Blockchain-Enabled Data Collection and Sharing for Industrial IoT With Deep Reinforcement Learning

With the rapid development of smart mobile terminals (MTs), various industrial Internet of things (IIoT) applications can fully leverage them to collect and share data for providing certain services. However, two key challenges still remain. One is how to achieve high-quality data collection with limited MT energy resource and sensing range. Another is how to ensure security when sharing and exchanging data among MTs, to prevent possible device failure, network communication failure, malicious users or attackers, etc. To this end, we propose a blockchain-enabled efficient data collection and secure sharing scheme combining Ethereum blockchain and deep reinforcement learning (DRL) to create a reliable and safe environment. In this scheme, DRL is used to achieve the maximum amount of collected data, and the blockchain technology is used to ensure security and reliability of data sharing. Extensive simulation results demonstrate that the proposed scheme can provide higher security level and stronger resistance to attack than a traditional database based data sharing scheme for different levels/types of attacks.

[1]  Zibin Zheng,et al.  IoT Service Based on JointCloud Blockchain: The Case Study of Smart Traveling , 2018, 2018 IEEE Symposium on Service-Oriented System Engineering (SOSE).

[2]  Prateek Saxena,et al.  Making Smart Contracts Smarter , 2016, IACR Cryptol. ePrint Arch..

[3]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[4]  Danny Bradbury,et al.  The problem with Bitcoin , 2013 .

[5]  Athanasios V. Vasilakos,et al.  Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles , 2016, Sensors.

[6]  Stephen B. Wicker,et al.  Vegvisir: A Partition-Tolerant Blockchain for the Internet-of-Things , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[7]  Lei Shu,et al.  Challenges and Research Issues of Data Management in IoT for Large-Scale Petrochemical Plants , 2018, IEEE Systems Journal.

[8]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[9]  Andrew Raij,et al.  A Survey of Incentive Techniques for Mobile Crowd Sensing , 2015, IEEE Internet of Things Journal.

[10]  Xi Fang,et al.  Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones , 2016, IEEE/ACM Transactions on Networking.

[11]  Xu Chen,et al.  Crowdlet: Optimal worker recruitment for self-organized mobile crowdsourcing , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[12]  Jian Ma,et al.  Learning-Based Energy-Efficient Data Collection by Unmanned Vehicles in Smart Cities , 2018, IEEE Transactions on Industrial Informatics.

[13]  Miguel A. Labrador,et al.  Privacy-Preserving Mechanisms for Crowdsensing: Survey and Research Challenges , 2017, IEEE Internet of Things Journal.

[14]  Joaquín B. Ordieres Meré,et al.  Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm , 2014, 2014 IEEE International Conference on Industrial Engineering and Engineering Management.

[15]  Leslie Lamport,et al.  The part-time parliament , 1998, TOCS.

[16]  Yunhao Liu,et al.  Incentives for Mobile Crowd Sensing: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[17]  Zibin Zheng,et al.  An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends , 2017, 2017 IEEE International Congress on Big Data (BigData Congress).

[18]  Mohsen Guizani,et al.  Home M2M networks: Architectures, standards, and QoS improvement , 2011, IEEE Communications Magazine.

[19]  SK Hafizul Islam,et al.  Provably Secure and Lightweight Certificateless Signature Scheme for IIoT Environments , 2018, IEEE Transactions on Industrial Informatics.

[20]  Jie Wu,et al.  Toward QoI and Energy Efficiency in Participatory Crowdsourcing , 2015, IEEE Transactions on Vehicular Technology.

[21]  Massimo Bartoletti,et al.  A Survey of Attacks on Ethereum Smart Contracts (SoK) , 2017, POST.

[22]  Kin K. Leung,et al.  Energy-Aware Participant Selection for Smartphone-Enabled Mobile Crowd Sensing , 2017, IEEE Systems Journal.

[23]  Virginia Pilloni,et al.  How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0 , 2018, Future Internet.

[24]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[25]  Peng Jiang,et al.  A Survey on the Security of Blockchain Systems , 2017, Future Gener. Comput. Syst..

[26]  Chi Harold Liu,et al.  Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach , 2018, IEEE Journal on Selected Areas in Communications.

[27]  Stefanos Gritzalis,et al.  Cryptographic Solutions for Industrial Internet-of-Things: Research Challenges and Opportunities , 2018, IEEE Transactions on Industrial Informatics.

[28]  Kin K. Leung,et al.  Energy-Efficient Event Detection by Participatory Sensing Under Budget Constraints , 2017, IEEE Systems Journal.

[29]  Jie Wu,et al.  Online Task Assignment for Crowdsensing in Predictable Mobile Social Networks , 2017, IEEE Transactions on Mobile Computing.

[30]  Ethan Heilman,et al.  Low-Resource Eclipse Attacks on Ethereum's Peer-to-Peer Network , 2020, IACR Cryptol. ePrint Arch..

[31]  Ethan Heilman,et al.  Eclipse Attacks on Bitcoin's Peer-to-Peer Network , 2015, USENIX Security Symposium.

[32]  Zhetao Li,et al.  Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[33]  Shengli Xie,et al.  Cognitive machine-to-machine communications: visions and potentials for the smart grid , 2012, IEEE Network.

[34]  Arshdeep Bahga,et al.  Blockchain Platform for Industrial Internet of Things , 2016 .

[35]  Bo Zhang,et al.  An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints , 2015, ACM Trans. Intell. Syst. Technol..

[36]  Nir Kshetri,et al.  Can Blockchain Strengthen the Internet of Things? , 2017, IT Professional.

[37]  Quanyan Zhu,et al.  Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach , 2013, IEEE Transactions on Smart Grid.

[38]  Jianwei Huang,et al.  Delay-Sensitive Mobile Crowdsensing: Algorithm Design and Economics , 2018, IEEE Transactions on Mobile Computing.

[39]  Zhu Wang,et al.  Mobile Crowd Sensing and Computing , 2015, ACM Comput. Surv..

[40]  Sooyong Park,et al.  Where Is Current Research on Blockchain Technology?—A Systematic Review , 2016, PloS one.

[41]  Elaine Shi,et al.  Step by Step Towards Creating a Safe Smart Contract: Lessons and Insights from a Cryptocurrency Lab , 2016, Financial Cryptography Workshops.

[42]  Dennis Miller,et al.  Blockchain and the Internet of Things in the Industrial Sector , 2018, IT Professional.