@article {Filali20222174,
	title = {Dynamic SDN-Based Radio Access Network Slicing With Deep Reinforcement Learning for URLLC and eMBB Services},
	journal = {IEEE Transactions on Network Science and Engineering},
	volume = {9},
	number = {4},
	year = {2022},
	note = {cited By 3},
	pages = {2174-2187},
	abstract = {Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to optimize the performance of URLLC and eMBB services. In a large time-scale, an SDN controller allocates radio resources to gNodeBs according to the requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB allocates its available resources to its end-users and requests, if needed, additional resources from adjacent gNodeBs. We formulate this problem as a non-linear binary program and prove its NP-hardness. Next, for each time-scale, we model the problem as a Markov decision process (MDP), where the large-time scale is modeled as a single agent MDP whereas the shorter time-scale is modeled as a multi-agent MDP. We leverage the exponential-weight algorithm for exploration and exploitation (EXP3) to solve the single-agent MDP of the large time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the multi-agent MDP of the short time-scale resource allocation. Extensive simulations show that our approach is efficient under different network parameters configuration and it outperforms recent benchmark solutions. {\textcopyright} 2013 IEEE.},
	keywords = {5G mobile communication systems, Deep learning, EMBB, Heuristic algorithms, Heuristics algorithm, Learning algorithms, Low-latency communication, Markov processes, Multi agent systems, Network slicing, Optimisations, Optimization, Quality of service, Quality-of-service, Radio access networks, Reinforcement learning, Resource allocation, Resource Management, Software agents, Software defined networking, Software-defined networkings, Time measurement, Ultra reliable low latency communication, URLLC},
	doi = {10.1109/TNSE.2022.3157274},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126273319\&doi=10.1109\%2fTNSE.2022.3157274\&partnerID=40\&md5=44d6a41c3d7b8b6d8c4d6918856f36c5},
	author = {Filali, A. and Mlika, Z. and Cherkaoui, S. and Kobbane, A.}
}
@conference {Abouaomar2021273,
	title = {A deep reinforcement learning approach for service migration in MEC-enabled vehicular networks},
	booktitle = {Proceedings - Conference on Local Computer Networks, LCN},
	volume = {2021-October},
	year = {2021},
	note = {cited By 4},
	pages = {273-280},
	abstract = {Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in a MEC-enabled vehicular network in order to minimize the total service latency and migration cost. This problem is formulated as a nonlinear integer program and is linearized to help obtaining the optimal solution using off-the-shelf solvers. Then, to obtain an efficient solution, it is modeled as a multi-agent Markov decision process and solved by leveraging deep Q learning (DQL) algorithm. The proposed DQL scheme performs a proactive services migration while ensuring their continuity under high mobility constraints. Finally, simulations results show that the proposed DQL scheme achieves close-to-optimal performance. {\textcopyright} 2021 IEEE.},
	keywords = {Deep learning, Edge computing, Infotainment, Integer programming, Learning schemes, Markov processes, Multi agent systems, Multi-access edge computing, Multiaccess, Q-learning, Quality of service, Reinforcement learning, Reinforcement learning approach, Service migration, Vehicle mobility, Vehicular networks},
	doi = {10.1109/LCN52139.2021.9524882},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118466383\&doi=10.1109\%2fLCN52139.2021.9524882\&partnerID=40\&md5=8e35dfee04f347da3f757f57f5d575c8},
	author = {Abouaomar, A. and Mlika, Z. and Filali, A. and Cherkaoui, S. and Kobbane, A.}
}
@article {Lhazmir2020,
	title = {A decision-making analysis in UAV-enabled wireless power transfer for IoT networks},
	journal = {Simulation Modelling Practice and Theory},
	volume = {103},
	year = {2020},
	note = {cited By 8},
	abstract = {We consider an IoT network with energy-harvesting capabilities. To extend the network lifetime, we propose a novel unmanned aerial vehicle (UAV)- enabled wireless power transfer (WPT) system, where UAVs move among IoT devices and act as data aggregators and wireless power providers. This paper addresses the decision-making problem since the limited buffer and energy resources constrain all nodes. Each IoT node must decide on whether to request a data transmission, to ask for a wireless energy transfer or to abstain and not take any action. When a UAV receives a request from an IoT device, either for data reception or wireless energy transmission, it has to accept or decline. In this paper, we aim to find a proper packet delivery and energy transfer policy according to the system state that maximizes the data transmission efficiency of the system. We first formulate the problem as a Markov Decision Process (MDP) to tackle the successive decision issues, to optimize a utility for each node upon a casual environment. As the MDP formalism achieves its limits when the interactions between different nodes are considered, we formulate the problem as a Graph-based MDP (GMDP). The transition functions and rewards are then decomposed into local functions, and a graph illustrates the dependency{\textquoteright} relations among the nodes. To obtain the optimal policy despite the system{\textquoteright}s variations, Mean-Field Approximation (MFA) and Approximate linear-programming (ALP) algorithms were proposed to solve the GMDP problem. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {Antennas, Approximate linear programming, Approximation algorithms, Behavioral research, Data communication systems, Data transfer, Data transmission efficiency, Decision making, Decision making analysis, Decision-making problem, Energy harvesting, Energy resources, Energy transfer, Graph theory, Graphic methods, Inductive power transmission, Internet of things, Linear programming, Markov Decision Processes, Markov processes, Mean field approximation, Unmanned aerial vehicles (UAV), Wireless energy transfers, Wireless power transfer (WPT)},
	doi = {10.1016/j.simpat.2020.102102},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084066274\&doi=10.1016\%2fj.simpat.2020.102102\&partnerID=40\&md5=6a1c9e107244a170a782e61d0f9755cc},
	author = {Lhazmir, S. and Oualhaj, O.A. and Kobbane, A. and Mokdad, L.}
}
@conference {Oualahj2018,
	title = {A Decentralized Control of Autonomous Delay Tolerant Networks: Multi Agents Markov Decision Processes Framework},
	booktitle = {IEEE International Conference on Communications},
	volume = {2018-May},
	year = {2018},
	doi = {10.1109/ICC.2018.8422718},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051430605\&doi=10.1109\%2fICC.2018.8422718\&partnerID=40\&md5=ea20d47bd369c11fe0555f1224db09f3},
	author = {Oualahj, O.A. and Kobbane, A. and Ben-Othman, J.}
}
@conference {Boudagdigue2018,
	title = {A Distributed Advanced Analytical Trust Model for IoT},
	booktitle = {IEEE International Conference on Communications},
	volume = {2018-May},
	year = {2018},
	doi = {10.1109/ICC.2018.8422726},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051430306\&doi=10.1109\%2fICC.2018.8422726\&partnerID=40\&md5=8907401b5d112341154ed5ed64157168},
	author = {Boudagdigue, C. and Benslimane, A. and Kobbane, A. and Elmachkour, M.}
}
@conference {Harbouche2017,
	title = {Dynamic coalitional matching game approach for fair and swift data-gathering in wireless body sensor networks},
	booktitle = {Proceedings - 2017 International Conference on Wireless Networks and Mobile Communications, WINCOM 2017},
	year = {2017},
	note = {cited By 0},
	abstract = {Wireless Sensor Networks are deployed in different fields of application to gather data on the monitored environment. The Wireless Body Sensor Network (WBSN) is a wireless sensor network designed to monitor a human body vital and environment parameters. The design and development of such WBSN systems for health monitoring have been motivated by costly healthcare and propelled by the development of miniature health monitoring devices. This paper presents the architecture design of a preventive health care monitoring system. This architecture is designed for monitoring multiple patients in a hospital. It is based on a set of mobile data collectors and static sensors for analysis of various patient{\textquoteright}s parameters. The data collectors need to cooperate together in order to gather the data from the sensor nodes. The point of this paper is how to dynamically and effectively appoint and deploy several data collectors in the hospital to gather the measured data in minimal time. We formulate the problem as a coalitional matching game between patients and data collectors, and we propose a patientdata collector association algorithm that ensures fairness and minimum total course in the stable matchings. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/WINCOM.2017.8238179},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041451720\&doi=10.1109\%2fWINCOM.2017.8238179\&partnerID=40\&md5=44ef24b18e66b8fb24573d73db99025f},
	author = {Harbouche, A. and Elmachkour, M. and Djedi, N. and Erradi, M. and Kobbane, A.}
}
@article {Koulali2012,
	title = {Dynamic power control for energy harvesting wireless multimedia sensor networks},
	journal = {Eurasip Journal on Wireless Communications and Networking},
	volume = {2012},
	year = {2012},
	note = {cited By 3},
	doi = {10.1186/1687-1499-2012-158},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872869198\&doi=10.1186\%2f1687-1499-2012-158\&partnerID=40\&md5=2b0c34f9aac01ae9f865a22159e62779},
	author = {Koulali, M.-A. and Kobbane, A. and Koutbi, M.E. and Tembine, H. and Ben-Othman, J.}
}
@conference {Kobbane2012518,
	title = {Dynamic power control with energy constraint for Multimedia Wireless Sensor Networks},
	booktitle = {IEEE International Conference on Communications},
	year = {2012},
	note = {cited By 2},
	pages = {518-522},
	doi = {10.1109/ICC.2012.6363971},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84871981947\&doi=10.1109\%2fICC.2012.6363971\&partnerID=40\&md5=2e71bfe8b840a119435a1fb89dd122f0},
	author = {Kobbane, A. and Koulali, M.-A. and Tembine, H. and Koutbi, M.E. and Ben-Othman, J.}
}
@conference { ISI:000312855700097,
	title = {Dynamic Power Control with Energy Constraint for Multimedia Wireless Sensor Networks},
	booktitle = {2012 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)},
	series = {IEEE International Conference on Communications},
	year = {2012},
	note = {IEEE International Conference on Communications (ICC), Ottawa, CANADA, JUN 10-15, 2012},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In recent years, many approaches and techniques have been explored for the optimization of energy usage in Wireless Sensor Networks (WSN). It is well recognized that a proper energy consumption model is the foundation for developing and evaluating a power management scheme in WSN. In this paper, we propose a new complete information Markov Decision Process (MDP) model to characterize sensors energy levels. We also propose and compare several centralized power control policies to select the more efficient policy that optimizes throughput and energy consumption.},
	isbn = {978-1-4577-2053-6},
	issn = {1550-3607},
	author = {Kobbane, A. and Koulali, M. -A. and Tembine, H. and El Koutbi, M. and Ben-Othman, J.}
}
@article {Tembine20111529,
	title = {Dynamic robust power allocation games under channel uncertainty and time delays},
	journal = {Computer Communications},
	volume = {34},
	number = {12},
	year = {2011},
	note = {cited By 1},
	pages = {1529-1537},
	doi = {10.1016/j.comcom.2011.02.009},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959380843\&doi=10.1016\%2fj.comcom.2011.02.009\&partnerID=40\&md5=5103dd198356953b3e6cf4a1d9c94519},
	author = {Tembine, H. and Kobbane, A. and Koutbi, M.E.}
}
