@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 {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}
}
@article {Herczeg:2009:ESE:1664750.1664756,
	title = {Energy simulation of embedded XScale systems with XEEMU},
	journal = {J. Embedded Comput.},
	volume = {3},
	year = {2009},
	month = {August},
	pages = {209{\textendash}219},
	publisher = {IOS Press},
	address = {Amsterdam, The Netherlands, The Netherlands},
	keywords = {DVFS, energy consumption, opensource, power dissipation, SDRAM, simulation, XScale},
	issn = {1740-4460},
	url = {http://dl.acm.org/citation.cfm?id=1664750.1664756},
	author = {Z. Herczeg and D. Schmidt and {\'A}kos Kiss and N. Wehn and Tibor Gyim{\'o}thy}
}
