@conference {Abouaomar2021,
	title = {Mean-Field Game and Reinforcement Learning MEC Resource Provisioning for SFC},
	booktitle = {2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings},
	year = {2021},
	note = {cited By 0},
	abstract = {In this paper, we address the resource provisioning problem for service function chaining (SFC) in terms of the placement and chaining of virtual network functions (VNFs) within a multi-access edge computing (MEC) infrastructure to reduce service delay. We consider the VNFs as the main entities of the system and propose a mean-field game (MFG) framework to model their behavior for their placement and chaining. Then, to achieve the optimal resource provisioning policy without considering the system control parameters, we reduce the proposed MFG to a Markov decision process (MDP). In this way, we leverage reinforcement learning with an actor-critic approach for MEC nodes to learn complex placement and chaining policies. Simulation results show that our proposed approach outperforms benchmark state-of-the-art approaches. {\textcopyright} 2021 IEEE.},
	keywords = {Computer games, Computing infrastructures, Computing resource, e-learning, Edge computing, Markov processes, Mean-field games, Multi-access edge computing, Multiaccess, Network function virtualization, Reinforcement learning, Reinforcement learnings, Resource provisioning, Service function chaining, Service functions, Transfer functions, Virtual addresses, Virtual reality},
	doi = {10.1109/GLOBECOM46510.2021.9685236},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124062107\&doi=10.1109\%2fGLOBECOM46510.2021.9685236\&partnerID=40\&md5=8319d7756843c2ccadb8f9de4bec7eff},
	author = {Abouaomar, A. and Cherkaoui, S. and Mlika, Z. and Kobbane, A.}
}
@conference {Ahajjam2021,
	title = {On Multi-Label Classification for Non-Intrusive Load Identification using Low Sampling Frequency Datasets},
	booktitle = {Conference Record - IEEE Instrumentation and Measurement Technology Conference},
	volume = {2021-May},
	year = {2021},
	note = {cited By 0},
	abstract = {Non-intrusive load monitoring (NILM) aims to infer information about the electric consumption of individual loads using the premises{\textquoteright} aggregate consumption. In this work, we target supervised multi-label classification for non-intrusive load identification. We describe how we have created a new dataset from Moroccan households using a low sampling frequency. Then, we analyze the performance of three machine learning models for NILM, and investigate the impact of signal input length on performance. {\textcopyright} 2021 IEEE.},
	keywords = {Aggregate consumption, Classification (of information), Electric consumption, Load identification, Machine learning models, Measurement, Multi label classification, Non-intrusive, Nonintrusive load monitoring, Sampling frequencies},
	doi = {10.1109/I2MTC50364.2021.9460059},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113709883\&doi=10.1109\%2fI2MTC50364.2021.9460059\&partnerID=40\&md5=2f6ca1a0b8790dce2a085d0e0ff10cf4},
	author = {Ahajjam, M.A. and Essayeh, C. and Ghogho, M. and Kobbane, A.}
}
@article {Gryech2020379,
	title = {Machine learning for air quality prediction using meteorological and traffic related features},
	journal = {Journal of Ambient Intelligence and Smart Environments},
	volume = {12},
	number = {5},
	year = {2020},
	note = {cited By 4},
	pages = {379-391},
	abstract = {The presence of pollutants in the air has a direct impact on our health and causes detrimental changes to our environment. Air quality monitoring is therefore of paramount importance. The high cost of the acquisition and maintenance of accurate air quality stations implies that only a small number of these stations can be deployed in a country. To improve the spatial resolution of the air monitoring process, an interesting idea is to develop data-driven models to predict air quality based on readily available data. In this paper, we investigate the correlations between air pollutants concentrations and meteorological and road traffic data. Using machine learning, regression models are developed to predict pollutants concentration. Both linear and non-linear models are investigated in this paper. It is shown that non-linear models, namely Random Forest (RF) and Support Vector Regression (SVR), better describe the impact of traffic flows and meteorology on the concentrations of pollutants in the atmosphere. It is also shown that more accurate prediction models can be obtained when including some pollutants{\textquoteright} concentration as predictors. This may be used to infer the concentrations of some pollutants using those of other pollutants, thereby reducing the number of air pollution sensors. {\textcopyright} 2020 - IOS Press and the authors. All rights reserved.},
	keywords = {Accurate prediction, Air quality, Air quality monitoring, Air quality prediction, Data-driven model, Decision trees, Forecasting, Machine learning, Non-linear model, Nonlinear systems, Pollution sensors, Predictive analytics, Spatial resolution, Support vector regression, Support vector regression (SVR)},
	doi = {10.3233/AIS-200572},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093821240\&doi=10.3233\%2fAIS-200572\&partnerID=40\&md5=f8b7d726f054f01a2be432560d359fc5},
	author = {Gryech, I. and Ghogho, M. and Elhammouti, H. and Sbihi, N. and Kobbane, A.}
}
@article {Lhazmir2020973,
	title = {Matching Game with No-Regret Learning for IoT Energy-Efficient Associations with UAV},
	journal = {IEEE Transactions on Green Communications and Networking},
	volume = {4},
	number = {4},
	year = {2020},
	note = {cited By 4},
	pages = {973-981},
	abstract = {Unmanned aerial vehicles (UAVs) are a promising technology to provide an energy-efficient and cost-effective solution for data collection from ground Internet of Things (IoT) network. In this paper, we analyze the UAV-IoT device associations that provide reliable connections with low communication power and load balance the traffic using analytical techniques from game theory. In particular, to maximize the IoT devices{\textquoteright} benefits, a novel framework is proposed to assign them the most suitable UAVs. We formulate the problem as a distributed algorithm that combines notions from matching theory and no-regret learning. First, we develop a many-to-one matching game where UAVs and IoT devices are the players. In this subgame, the players rank one another based on individual utility functions that capture their needs. Each IoT device aims to minimize its transmitting energy while meeting its signal-to-interference-plus-noise-ratio (SINR) requirements, and each UAV seeks to maximize the number of served IoT devices while respecting its energy constraints. Second, a non-cooperative game based on no-regret learning is used to determine each IoT device{\textquoteright}s regret. Then, UAVs open a window for transfers to the IoT devices. Simulation results show that the proposed approach provides a low average total transmit power, ensures fast data transmission and optimal utilization of the UAVs{\textquoteright} bandwidth. {\textcopyright} 2017 IEEE.},
	keywords = {Antennas, Cost effectiveness, Cost-effective solutions, energy efficiency, Fast data transmission, Game theory, Internet of things, Internet of Things (IOT), Noncooperative game, Optimal utilization, Reliable connections, Signal interference, Signal to interference plus noise ratio, Signal to noise ratio, Total transmit power, Unmanned aerial vehicles (UAV)},
	doi = {10.1109/TGCN.2020.3008992},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096697293\&doi=10.1109\%2fTGCN.2020.3008992\&partnerID=40\&md5=7f2e844f54122845029dfd8503c5eaf6},
	author = {Lhazmir, S. and Oualhaj, O.A. and Kobbane, A. and Ben-Othman, J.}
}
@article {Gryech2020,
	title = {Moreair: A low-cost urban air pollution monitoring system},
	journal = {Sensors (Switzerland)},
	volume = {20},
	number = {4},
	year = {2020},
	note = {cited By 25},
	abstract = {MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children{\textquoteright}s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution. {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {Agile deployments, Agile manufacturing systems, Air quality, Costs, Decision trees, Geographic information systems, Information systems, Information use, Learning systems, Machine learning, Mobile sensing, Monitoring, Particles (particulate matter), Particulate Matter, Pollution detection, Pollution monitoring, Random forests, Real-time information, Sensor deployment, Urban air pollution, Urban pollutions},
	doi = {10.3390/s20040998},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079609813\&doi=10.3390\%2fs20040998\&partnerID=40\&md5=dc90b4357fcdc64d82d12f57a45f971f},
	author = {Gryech, I. and Ben-Aboud, Y. and Guermah, B. and Sbihi, N. and Ghogho, M. and Kobbane, A.}
}
@article {Ahajjam2020,
	title = {Mored: A moroccan buildings{\textquoteright} electricity consumption dataset},
	journal = {Energies},
	volume = {13},
	number = {24},
	year = {2020},
	note = {cited By 6},
	abstract = {This paper consists of two parts: An overview of existing open datasets of electricity consumption and a description of the Moroccan Buildings{\textquoteright} Electricity Consumption Dataset, a first of its kind, coined as MORED. The new dataset comprises electricity consumption data of various Moroccan premises. Unlike existing datasets, MORED provides three main data components: Whole premises (WP) electricity consumption, individual load (IL) ground-truth consumption, and fully labeled IL signatures, from affluent and disadvantaged neighborhoods. The WP consumption data were acquired at low rates (1/5 or 1/10 samples/s) from 12 households; the IL ground-truth data were acquired at similar rates from five households for extended durations; and IL signature data were acquired at high and low rates (50 k and 4 samples/s) from 37 different residential and industrial loads. In addition, the dataset encompasses non-intrusive load monitoring (NILM) metadata. {\textcopyright} 2020 by the authors.},
	keywords = {Data components, Electric load management, Electric power utilization, Electricity-consumption, Ground truth, Ground truth data, Industrial loads, Low rates, Nonintrusive load monitoring, Signature data},
	doi = {10.3390/en13246737},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106664615\&doi=10.3390\%2fen13246737\&partnerID=40\&md5=2d7b881b6d24a1d21b672b26fa9ee62f},
	author = {Ahajjam, M.A. and Licea, D.B. and Essayeh, C. and Ghogho, M. and Kobbane, A.}
}
@article {Filali2020197017,
	title = {Multi-access edge computing: A survey},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	note = {cited By 49},
	pages = {197017-197046},
	abstract = {Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks{\textquoteright} architecture as well as the transition mechanisms to migrate into a standard 5G network architecture.We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture. {\textcopyright} 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.},
	keywords = {5G mobile communication systems, Architectural frameworks, Bandwidth, Close proximity, Edge computing, Network function virtualization, Network slicing, Networks and systems, Optimized resources, QoS parameters, State of the art, Transition mechanism},
	doi = {10.1109/ACCESS.2020.3034136},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099901543\&doi=10.1109\%2fACCESS.2020.3034136\&partnerID=40\&md5=bc99db081ebbbf5eb6d188c020bb33a2},
	author = {Filali, A. and Abouaomar, A. and Cherkaoui, S. and Kobbane, A. and Guizani, M.}
}
@conference {Abouaomar2019,
	title = {Matching-Game for User-Fog Assignment},
	booktitle = {2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings},
	year = {2019},
	note = {cited By 0},
	doi = {10.1109/GLOCOM.2018.8647545},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063543941\&doi=10.1109\%2fGLOCOM.2018.8647545\&partnerID=40\&md5=2e86072aa15d3175de06d25bff61c79f},
	author = {Abouaomar, A. and Kobbane, A. and Cherkaoui, S.}
}
@conference {Assila20181,
	title = {A Many-To-One Matching Game Approach to Achieve Low-Latency Exploiting Fogs and Caching},
	booktitle = {2018 9th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2018 - Proceedings},
	volume = {2018-January},
	year = {2018},
	pages = {1-2},
	doi = {10.1109/NTMS.2018.8328671},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051102582\&doi=10.1109\%2fNTMS.2018.8328671\&partnerID=40\&md5=93f51b2e077f7f00d3d0e5e5a8123950},
	author = {Assila, B. and Kobbane, A. and El Koutbi, M.}
}
@article {AitOualhaj2018,
	title = {Mobile delay-tolerant networks with energy-harvesting and wireless energy transfer cooperation},
	journal = {Concurrency Computation},
	year = {2018},
	doi = {10.1002/cpe.5112},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059067813\&doi=10.1002\%2fcpe.5112\&partnerID=40\&md5=4d5955405a79b55e86781f7a65a66687},
	author = {Ait Oualhaj, O. and Kobbane, A. and Ben-Othman, J.}
}
@conference {Amine20171245,
	title = {A many-to-many matching game in ultra-dense LTE HetNets},
	booktitle = {2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017},
	year = {2017},
	note = {cited By 0},
	pages = {1245-1250},
	abstract = {In this work, we focus our study to improving the energy efficiency of mobile cellular users in ultra-dense LTE HetNets. The hyper-dense co-channel deployment of indoor LTE small cell networks (SCNs) within LTE macro cell networks (MCNs) will aggravate the effect of cross-tier interferences caused by the uplink transmissions of macro-indoor users located inside the overlapping zones of small base station (SBS) coverage areas. Hence, degrading the uplink performance at the level of SBSs adopting closed access policy. In order to eliminate the severe cross-tier interferences, each SBS attempts to open the access for macro-indoor users that accept only the SBS with Max-SINR offer. This will lead to network congestion problems in several SCNs. Wherefore, we formulate our problem as a many-to-many matching game. Then, we introduce an algorithm that computes the optimal many-to-many stable matching which consist of assigning each macro-indoor user with multi-homing capabilities to the most suitable set of SBSs and vice versa based on their preference profiles. With regard to the conventional Max-SINR association scheme, our solution can effectively improve the energy efficiency of cellular users. Moreover, it can ensure load balancing in ultra-dense LTE HetNets. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/IWCMC.2017.7986463},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027886532\&doi=10.1109\%2fIWCMC.2017.7986463\&partnerID=40\&md5=a4a95449b5ea20b54454dcf7b7bd1e59},
	author = {Amine, M. and Walid, A. and Kobbane, A. and Cherkaoui, S.}
}
@conference {Azougaghe2017,
	title = {Many-to-one matching game towards secure virtual machines migration in cloud computing},
	booktitle = {2016 International Conference on Advanced Communication Systems and Information Security, ACOSIS 2016 - Proceedings},
	year = {2017},
	note = {cited By 0},
	abstract = {Virtual machine migration represents a new topic of research last years. In this context, we present a new system based on matching game theory to design efficient and practical migration algorithms that work well with huge numbers of VMs and servers. This strategy solves VM migration, resources managements and load balancing problems in cloud computing environment. The experimental results show that our algorithm not only obtains multi-resource load balancing performance but also improves the cloud security services and gets a gain of resources consummation on all servers. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/ACOSIS.2016.7843922},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015191060\&doi=10.1109\%2fACOSIS.2016.7843922\&partnerID=40\&md5=6b59b3295f8598830c0771717fb9a86b},
	author = {Azougaghe, A. and Oualhaj, O.A. and Hedabou, M. and Belkasmi, M. and Kobbane, A.}
}
@article {Harbouche20171339,
	title = {Model driven flexible design of a wireless body sensor network for health monitoring},
	journal = {Computer Networks},
	volume = {129},
	year = {2017},
	note = {cited By 1},
	pages = {1339-1351},
	abstract = {The Wireless Body Sensor Network (WBSN) is a wireless network that is designed to allow communication among sensor nodes that are attached to a human body to monitor the body{\textquoteright}s vital parameters and environment. The design and development of such WBSN systems for health monitoring have received a large amount of attention recently, in research studies and in industry. This attention is mainly motivated by costly health care and by recent advances in the development of miniature health monitoring devices as well as emerging technologies, such as the Internet of Things (IoT), which contribute to the main challenges of 5G. The existence of an explicit approach to address the required software design and verification should be very beneficial for the construction and maintenance of such systems. This paper presents a preventive health care system that has a flexible design. The proposed system is based on an architecture that has heterogeneous nodes and provides both daily continuous monitoring as well as specific controls. We defined a model to describe the WBSN{\textquoteright}s global behavior. An important aspect of this work is that we propose a model-driven engineering (MDE) approach to address the derivation of each node{\textquoteright}s behavior in the WBSN from the WBSN global behavior. This approach allows developers to obtain a system design from the global specification of its requirement. To ensure the conformance of this design to its specification, the derived behaviors should be validated and verified before their deployment. In fact, formal methods are powerful tools for software engineers to verify the logical correctness of concurrent software at different levels of its life cycle. Model checking is one of the most powerful formal methods for verifying the logical correctness of such concurrent systems. In this work, we make use of a model checking approach that is based on a model transformation to validate the automatically derived behavior of a WBSN for health monitoring. This model-driven approach will check whether the derived system behaves correctly according to its global specification, while the objective is to increase the system{\textquoteright}s performance and QoS. This approach allows the developer to reason about a model of the global system rather than about the system itself. {\textcopyright} 2017 Elsevier B.V.},
	doi = {10.1016/j.comnet.2017.06.014},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022027303\&doi=10.1016\%2fj.comnet.2017.06.014\&partnerID=40\&md5=3d665e7ff240e8a6d9564857abf695da},
	author = {Harbouche, A. and Djedi, N. and Erradi, M. and Ben-Othman, J. and Kobbane, A.}
}
@conference {Amine2016133,
	title = {Matching game for green uplink in hyper dense LTE HeTNets},
	booktitle = {Proceedings - 2016 International Conference on Wireless Networks and Mobile Communications, WINCOM 2016: Green Communications and Networking},
	year = {2016},
	note = {cited By 0},
	pages = {133-139},
	abstract = {In this paper, we aim to improve the energy efficiency of cellular users located in hyper-dense co-channel deployments of LTE small cell Networks (SCNs), randomly distributed within LTE macro cell networks (MCNs). Avoiding the severe cross-Tier interferences at the small base stations (SBSs) levels caused by the uplink transmissions between the macro indoor users (which are inside the SBS coverage area) and the macro base station (MBS), ensuring load balancing, and improving energy efficiency are critical technical challenges in hyper-dense co-channel LTE SCNs deployments. As a solution, we formulate our problem as a matching game, then we propose the deferred acceptance algorithm to compute the optimal stable matching consisting of assigning each macro indoor user to the most suitable SBS and vice versa. Simulation results validate our solution, and show how it can effectively improve the energy efficiency of cellular users in hyper-dense LTE HetNets compared to the default Max-SINR association scheme. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/WINCOM.2016.7777204},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010465149\&doi=10.1109\%2fWINCOM.2016.7777204\&partnerID=40\&md5=cfdd0315adeb3a468a5382cae7b1ed26},
	author = {Amine, M. and Walid, A. and Oualhaj, O.A. and Kobbane, A.}
}
