In permissioned blockchains, a set of identifiable miners validates transactions and creates new blocks. In scholarship, the proposed solution for the consensus protocol is usually inspired by the Byzantine fault tolerance (BFT) based on voting rather than the proof-of-work (PoW). The advantage of PoW with respect to BFT is that it allows the final user to evaluate the cost required to change a confirmed transaction without the need to trust the consortium of miners. In this paper, we analyse the problems that arise from the application of PoW in permissioned blockchains. In standard PoW, it may be easy for colluded miners to temporarily reach 50% of the total hash power (HP). Moreover, since mining rewards are not usually expected in permissioned contexts, the problem of balancing the computational efforts among the miners becomes crucial. We propose a solution based on a sliding window algorithm to address these problems and analyse its effectiveness in terms of fairness and security. Furthermore, we present a quantitative, analytical model in order to assess its capacity to balance the hash power provided by heterogeneous miners. Our study considers the trade-off between the need to trust the entire consortium of miners guaranteed by the global HP invested by the mining process and the need to prevent collusion among malicious miners aimed at reaching 50% of the total HP. As a result, the model can be used to find the optimal parameters for the sliding window protocol.INDEX TERMS Permissioned blockchain, Markov models, security, fairness.IVAN MALAKHOV received the B.S. degree in information technology, information and communication and the M.S. degree in computer system networking and telecommunications, information and communication from the Higher School of Economics, Moscow, Russia, in 2017 and 2019, respectively, and the M.S. degree in computer science from the University Ca' Foscari of Venice, Italy, in 2019, where he is currently pursuing the Ph.D. degree in computer science. His research interests include the quantitative analysis of public and private blockchains.ANDREA MARIN (Senior Member, IEEE) received the Ph.D. degree in computer science from the University Ca' Foscari of Venice, in 2007. He is currently an Associate Professor in computer science with the University Ca' Foscari of Venice. His research interests include the stochastic modeling of computer and communication systems for performance and reliability analysis, queuing theory, and models with product-form solutions. He has contributed to developing a probabilistic calculus for the formal analysis of wireless ad hoc networks.SABINA ROSSI received the Ph.D. degree in computational mathematics and informatics from the University of Padova, in 1994. She has been a Visiting
The paper reviews the current situation of the Augmented Reality and Internet of Things markets. The implementing possibilities of AR for Big Data visualization from IoT devices are considered in this paper. The review and the analysis of methods, tools, products and data system of the visualization are presented. The paper provides an overview of the programs and devices of Augmented Reality, and an overview of development environments. The paper presents the existing classifications of computerized data visualization tools and proposes new classification, which takes into account interactive visualization, the purpose of the tool, the type of software product, the availability of ready-made templates, and other characteristics. The article proposes the architecture of the system for collecting data from IoT endpoint devices based on the Heltec modules. Experiments based on the developed experimental stand were carried out with Heltec devices of both versions to determine the number of losses with increasing distance between the sending device and the receiving device. The results of measuring the power consumption of these devices are presented in two modes: in standby mode and when sending a message to the Heltec endpoint device and in standby mode and when receiving a message for the base station. These studies were conducted using various data transfer protocols (LoRa, Wi-Fi and Bluetooth). The paper presents the result of the development of a digital twin of a university building and the development of augmented reality software for receiving data from real-time data collection devices.
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