 737-742.

[56]姚枝秀,夏士超,李云.不确定网络环境下的任务卸载和资源分配算法[J].中国科学:信息科学,2022,52(07): 1349-1361.

[57]刘庆川. 面向能耗和时延的云边协同网络计算卸载和迁移算法[D].北京邮电大学,2021.

[58]林邦. 区块链网络中边缘计算资源分配机制与优化[D].北京邮电大学, 2020.

[59]SHENG Z, MAHAPATRA C, LEUNG V C M, et al. Energy effificient cooperative computing in mobile wireless sensor networks[J]. IEEE Transactions on Cloud Computing (TCT), 2018, 6(1): 114-126.

[60]LAI P, HE Q, XIA X, et al. Dynamic user allocation in stochastic mobile edge computing systems[J]. IEEE Transactions on Services Computing (TSC), 2021.

[61]谢瑶. 基于DAG区块链的可信车联网关键技术研究[D].电子科技大学,2022.

[62]] N. Houy. The bitcoin mining game[J]. Ledger, 2016: 53-68.

[63]Narayanan A, Bonneau J, Felten E, et al. Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction[M]. Princeton, NJ, USA: Princeton Univ. Press, 2016.

[64]Tran T. D. , Le L. B. .Resource allocation for multi-tenant network slicing: A multi-leader multi-follower stackelberg game approach[J]. IEEE Transactions on Vehicular Technology (TVT), 2020, 69(8): 8886-8899.

[65]Yates R. D. . A framework for uplink power control in cellular radio systems[J]. IEEE Journal On Selected Areas In Communications, 1995, 13(7): 1341-1347.

[66]Sung C. W. and Leung K.-K.. A generalized framework for distributed power control in wireless networks[J]. IEEE Trans. Inf. Theory, 2005, 51(7): 2625-2635.

[67]Liu M, Yu R, Teng Y, et al. Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach[J]. IEEE Trans. Ind. Info., 2019: 1-1.

[68]ZHANG J, XIA W, ZHANG Y, et al. Joint offloading and resource allocation optimization for mobile edge computing[C]. 2017 IEEE Global Communications Conference(GLOBECOM). IEEE, 2017: 1-6.

[69]Kang J, Xiong Z, Niyato D, et al. Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory[J]. IEEE Transactions on Vehicular Technology (TVT). 2019, 68(3): 2906-2920.

[70]Rahman M A, Hossain M S, Loukas G, et al. Blockchain-Based Mobile Edge Computing Framework for Secure Therapy Applications[J]. IEEE Access, 2018(6): 72469-72478.

[71]冯杰. 移动边缘计算中可信资源联合优化方法研究[D].西安电子科技大学,2020.

[72]ANON. Evolved Universal Terrestrial Radio Access (E-UTRA): Physical Channels and Modulation[J]. 3GPP TS 36.211 V8.6.0 Std., Mar., 2009.

[73]You C, Huang K, Chae H, et al. Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading[J]. IEEE Transactions on Wireless Communications (TWC), 2017, 16(3): 1397-1411.

[74]Du J, Zhao L, Chu X, et al. Enabling Low-Latency Applications in LTE-A Based Mixed Fog/Cloud Computing Systems[J]. IEEE Transactions on Vehicular Technology (TVT), 2019, 68(2): 1757-1771.

[75]Mao Y, Zhang J, Song S H, et al. Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems[J]. IEEE Transactions on Wireless Communications (TWC), 2017, 16(9): 5994-6009.