Fuzzy rules based efficient event-driven simulation of blockchain-based applications

Decentralized application (DAPP), replacing traditional business logic and data access layer with block chain, is a new form of Internet service. Testing DAPP requires large-scale distributed systems. Performing experiments in a real system is costly and difficult. This article carefully analyses the process of block generation and synchronization and explains the reasons for the low efficiency of block chain system simulation. We incorporate fuzzy rule based model for enhancing the logging system in blockchain. Rules based on fuzzy are utilized inside system of fuzzy logic to obtain outcome on basis of input variables. The data of Ethereum and Bitcoin proves that the block generation interval conforms to the exponential distribution, and the real PoW calculation can be replaced with random numbers. Both block verification and network propagation processes have latency, which can be simulated with asynchronous messaging. Based on the above analysis, this article proposes a high-performance simulation method based on event-driven model, which is suitable for describing the communication and synchronization behave our of block chain networks. The method can effectively describe the block generation, the synchronization process between nodes, and supports different equity proof forms. Using this method, the performance of the PoW systemis tested. Under the ecs.c6.xlargeinstance,the simulation running speed reaches 782 times of actual system. Further experiments show that this method can be efficiently used in larger-scale networks and is an effective tool for DAPP developing and testing.

[1]  Bayan Mahdi Sabbar,et al.  An overview on wireless sensor networks and finding optimal location of nodes , 2019, Periodicals of Engineering and Natural Sciences (PEN).

[2]  Albert Levi,et al.  A Survey on Anonymity and Privacy in Bitcoin-Like Digital Cash Systems , 2018, IEEE Communications Surveys & Tutorials.

[3]  Robert H. Deng,et al.  Blockchain based efficient and robust fair payment for outsourcing services in cloud computing , 2018, Inf. Sci..

[4]  Geir Hovland,et al.  Nonlinear Feedback Control and Stability Analysis of a Proof-of-Work Blockchain , 2017 .

[5]  Hui Li,et al.  Blockchain-Based Secure Time Protection Scheme in IoT , 2019, IEEE Internet of Things Journal.

[6]  Lavanya Ramakrishnan,et al.  Performance and cost analysis of the Supernova factory on the Amazon AWS cloud , 2011, CloudCom 2011.

[7]  Hong Li,et al.  Blockchain for Large-Scale Internet of Things Data Storage and Protection , 2019, IEEE Transactions on Services Computing.

[8]  Bei Yu,et al.  Efficient semantic-based content search in P2P network , 2004, IEEE Transactions on Knowledge and Data Engineering.

[9]  Xue Liu,et al.  Towards Secure Industrial IoT: Blockchain System With Credit-Based Consensus Mechanism , 2019, IEEE Transactions on Industrial Informatics.

[10]  R. B. Patel,et al.  Capacity and interference aware link scheduling with channel assignment in wireless mesh networks , 2011, J. Netw. Comput. Appl..

[11]  Marcel Antal,et al.  Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids , 2018, Sensors.

[12]  Peter Fairley Ethereum will cut back its absurd energy use , 2019, IEEE Spectrum.

[13]  Yan Zhang,et al.  Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains , 2017, IEEE Transactions on Industrial Informatics.

[14]  Philip C. Treleaven,et al.  Blockchain Technology in Finance , 2017, Computer.

[15]  Salil S. Kanhere,et al.  BlockChain: A Distributed Solution to Automotive Security and Privacy , 2017, IEEE Communications Magazine.

[16]  Hemang Subramanian,et al.  Decentralized blockchain-based electronic marketplaces , 2017, Commun. ACM.

[17]  Sarah Underwood,et al.  Blockchain beyond bitcoin , 2016, Commun. ACM.

[18]  Naixue Xiong,et al.  Cold-Start Recommendation Using Bi-Clustering and Fusion for Large-Scale Social Recommender Systems , 2014, IEEE Transactions on Emerging Topics in Computing.

[19]  Calton Pu,et al.  Continual Queries for Internet Scale Event-Driven Information Delivery , 1999, IEEE Trans. Knowl. Data Eng..

[20]  Paolo Tasca,et al.  Blockchain Technologies: The Foreseeable Impact on Society and Industry , 2017, Computer.

[21]  Nauman Aslam,et al.  A bonded channel in cognitive wireless body area network based on IEEE 802.15.6 and internet of things , 2020, Comput. Commun..

[22]  Voon Chet Koo,et al.  IPDDF: an improved precision dense descriptor based flow estimation , 2020, CAAI Trans. Intell. Technol..

[23]  Lavanya Ramakrishnan,et al.  Performance and cost analysis of the Supernova factory on the Amazon AWS cloud , 2011, Sci. Program..

[24]  Moslem Noori,et al.  Lifetime Analysis of Random Event-Driven Clustered Wireless Sensor Networks , 2011, IEEE Transactions on Mobile Computing.

[25]  Markus Kraft,et al.  Blockchain technology in the chemical industry: Machine-to-machine electricity market , 2017 .

[26]  Nasir Saleem,et al.  Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments , 2020, Int. J. Interact. Multim. Artif. Intell..

[27]  Naixue Xiong,et al.  Predictive control for vehicular sensor networks based on round-trip time-delay prediction , 2010, IET Commun..

[28]  Massimo Di Pierro,et al.  What Is the Blockchain? , 2017, Computing in Science & Engineering.