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Latency of write TXs (in seconds) measured with a local Indy-based BC and different arrival rates (1-500).
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![Figure 1. Our proposed blockchain architecture based on Indy [15].](publication/364039226/figure/fig1/AS:11431281087402824@1664549159150/Our-proposed-blockchain-architecture-based-on-Indy-15_Q320.jpg)
Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, we can find many other application fields in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing perm...
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... can also see that increasing the arrival rate increases the average latency for both types of requests. It is observable in the table that the average write latency fluctuates from 1.8 to 7.1 s per TX in the four-node network, and from 2.0 to 7.7 s per TX in the eight-node network, depending on the arrival rate, as Figure 4 shows. On the other hand, the average read latency fluctuates from 0.03 to 1.4 s per TX in the four-node network, and from 0.03 to 2.4 s per TX in the eight-node network, as shown in Figure 5. ...
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Smart contracts function like specialized computer programs on the blockchain. Many of these contracts are on Ethereum, but sometimes these contracts have problems with security. These problems caused big money losses and made the blockchain less stable. Smart contracts are self-executing with predefined rules and are at the core of many blockchain...
Citations
... In [7], the authors analyzed and compared ShoCard, WeIdentity, Microsoft DID, Cambridge Blockchain, uPort, and Sovrin. Nevertheless, [70] presents a detailed latency analysis of a permissioned blockchain system built with Indy and Aries, and [15] provides an overview and analysis of SSI properties from the literature. Related to government use, [19] analyzed SSI and Blockchain to access services of a Public Administration (Italy). ...
... In this way, users can easily define their initial application requirements using templates based on real cases that have been previously deployed, tested, or simulated. -Relation of characteristics: We will use our knowledge, experience, and continuously published state-of-the-art analysis related to the configuration of BC and CC-based SSs, for example [26]. Accordingly, pre-analysis shared between the two simulators shall be the most accurate and efficient (in terms of computational and time cost). ...
In the past years, we have seen an unprecedented pace of technological development in smart applications. Smart Systems incorporate securely connected sensors, actuators, and data processing resources to provide digital services. They provide a wide range of smart applications using emerging technologies that address governmental or industrial processes or citizen life in smart cities, and many of them have been affected by the COVID-19 pandemic which involved a general lack of trust. Integrating Blockchain-based data management into smart systems can enhance the performance, trust, and privacy of their applications, which are getting more and more crucial. In this paper, we propose a vision for a unified Simulation as a Service platform, which will be able to model and investigate Blockchain-based smart systems exploiting IoT, Fog, and Cloud Computing infrastructures.
... This means that the average transaction time of the Fabric network using the PBFT consensus algorithm is completed within 1 second. Correspondingly, the transaction confirmation time of Bitcoin is 10 minutes [62]. Compared with the public blockchain, the transaction confirmation delay of Fabric is extremely low. ...
In the fire scene investigation, the firefighting Internet of Things (IoT) data is the key electronic evidence for event analysis and responsibility determination. However, the traditional centralized storage method leads to data easy to be tampered with and damaged. To solve these problems, this paper designs and implements a secure, reliable and low-cost distributed firefighting IoT data storage scheme based on the Fabric framework, combining blockchain technology, Interplanetary File System (IPFS) and Practical Byzantine Fault Tolerance (PBFT) consensus algorithm to provide a strong support for fire accident traceability. This scheme mainly includes the storage model, key algorithms and Fabric construction and improvement. IPFS stores the complete firefighting IoT data, as the off-chain storage system of the blockchain, and the blockchain only stores the storage address (IPFS hash) of data returned by IPFS, thus reducing the storage space overhead of the blockchain and ensuring data security. Further, we adopt the Fabric framework as the blockchain platform for firefighting IoT data, and embed the PBFT consensus algorithm into the framework to ensure the reliability of consensus nodes in Fabric, thus improving the availability of the blockchain. In addition, we use the AES and RSA algorithms to ensure the security of firefighting IoT data storage and transmission. Through system analysis and experimental testing, the proposed scheme meets the need for secure storage and traceability of firefighting IoT data. Compared with the storage scheme using only blockchain, the blockchain combined with IPFS technology has advantages in storage space occupation, significantly improved throughput, and lower latency overhead. Meanwhile, compared with the official Fabric, the improved Fabric supports Byzantine fault tolerance and has better security.
This PhD dissertation concludes a three-year long research journey on the integration of Fog Computing and Blockchain technologies. The main aim of such integration is to address the challenges of each of these technologies, by integrating it with the other. Blockchain technology (BC) is a distributed ledger technology in the form of a distributed transactional database, secured by cryptography, and governed by a consensus mechanism. It was initially proposed for decentralized cryptocurrency applications with practically proven high robustness. Fog Computing (FC) is a geographically distributed computing architecture, in which various heterogeneous devices at the edge of network are ubiquitously connected to collaboratively provide elastic computation services. FC provides enhanced services closer to end-users in terms of time, energy, and network load. The integration of FC with BC can result in more efficient services, in terms of latency and privacy, mostly required by Internet of Things systems.
