@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 {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}
}
@article {16783,
	title = {The Use of MQTT in M2M and IoT Systems: A Survey},
	journal = {IEEE Access},
	volume = {vol 8},
	year = {2020},
	pages = {201071-201086},
	abstract = {Nowadays billions of smart devices or things are present in Internet of Things (IoT) environments, such as homes, hospitals, factories, and vehicles, all around the world. As a result, the number of interconnected devices is continuously and rapidly growing. These devices communicate with each other and with other services using various communication protocols for the transportation of sensor or event data. These protocols enable applications to collect, store, process, describe, and analyze data to solve a variety of problems. IoT also aims to provide secure, bi-directional communication between interconnected devices, such as sensors, actuators, microcontrollers or smart appliances, and corresponding cloud services. In this paper we analyze the growth of M2M protocol research (MQTT, AMQP, and CoAP) over the past 20 years, and show how the growth in MQTT research stands out from the rest. We also gather relevant application areas of MQTT, as the most widespread M2M/IoT protocol, by performing a detailed literature search in major digital research archives. Our quantitative evaluation presents some of the important MQTT-related studies published in the past five years, which we compare to discuss the main features, advantages, and limitations of the MQTT protocol. We also propose a taxonomy to compare the properties and features of various MQTT implementations, i.e. brokers and libraries currently available in the public domain to help researchers and end-users to efficiently choose a broker or client library based on their requirements. Finally, we discuss the relevant findings of our comparison and highlight open issues that need further research and attention.},
	keywords = {cloud computing, Internet of Things, IoT, IoT protocols, ISO standards, Machine-to-machine communications, MQTT, MQTT brokers, Protocols, Quality of service, Reliability, Survey},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2020.3035849},
	url = {https://ieeexplore.ieee.org/document/9247996},
	author = {Mishra, Biswajeeban and Kert{\'e}sz, Attila}
}
