@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}
}
