@article {16776,
	title = {Exploring the Synergy of Fog Computing, Blockchain, and Federated Learning for IoT Applications: A Systematic Literature Review},
	journal = {IEEE Access},
	volume = {vol 12},
	year = {2024},
	abstract = {The proliferation of Internet of Things (IoT) applications poses formidable challenges in managing data processing, privacy, and security. In response, technologies such as Fog Computing (FC), Blockchain (BC), and Federated Learning (FL) have emerged as promising solutions. Combining these technologies can broaden their scope, and impose novel challenges. This paper conducts a Systematic Literature Review (SLR) to investigate their integration within the IoT domain, systematically evaluating the current state-of-the-art by analyzing 40 papers against 38 extraction criteria, encompassing technical characteristics specific to FC, BC, FL, or their integration. The findings offer insights into the advantages, challenges, opportunities, and limitations of this integration, addressing data processing, privacy, and security concerns in IoT. By filling a research gap and directly examining FC, BC, and FL interoperability across architectural layers, this study contributes to knowledge expansion in the field. This paper proposes a novel framework for implementing FL and BC within FC environments for IoT applications, alongside a comprehensive synthesis of existing literature, distinguishing it from previous research efforts. Furthermore, it offers valuable insights into the current landscape, identifies research needs, and proposes future research directions. The framework and literature synthesis provided allow readers to access customized information on FC-BC-FL integration, aiding in designing and implementing robust IoT solutions.},
	keywords = {Blockchain, edge computing, federated learning, fog computing, Internet of Things, systematic literature review},
	doi = {10.1109/ACCESS.2024.3398034},
	url = {https://ieeexplore.ieee.org/document/10522660},
	author = {Wilson Valdez Solis and Juan Marcelo Parra-Ullauri and Attila Kert{\'e}sz}
}
@conference {16779,
	title = {Synergizing Fuzzy-based Task Offloading with Machine Learning-driven Forecasting for IoT},
	booktitle = {2024 IEEE 8th International Conference on Fog and Edge Computing (ICFEC)},
	year = {2024},
	pages = {71-78},
	abstract = {Nowadays IoT applications play an increasing role in our lives, which require adaptive solutions to meet an acceptable level of quality. To support this need, IoT is often coupled with fog and cloud services, especially for real-time applications, where short latency and fast data integration matter the most. To manage such IoT-Fog-Cloud systems effectively, task offloading methods are needed, which represent an NP-hard problem, to be addressed by scheduling algorithms using some sort of heuristics. In this paper, we present a fuzzy-based offloading algorithm improved by machine learning-based time-series prediction to boost its decision making, and thus to reach efficient system usage. We also extend and use the DISSECT-CF-Fog simulator to evaluate our proposed approach on a real-world-based IoT use case. Our results showed improvements by reducing execution time of the considered IoT application with essentially the same utilisation cost, energy consumption and network usage.},
	keywords = {edge computing, energy consumption, fog computing, Forecasting, Internet of Things, machine learning, Machine learning algorithms, NP-hard problem, Prediction, Prediction algorithms, Real-time systems, Scheduling algorithms, simulation, Task Offloading, Time series analysis},
	doi = {10.1109/ICFEC61590.2024.00015},
	url = {https://ieeexplore.ieee.org/document/10707224},
	author = {M{\'a}rkus, Andr{\'a}s and Heged{\H u}s, Valentin D{\'a}niel and Dombi, J{\'o}zsef D{\'a}niel and Kert{\'e}sz, Attila}
}
@article {16780,
	title = {PriFoB: A Privacy-aware Fog-enhanced Blockchain-based system for Global Accreditation and Credential Verification},
	journal = {Journal of Network and Computer Applications},
	volume = {205},
	year = {2022},
	abstract = {Trusted online credential management solutions are needed for instant and practical verification. Most of the available frameworks targeting this field violate the privacy of end-users or lack sufficient solutions in terms of security and Quality-of-Service (QoS). In this paper, we propose a Privacy-aware Fog-enhanced Blockchain-based online credential management solution, namely PriFoB. Our proposed solution adopts a public permissioned Blockchain model with different reliable encryption schemes, standardized Zero-Knowledge-Proofs (ZKPs) and Digital Signatures (DSs) within a Fog{\textendash}Blockchain integrated framework, which is also GDPR compliant. We deploy both the Proof-of-Authority (PoA) and the Signatures-of-Work (SoW) consensus algorithms for efficient and secure handling of Verifiable Credentials (VCs) and global accreditation of VC issuers, respectively. Furthermore, we propose a novel three-dimensional DAG-based model of the Distributed Ledger (3DDL), and provide a ready-to-deploy PriFoB implementation. We discuss insights regarding the utilization and the potential of PriFoB, and evaluate it in terms of security, privacy, latency, throughput and power utilization. We analyze its performance in different layers of a Fog-enabled cloud architecture with simulation and emulation, and we show that PriFoB outperforms several Blockchain-based solutions utilizing Ethereum, Hyperledger Fabric, Hyperledger Besu and Hyperledger Indy platforms.},
	keywords = {Blockchain, Credential Verification, fog computing, Global accreditation, Privacy, Self Sovereign Identity (SSI)},
	issn = {1084-8045},
	doi = {https://doi.org/10.1016/j.jnca.2022.103440},
	url = {https://www.sciencedirect.com/science/article/pii/S1084804522000935},
	author = {Hamza Baniata and Attila Kert{\'e}sz}
}
@article {16781,
	title = {FoBSim: an extensible open-source simulation tool for integrated fog-blockchain systems},
	journal = {PeerJ Computer Science},
	volume = {vol 7},
	year = {2021},
	abstract = {A lot of hard work and years of research are still needed for developing successful Blockchain (BC) applications. Although it is not yet standardized, BC technology was proven as to be an enhancement factor for security, decentralization, and reliability, leading to be successfully implemented in cryptocurrency industries. Fog computing (FC) is one of the recently emerged paradigms that needs to be improved to serve Internet of Things (IoT) environments of the future. As hundreds of projects, ideas, and systems were proposed, one can find a great R\&D potential for integrating BC and FC technologies. Examples of organizations contributing to the R\&D of these two technologies, and their integration, include Linux, IBM, Google, Microsoft, and others. To validate an integrated Fog-Blockchain protocol or method implementation, before the deployment phase, a suitable and accurate simulation environment is needed. Such validation should save a great deal of costs and efforts on researchers and companies adopting this integration. Current available simulation environments facilitate Fog simulation, or BC simulation, but not both. In this paper, we introduce a Fog-Blockchain simulator, namely FoBSim, with the main goal to ease the experimentation and validation of integrated Fog-Blockchain approaches. According to our proposed workflow of simulation, we implement different Consensus Algorithms (CA), different deployment options of the BC in the FC architecture, and different functionalities of the BC in the simulation. Furthermore, technical details and algorithms on the simulated integration are provided. We validate FoBSim by describing the technologies used within FoBSim, highlighting FoBSim{\textquoteright}s novelty compared to the state-of-the-art, discussing the event validity in FoBSim, and providing a clear walk-through validation. Finally, we simulate case studies, then present and analyze the obtained results, where deploying the BC network in the fog layer shows enhanced efficiency in terms of total run time and total storage cost.},
	keywords = {Blockchain, fog computing, simulation},
	doi = {10.7717/peerj-cs.431},
	url = {https://peerj.com/articles/cs-431/},
	author = {Baniata, Hamza and Kert{\'e}sz, Attila}
}
@article {16782,
	title = {A survey and taxonomy of simulation environments modelling fog computing},
	journal = {Simulation Modelling Practice and Theory},
	volume = {vol 101},
	year = {2020},
	abstract = {In the past ten years, the latest advances in Information and Communication Technology had a significant impact on distributed systems by giving birth to paradigms such as Cloud Computing, Fog Computing and the Internet of Things (IoT). The environments they created are closely coupled in most cases: IoT sensors and devices generate data that have to be stored, processed and analysed by cloud or fog services, depending on the actual application needs. These IoT-Fog-Cloud systems are very complex, and the use of simulations in their design, development and operational processes is inevitable. Nowadays, there are many simulator solutions available to model and analyse these systems depending our research needs, but in many cases it is hard to grasp their differences, and implementing certain scenarios in different tools is time consuming. The goal of this work is to help researchers and practitioners in this regard by proposing a survey and taxonomy of the available simulators modelling clouds, IoT and specifically fogs, which is the latest, currently still forming paradigm. The main contributions of this study are our novel viewpoints for classification including software quality, which is performed by analysing the source code of the considered simulators. We also propose comparison tables for three groups of simulators that reveal their differences and the way they model the elements of these systems. Finally, we discuss the relevant findings of our classifications, and highlight open issues that need further research.},
	keywords = {cloud computing, fog computing, Internet of Things, simulation, Survey, Taxonomy},
	issn = {1569-190X},
	doi = {https://doi.org/10.1016/j.simpat.2019.102042},
	url = {https://www.sciencedirect.com/science/article/pii/S1569190X1930173X},
	author = {Andr{\'a}s M{\'a}rkus and Attila Kert{\'e}sz}
}
