Abstract
Blockchain is regarded as one of the most promising technologies to upgrade e-commerce. This article analyzes the challenges that current e-commerce is facing and introduces a new scenario of e-commerce enabled by blockchain. A framework is proposed for mining tasks in this scenario offloaded onto edge servers based on mobile edge computing. Then, the offloading issue is modeled as a multi-constrained optimization problem, and evolutionary algorithms are utilized and re-designed as solvers. The experimental results validate the efficiency of the framework and algorithms and also show that the lower bound of computation resources exists to obtain the maximum overall revenue.
- Nasir Abbas, Yan Zhang, Amir Taherkordi, and Tor Skeie. 2018. Mobile edge computing: A survey. IEEE Internet of Things Journal 5, 1 (Feb. 2018), 450--465. DOI:https://doi.org/10.1109/JIOT.2017.2750180Google Scholar
Cross Ref
- Ejaz Ahmed, Abdullah Gani, Muhammad [Khurram Khan], Rajkumar Buyya, and Samee U. Khan. 2015. Seamless application execution in mobile cloud computing: Motivation, taxonomy, and open challenges. Journal of Network and Computer Applications 52 (2015), 154--172. DOI:https://doi.org/10.1016/j.jnca.2015.03.001Google Scholar
Digital Library
- Esma Aïmeur and David Schönfeld. 2011. The ultimate invasion of privacy: Identity theft. In 2011 9th Annual International Conference on Privacy, Security and Trust. 24--31. DOI:https://doi.org/10.1109/PST.2011.5971959Google Scholar
Cross Ref
- Samuel Burer and Adam N. Letchford. 2012. Non-convex mixed-integer nonlinear programming: A survey. Surveys in Operations Research and Management Science 17, 2 (2012), 97--106. DOI:https://doi.org/10.1016/j.sorms.2012.08.001Google Scholar
Cross Ref
- Christian Catalini and Joshua S. Gans. 2016. Some simple economics of the blockchain. 22952 (Dec 2016). DOI:https://doi.org/10.3386/w22952Google Scholar
- Yan Chen. 2018. Blockchain tokens and the potential democratization of entrepreneurship and innovation. Business Horizons 61, 4 (2018), 567--575. DOI:https://doi.org/10.1016/j.bushor.2018.03.006Google Scholar
Cross Ref
- Yishan Chen, Shuiguang Deng, Hongtao Ma, and Jianwei Yin. 2019. Deploying data-intensive applications with multiple services components on edge. Mobile Networks and Applications (Apr 2019), 1--16. DOI:https://doi.org/10.1007/s11036-019-01245-3Google Scholar
- Konstantinos Christidis and Michael Devetsikiotis. 2016. Blockchains and smart contracts for the Internet of Things. IEEE Access 4 (2016), 2292--2303. DOI:https://doi.org/10.1109/ACCESS.2016.2566339Google Scholar
Cross Ref
- Shuiguang Deng, Longtao Huang, Javid Taheri, and Albert Y. Zomaya. 2015. Computation offloading for service workflow in mobile cloud computing. IEEE Transactions on Parallel and Distributed Systems 26, 12 (Dec 2015), 3317--3329. DOI:https://doi.org/10.1109/TPDS.2014.2381640Google Scholar
Digital Library
- Shuiguang Deng, Zhengzhe Xiang, Javid Taheri, Khoshkholghi Ali Mohammad, Jianwei Yin, Albert Zomaya, and Schahram Dustdar. 2020. Optimal application deployment in resource constrained distributed edges. IEEE Transactions on Mobile Computing (2020), 1--1. DOI:https://doi.org/10.1109/TMC.2020.2970698Google Scholar
- Shuiguang Deng, Zhengzhe Xiang, Peng Zhao, Javid Taheri, Honghao Gao, Jianwei Yin, and Albert Y. Zomaya. 2020. Dynamical resource allocation in edge for trustable iot systems: A reinforcement learning method. IEEE Transactions on Industrial Informatics 16, 9 (2020), 6103--6113. DOI:https://doi.org/10.1109/TII.2020.2974875Google Scholar
Cross Ref
- Hoang T. Dinh, Chonho Lee, Dusit Niyato, and Ping Wang. 2013. A survey of mobile cloud computing: Architecture, applications, and approaches. Wireless Communications and Mobile Computing 13, 18 (2013), 1587--1611. DOI:https://doi.org/10.1002/wcm.1203Google Scholar
Cross Ref
- Qiang He, Guangming Cui, Xuyun Zhang, Feifei Chen, Shuiguang Deng, Hai Jin, Yanhui Li, and Yun Yang. 2020. A game-theoretical approach for user allocation in edge computing environment. IEEE Transactions on Parallel and Distributed Systems 31, 3 (Mar 2020), 515--529. DOI:https://doi.org/10.1109/TPDS.2019.2938944Google Scholar
Cross Ref
- Miao Hu, Lei Zhuang, Di Wu, Yipeng Zhou, Xu Chen, and Liang Xiao. 2019. Learning driven computation offloading for asymmetrically informed edge computing. IEEE Transactions on Parallel and Distributed Systems 30, 8 (Aug 2019), 1802--1815. DOI:https://doi.org/10.1109/TPDS.2019.2893925Google Scholar
Cross Ref
- Yanjun Jiang and Siye Ding. 2018. A high performance consensus algorithm for consortium blockchain. In 2018 IEEE 4th International Conference on Computer and Communications (ICCC). 2379--2386. DOI:https://doi.org/10.1109/CompComm.2018.8781067Google Scholar
Cross Ref
- Yutao Jiao, Ping Wang, Dusit Niyato, and Zehui Xiong. 2018. Social welfare maximization auction in edge computing resource allocation for mobile blockchain. In IEEE International Conference on Communications (ICC'18). 1--6. DOI:https://doi.org/10.1109/ICC.2018.8422632Google Scholar
Cross Ref
- Rami Khalil and Arthur Gervais. 2017. Revive: Rebalancing off-blockchain payment networks. In 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS’17). ACM, New York, NY, 439--453. DOI:https://doi.org/10.1145/3133956.3134033Google Scholar
Digital Library
- Daniel Kraft. 2015. Difficulty control for blockchain-based consensus systems. Peer-to-Peer Networking and Applications 9, 2 (Apr 2015), 397--413. DOI:https://doi.org/10.1007/s12083-015-0347-xGoogle Scholar
- David W. Kravitz and Jason Cooper. 2017. Securing user identity and transactions symbiotically: IoT meets blockchain. In 2017 Global Internet of Things Summit (GIoTS). 1--6. DOI:https://doi.org/10.1109/GIOTS.2017.8016280Google Scholar
- Hongwei Li, Rongxing Lu, Liang Zhou, Bo Yang, and Xuemin Shen. 2014. An efficient merkle-tree-based authentication scheme for smart grid. IEEE Systems Journal 8, 2 (Jun 2014), 655--663. DOI:https://doi.org/10.1109/JSYST.2013.2271537Google Scholar
Cross Ref
- Weiling Li, Kewen Liao, Qiang He, and Yunni Xia. 2019. Performance-aware cost-effective resource provisioning for future grid IoT-cloud system. Journal of Energy Engineering 145, 5 (Oct 2019), 04019016. DOI:https://doi.org/10.1061/(ASCE)EY.1943-7897.0000611Google Scholar
Cross Ref
- Mengting Liu, F. Richard Yu, Yinglei Teng, Victor C. M. Leung, and Mei Song. 2019. Distributed resource allocation in blockchain-based video streaming systems with mobile edge computing. IEEE Transactions on Wireless Communications 18, 1 (Jan 2019), 695--708. DOI:https://doi.org/10.1109/TWC.2018.2885266Google Scholar
Digital Library
- Xinwei Liu, Jiaxin Zhang, Xing Zhang, and Wenbo Wang. 2017. Mobility-aware coded probabilistic caching scheme for MEC-enabled small cell networks. IEEE Access 5 (2017), 17824--17833. DOI:https://doi.org/10.1109/ACCESS.2017.2742555Google Scholar
Cross Ref
- Pavel Mach and Zdenek Becvar. 2017. Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys Tutorials 19, 3 (Aug 2017), 1628--1656. DOI:https://doi.org/10.1109/COMST.2017.2682318Google Scholar
Digital Library
- Yuyi Mao, Changsheng You, Jun Zhang, Kaibin Huang, and Khaled B. Letaief. 2017. A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys Tutorials 19, 4 (Fourthquarter 2017), 2322--2358. DOI:https://doi.org/10.1109/COMST.2017.2745201Google Scholar
Cross Ref
- Cong Nguyen, Zehui Xiong, Ping Wang, and Dusit Niyato. 2018. Optimal auction for edge computing resource management in mobile blockchain networks: A deep learning approach. In IEEE International Conference on Communications (ICC'18). 1--6. DOI:https://doi.org/10.1109/ICC.2018.8422743Google Scholar
- Shihao Shen, Yiwen Han, Xiaofei Wang, and Yan Wang. 2019. Computation offloading with multiple agents in edge-computing--supported IoT. ACM Transactions on Sensor Networks 16 (Dec 2019), 1--27. DOI:https://doi.org/10.1145/3372025Google Scholar
- Xiaoning Sun, Shu Wang, Yunni Xia, and Wanbo Zheng. 2020. Predictive-trend-aware composition of web services with time-varying quality-of-service. IEEE Access 8 (2020), 1910--1921. DOI:https://doi.org/10.1109/ACCESS.2019.2962703Google Scholar
Cross Ref
- De-long Tang, Si-ai Liao, Kun Ding, and Joseph Shyu. 2019. A strategic overview of blockchain applications in the healthcare. 2nd International Conference on Social Science, Public Health and Education (SSPHE'18) (Jan 2019). DOI:https://doi.org/10.2991/ssphe-18.2019.84Google Scholar
- Wenda Tang, Xuan Zhao, Wajid Rafiq, and Wanchun Dou. 2018. A blockchain-based offloading approach in fog 661 computing environment. In 2018 IEEE International Conference on Parallel Distributed Processing with Applications, Ubiquitous Computing Communications, Big Data Cloud Computing, Social Computing Networking, Sustainable Computing Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). 308--315. DOI:https://doi.org/10.1109/BDCloud.2018.00056Google Scholar
- Pinyaphat Tasatanattakool and Chian Techapanupreeda. 2018. Blockchain: Challenges and applications. In 2018 International Conference on Information Networking (ICOIN). 473--475. DOI:https://doi.org/10.1109/ICOIN.2018.8343163Google Scholar
Cross Ref
- Huaimin Wang, Zibin Zheng, Shaoan Xie, Hong-Ning Dai, and Xiangping Chen. 2018. Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services 14 (Oct 2018), 352--375. DOI:https://doi.org/10.1504/IJWGS.2018.10016848Google Scholar
Cross Ref
- Shangguang Wang, Guo Yan, Ning Zhang, Peng Yang, Ao Zhou, and Xuemin Shen. 2019. Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach. IEEE Transactions on Mobile Computing PP (Dec 2019), 1--1. DOI:https://doi.org/10.1109/TMC.2019.2957804Google Scholar
Cross Ref
- Hiroki Watanabe, Shigeru Fujimura, Atsushi Nakadaira, Yasuhiko Miyazaki, Akihito Akutsu, and Jay Kishigami. 2016. Blockchain contract: Securing a blockchain applied to smart contracts. In 2016 IEEE International Conference on Consumer Electronics (ICCE). 467--468. DOI:https://doi.org/10.1109/ICCE.2016.7430693Google Scholar
Cross Ref
- Longfei Wu, Xiaojiang Du, Wei Wang, and Bin Lin. 2018. An out-of-band authentication scheme for Internet of Things using blockchain technology. In 2018 International Conference on Computing, Networking and Communications (ICNC). 769--773. DOI:https://doi.org/10.1109/ICCNC.2018.8390280Google Scholar
Cross Ref
- Zehui Xiong, Shaohan Feng, Dusit Niyato, Ping Wang, and Zhu Han. 2018. Optimal pricing-based edge computing resource management in mobile blockchain. In IEEE International Conference on Communications (ICC'18). 1--6. DOI:https://doi.org/10.1109/ICC.2018.8422517Google Scholar
Cross Ref
- Zehui Xiong, Yang Zhang, Dusit Niyato, Ping Wang, and Zhu Han. 2018. When mobile blockchain meets edge computing. IEEE Communications Magazine 56, 8 (Aug 2018), 33--39. DOI:https://doi.org/10.1109/MCOM.2018.1701095Google Scholar
Digital Library
- Cheng Zhang, Hailiang Zhao, and Shuiguang Deng. 2018. A density-based offloading strategy for IoT devices in edge computing systems. IEEE Access 6 (Nov 2018), 73520--73530. DOI:https://doi.org/10.1109/ACCESS.2018.2882452Google Scholar
Cross Ref
- Yinghui Zhang, Robert H. Deng, Ximeng Liu, and Dong Zheng. 2018. Blockchain based efficient and robust fair payment for outsourcing services in cloud computing. Information Sciences 462 (2018), 262--277. DOI:https://doi.org/10.1016/j.ins.2018.06.018Google Scholar
Digital Library
- Hailiang Zhao, Shuiguang Deng, Cheng Zhang, Wei Du, Qiang He, and Jianwei Yin. 2019. A mobility-aware cross-edge computation offloading framework for partitionable applications. In 2019 IEEE International Conference on Web Services (ICWS). 193--200. DOI:https://doi.org/10.1109/ICWS.2019.00041Google Scholar
Cross Ref
Index Terms
Incentive-Driven Computation Offloading in Blockchain-Enabled E-Commerce
Recommendations
Incentive mechanism for computation offloading using edge computing
IoT-based services benefit from cloud which offers a virtually unlimited capabilities, such as storage, processing, and communication. However, the challenges are still open for mobile users to receive computation from the cloud with satisfied quality-...
Learning for Smart Edge: Cognitive Learning-Based Computation Offloading
AbstractWith the development of intelligent applications, more and more intelligent applications are computation intensive, data intensive and delay sensitive. Compared with traditional cloud computing, edge computing can reduce communication delay by ...
Intelligent task prediction and computation offloading based on mobile-edge cloud computing
AbstractEdge computing overcomes the high communication delay shortcoming of traditional cloud computing and provides computing services with high reliability and high bandwidth for mobile devices. At present, edge computing has become the ...
Highlights- The paper designs an intelligent computation offloading based MEC architecture.






Comments