Integrating Blockchain and Fog Computing Technologies for Efficient Privacy-preserving Systems

Abstract

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.

Exploring the Synergy of Fog Computing, Blockchain, and Federated Learning for IoT Applications: A Systematic Literature Review

“…BC systems encompass distributed computing structures that primarily store and process a Distributed Ledger (DL) within Peer-to-Peer (P2P) networks [9], [33], [34]. Both permissioned and permissionless variants of BC exist, offering authorized or open participation in the system, respectively.…”

“…Both permissioned and permissionless variants of BC exist, offering authorized or open participation in the system, respectively. The data archived within Blockchain DLs can be public or private, depending on the domain/profile of users capable of requesting or executing tasks within the system [33].…”

“…Miners represent the core of the BC ecosystem, undertaking the resolution of computational puzzles or problems (whose complexity is conditionally predefined) to validate blocks and integrate them into the chain. Various consensus algorithms, including proof-based mechanisms (e.g., Proofof-Work (PoW), Proof-of-Stake (PoS), Proof-of-Authority (PoA)), as well as non-proof-based strategies (e.g., Byzantine Fault Tolerance (BFT)), are employed to achieve consensus on transaction validity [33]. Forking arises when different valid DL versions emerge, each consensus algorithm having a distinct protocol for handling forks, while hashing prior blocks maintains BC's immutability [9], [34].…”

“…The potential application of BC in decentralized cloud servers at the network edge has garnered significant attention, leading to the proposition of models like Blockchain-as-a-Service (BaaS) to revolutionize CC across various domains. These models aim to elevate the capabilities of CC by seamlessly integrating BC technology, offering a versatile array of functionalities (i.e., e-voting, authentication, and identity management to trading, reputation management, supply chain management, data management) [9], [33]- [35].…”

“…BC-based systems are structured across five layers (see Figure 3) comprising hardware/infrastructure, data, network, consensus (including incentives), and application [9], [33], [34]. These layers are described below:…”