@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}
}
@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}
}
