@article {Douch202269264,
	title = {Edge Computing Technology Enablers: A Systematic Lecture Study},
	journal = {IEEE Access},
	volume = {10},
	year = {2022},
	note = {cited By 1},
	pages = {69264-69302},
	abstract = {With the increasing stringent QoS constraints (e.g., latency, bandwidth, jitter) imposed by novel applications (e.g., e-Health, autonomous vehicles, smart cities, etc.), as well as the rapidly increasing number of connected IoT (Internet of Things) devices, the core network is becoming increasingly congested. To cope with those constraints, Edge Computing (EC) is emerging as an innovative computing paradigm that leverages Cloud computing and brings it closer to the customer. {\textquoteright}EC{\textquoteright} refers to transferring computing power and intelligence from the central Cloud to the network{\textquoteright}s Edge. With that, EC promotes the idea of processing and caching data at the Edge, thus reducing network congestion and latency. This paper presents a detailed, thorough, and well-structured assessment of Edge Computing and its enabling technologies. Initially, we start by defining EC from the ground up, outlining its architectures and evolution from Cloudlets to Multi-Access Edge Computing. Next, we survey recent studies on the main cornerstones of an EC system, including resource management, computation offloading, data management, network management, etc. Besides, we emphasized EC technology enablers, starting with Edge Intelligence, the branch of Artificial Intelligence (AI) that integrates AI models at resource-constrained edge nodes with significant heterogeneity and mobility. Then, moving on to 5G and its empowering technologies, we explored how EC and 5G complement each other. After that, we studied virtualization and containerization as promising hosting runtime for edge applications. Further to that, we delineated a variety of EC use-case scenarios, e.g., smart cities, e-Health, military applications, etc. Finally, we concluded our survey by highlighting the role of EC integration with future concerns regarding green energy and standardization. {\textcopyright} 2013 IEEE.},
	keywords = {5g, 5g mobile communication, 5G mobile communication systems, Cloud-computing, computation offloading, Containerization, Containers, Data handling, Edge computing, Edge intelligence, Fog computing, Information management, Internet of things, License, Military applications, Mobile communications, Multi-access edge computing, Multiaccess, Quality of service, Quality-of-service, Surveys},
	doi = {10.1109/ACCESS.2022.3183634},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132750947\&doi=10.1109\%2fACCESS.2022.3183634\&partnerID=40\&md5=88667ef450dd686759983878427de051},
	author = {Douch, S. and Abid, M.R. and Zine-Dine, K. and Bouzidi, D. and Benhaddou, D.}
}
@article {Elmouatamid2021571,
	title = {A MicroGrid System Infrastructure Implementing IoT/Big-Data Technologies for Efficient Energy Management in Buildings},
	journal = {Green Energy and Technology},
	year = {2021},
	note = {cited By 4},
	pages = {571-600},
	abstract = {Recent studies showed that energy consumption in buildings could be efficiently reduced by including recent IoT (Internet of Things) and Big-Data technologies into microgrid systems. In fact, three major aspects could be further considered for reducing energy consumption while maintaining a suitable occupants{\textquoteright} comfort, (i) integrating renewable energy sources and storage devices, (ii) integrating programmable and less-energy-consuming equipment, and (iii) deploying innovative information and communication technologies. These aspects might contribute substantially to the improvement of winning and saving energy toward smart and energy-efficient buildings. In this chapter, a microgrid system infrastructure is developed together with a platform for data gathering, monitoring, and processing. We put more emphasis on microgrid systems as crucial infrastructures for leveraging energy-efficient and smart buildings by developing and deploying a holistic IoT/Big-Data platform in which sensing and actuation tasks are performed according to the actual contextual changes. Scenarios are presented in order to show the usefulness of this holistic platform for monitoring, data processing, and control in energy-efficient buildings. {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Data gathering, Data handling, Data technologies, energy efficiency, Energy efficient, Energy efficient building, Energy utilization, Information and Communication Technologies, Intelligent buildings, Internet of things, Micro-grid systems, Microgrids, Monitoring, Reducing energy consumption, Renewable energy resources, Renewable energy source, Virtual storage},
	doi = {10.1007/978-3-030-64565-6_20},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105764631\&doi=10.1007\%2f978-3-030-64565-6_20\&partnerID=40\&md5=b0a762b2a1e37fc93eff19c16f955daa},
	author = {Elmouatamid, A. and NaitMalek, Y. and Ouladsine, R. and Bakhouya, M. and El kamoun, N. and Khaidar, M. and Zine-Dine, K.}
}
@article {Boulmrharj2020,
	title = {Online battery state-of-charge estimation methods in micro-grid systems},
	journal = {Journal of Energy Storage},
	volume = {30},
	year = {2020},
	note = {cited By 30},
	abstract = {Batteries have shown great potential for being integrated in Micro-Grid (MG) systems. However, their integration is not a trivial task and, therefore, requires extensive modeling and simulations in order to efficiently estimating their State-of-Charge (SoC) within MG systems. The work presented in this article focuses mainly on battery modeling for SoC estimation. We put more emphasize on battery system{\textquoteright}s characterization, modeling, SoC estimation and its integration into MG systems in order to study its performance. In fact, an instrumentation platform, composed of recent and low cost sensing and actuating equipment, is developed in order to identify the battery{\textquoteright}s parameters and then build the battery model. The simulation results show good agreement with the experimental results, and thus the battery model is validated in both charging and discharging processes. Then, the battery{\textquoteright}s SoC estimation in both charging and discharging processes is investigated by means of sophisticated methods, such as, Artificial Neural Network, Luenberger observer and Kalman Filter combined with Coulomb counting. The results of these methods are reported and compared to the SoC estimated using the Coulomb counting method in order to end up with the precise method. The modeled battery is afterwards integrated into an MG system, which is deployed into our EEBLab (Energy Efficient Building Laboratory) test-site, for simulation and experimental investigations. Finally, the four algorithms for SoC estimation are included into the instrumentation platform, which is connected to a Lead-acid battery already integrated into the MG system, in order to show and compare their performances and accuracy for online battery{\textquoteright}s SoC estimation. {\textcopyright} 2020 Elsevier Ltd},
	keywords = {Battery management systems, Battery state of charge, Charging (batteries), Coulomb counting method, Discharging process, energy efficiency, Energy efficient building, Experimental investigations, Intelligent buildings, Lead acid batteries, Luenberger observers, Model and simulation, Neural networks, Online systems, Sensing and actuating},
	doi = {10.1016/j.est.2020.101518},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085273724\&doi=10.1016\%2fj.est.2020.101518\&partnerID=40\&md5=f5d31d6e8a0f90e2bec4e1dd9d7da9d8},
	author = {Boulmrharj, S. and Ouladsine, R. and NaitMalek, Y. and Bakhouya, M. and Zine-Dine, K. and Khaidar, M. and Siniti, M.}
}
@conference {Boulmrharj201856,
	title = {Approach for dimensioning stand-alone photovoltaic systems},
	booktitle = {Energy Procedia},
	volume = {153},
	year = {2018},
	pages = {56-61},
	doi = {10.1016/j.egypro.2018.10.058},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057433465\&doi=10.1016\%2fj.egypro.2018.10.058\&partnerID=40\&md5=b03f45460b45700ebfe92eb2f1b70e53},
	author = {Boulmrharj, S. and NaitMalek, Y. and Elmouatamid, A. and Bakhouya, M. and Ouladsine, R. and Zine-Dine, K. and Khaidar, M. and Abid, R.}
}
@conference {Boulmrharj2018,
	title = {Towards a battery characterization methodology for performance evaluation of micro-grid systems},
	booktitle = {2018 International Conference on Smart Energy Systems and Technologies, SEST 2018 - Proceedings},
	year = {2018},
	doi = {10.1109/SEST.2018.8495829},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056492547\&doi=10.1109\%2fSEST.2018.8495829\&partnerID=40\&md5=f25a82d795aa6991c23f1064013cde45},
	author = {Boulmrharj, S. and NaitMalek, Y. and Mouatamid, A.E. and Ouladsine, R. and Bakhouya, M. and Ouldmoussa, M. and Zine-Dine, K. and Khaidar, M. and Abid, R.}
}
@conference {Elmouatamid2018984,
	title = {Towards a Demand/Response Control Approach for Micro-grid Systems},
	booktitle = {2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018},
	year = {2018},
	pages = {984-988},
	doi = {10.1109/CoDIT.2018.8394951},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050227456\&doi=10.1109\%2fCoDIT.2018.8394951\&partnerID=40\&md5=97a6b224a481de8a0d5d91a46b76b895},
	author = {Elmouatamid, A. and NaitMalek, Y. and Ouladsine, R. and Bakhouya, M. and Elkamoun, N. and Zine-Dine, K. and Khaidar, M. and Abid, R.}
}
