@article {Kharbouch2022,
	title = {Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation},
	journal = {Sensors},
	volume = {22},
	number = {20},
	year = {2022},
	note = {cited By 0},
	abstract = {In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO2) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants{\textquoteright} numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16\%, while maintaining good indoor air quality. {\textcopyright} 2022 by the authors.},
	keywords = {Air conditioning, Air pollution, Building ventilations, Carbon dioxide, Control approach, Control program, Data platform, energy efficiency, Energy utilization, Hardware in the loops, Hardware-in-the-loop simulation, Hardwarein-the-loop simulations (HIL), Indoor, Indoor air pollution, Internet of things, Learning algorithms, Long short-term memory, Machine learning algorithms, Machine-learning, MATLAB, Model predictive control, Model-predictive control, Predictive control, procedures, Simulation platform, Synthetic apertures, Ventilation},
	doi = {10.3390/s22207978},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140844555\&doi=10.3390\%2fs22207978\&partnerID=40\&md5=85b1a27cdc872a2b2f27b188c17282c8},
	author = {Kharbouch, A. and Berouine, A. and Elkhoukhi, H. and Berrabah, S. and Bakhouya, M. and El Ouadghiri, D. and Gaber, J.}
}
@conference {Fellir2020377,
	title = {A multi-Agent based model for task scheduling in cloud-fog computing platform},
	booktitle = {2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020},
	year = {2020},
	note = {cited By 9},
	pages = {377-382},
	abstract = {The Internet-of-Things (IoT) is a network of physical objects that communicate and exchange data between them over the Internet. These objects, or things, generate a massive amount of data that require powerful analytics approaches and data storage platforms - this is where cloud computing may well come into play. The classic cloud computing paradigm, however, faces several serious issues, due to centralization, such as low performance, latency issues, security issues, bandwidth obstacle, etc.. The Fog (Edge) computing paradigm has been proposed to improve the performance and to address other weaknesses, by providing IoT data processing and storage capabilities locally at the edges (IoT devices), instead of sending all the data to the centralized cloud. Whether in cloud or in fog computing, with billions of devices and a huge number of simultaneous requests, managing limited resources becomes essential. One of the important steps in resource management is task scheduling. In this paper, we consider task scheduling in a cloud-fog computing platform, we propose a multi-agent based model that aims at serving the most important task first, taking into consideration the task priority, its wait time, its status and the resources required to complete it successfully. Furthermore, in the scheduling process, we first propose an update to the priority value of the task, while taking into consideration its dependencies to other tasks and their priorities. Simulation results show that our proposed model can lead to better resource utilization and improve performance. {\textcopyright} 2020 IEEE.},
	keywords = {Autonomous agents, Computational methods, Computing paradigm, Computing platform, Data handling, Digital storage, Fog, Fog computing, Improve performance, Internet of thing (IOT), Internet of things, Multi agent systems, Multi-agent based modeling, Multitasking, Resource Management, Resource utilizations, Scheduling, Scheduling algorithms, Simulation platform, Storage capability},
	doi = {10.1109/ICIoT48696.2020.9089625},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085496418\&doi=10.1109\%2fICIoT48696.2020.9089625\&partnerID=40\&md5=4ee0e2ab76422fc76603ec3b6f6942cb},
	author = {Fellir, F. and El Attar, A. and Nafil, K. and Chung, L.}
}
