@article {Mabrouk2021,
	title = {A cooperative game based mechanism for autonomous organization and ubiquitous connectivity in VANETs},
	journal = {Simulation Modelling Practice and Theory},
	volume = {107},
	year = {2021},
	note = {cited By 7},
	abstract = {Vehicular communications in Vehicular Ad-hoc NETworks (VANETs) play an interesting role in improving traffic flow on the roads. In such wireless networks, the vehicles cooperate with each other or with the roadside infrastructure to disseminate the data. In this paper, we introduce a mechanism based on coalitional game theory to study the cooperative transmission in VANETs. Each vehicle tries to access the Internet via gateways that can be either fixed or mobile. However, these gateways help the vehicles to access the Internet by cooperatively relaying the vehicles{\textquoteright} messages. Thus, two notions to indicate the availability of transmission between vehicles and gateways are introduced. These notions are chosen to deal with the highly dynamic topology in VANETs, namely the notion of an encounter and the notion of a link{\textquoteright}s predicted lifetime. The problem is modelled as a non-transferable utility game. Then, an algorithm is proposed to dynamically form coalitions among the vehicles. Moreover, a probability of being active is set in terms of speed of the received vehicle. To evaluate the proposed solution, two scenarios are used: fixed gateways scenario and mobile gateways scenario. The numerical results show that the scenario of mobile gateways is more effective in terms of stimulating vehicles to regroup into coalitions. Hence, this helps the vehicles to access the Internet. {\textcopyright} 2020},
	keywords = {Coalitional game theory, Cooperative game, Cooperative transmission, Dynamic topologies, Game theory, Gateways (Computer networks), Non-transferable utilities, Numerical results, Probability, Vehicle transmissions, Vehicular ad hoc networks, Vehicular Adhoc Networks (VANETs), Vehicular communications},
	doi = {10.1016/j.simpat.2020.102213},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093923113\&doi=10.1016\%2fj.simpat.2020.102213\&partnerID=40\&md5=3ede889d7bb9d063f8c8265857ff2ede},
	author = {Mabrouk, A. and Naja, A. and Oualhaj, O.A. and Kobbane, A. and Boulmalf, M.}
}
@conference {Naja2020,
	title = {Alert Message Dissemination using Graph-based Markov Decision Process Model in VANETs},
	booktitle = {IEEE International Conference on Communications},
	volume = {2020-June},
	year = {2020},
	note = {cited By 0},
	abstract = {Vehicular Ad-hoc Networks (VANETs) have many promising applications such as improving vehicle driver{\textquoteright}s safety, and so, decreasing car deaths. In VANETs, the main communication way is broadcast. Such Broadcast needs to be done efficiently especially in congested areas to avoid the broadcast storm problem. A crashed vehicle disseminates a message about its incident to all nodes in the network. The vehicle nodes of the network have to disseminate in their turn the alert message carefully to maximize both reachability and saved rebroadcast (by reducing congestion) while minimizing the delay. This concern is demonstrated and modeled by decision hypothesis. In this paper, at first, we are keen on the Markov decision process (MDP), which is utilized to model and tackle such successive decision issues. We aim to optimize a utility for each vehicle relying upon an irregular domain and choices made by it. As the MDP formalism achieves its cutoff points when it is important to consider the cooperation between the several vehicles. We will begin utilizing the Graph-based MDP where the state and activity spaces are factorizable by factors. We then demonstrate that the transition functions and rewards are deteriorated into nearby functions, and the reliance relations between the vehicles are spoken to by a graph. To Figure the optimal strategy, we use Mean Field Approximation (MFA) for tackling GMDP issue. {\textcopyright} 2020 IEEE.},
	keywords = {Accidents, Broadcast storm problem, Graphic methods, Markov decision process models, Markov Decision Processes, Markov processes, Mean field approximation, Message dissemination, Mobile telecommunication systems, Optimal strategies, Traffic congestion, Transition functions, Vehicles, Vehicular ad hoc networks, Vehicular Adhoc Networks (VANETs)},
	doi = {10.1109/ICC40277.2020.9148997},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089436123\&doi=10.1109\%2fICC40277.2020.9148997\&partnerID=40\&md5=12d16adc7ab7a4b60d7ff52c15709c2c},
	author = {Naja, A. and Oualhaj, O.A. and Boulmalf, M. and Essaaidi, M. 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.}
}
@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.}
}
@conference {Lhazmir2020,
	title = {UAV for Wireless Power Transfer in IoT Networks: A GMDP approach},
	booktitle = {IEEE International Conference on Communications},
	volume = {2020-June},
	year = {2020},
	note = {cited By 3},
	abstract = {Unmanned aerial vehicles (UAVs) are a promising technology employed as moving aggregators and wireless power transmitters for IoT networks. In this paper, we consider an UAV-IoT wireless energy and data transmission system and the decision-making problem is investigated. We aim at optimizing the nodes{\textquoteright} utilities by defining a good packet delivery and energy transfer policy according to the system state. We formulate the problem as a Markov Decision Process (MDP) to tackle the successive decision issues. As the MDP formalism achieves its limits when the neighbors{\textquoteright} interactions are considered, we formulate the problem as a Graph-based MDP (GMDP). We then propose a Mean-Field Approximation (MFA) algorithm to find a solution. The simulation results show that our framework achieves a good analysis of the system behavior. {\textcopyright} 2020 IEEE.},
	keywords = {Antennas, Approximation algorithms, Behavioral research, Decision making, Decision-making problem, Energy transfer, Graphic methods, Inductive power transmission, Internet of things, Markov Decision Processes, Markov processes, Mean field approximation, Packet Delivery, System behaviors, System state, Unmanned aerial vehicles (UAV), Wireless energy, Wireless power},
	doi = {10.1109/ICC40277.2020.9148956},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089418365\&doi=10.1109\%2fICC40277.2020.9148956\&partnerID=40\&md5=a3d1ed58b9a7436f2191be8372102f51},
	author = {Lhazmir, S. and Oualhaj, O.A. and Kobbane, A. and Amlioud, E.M. and Ben-Othman, J.}
}
@conference {Oualhaj20181,
	title = {Graph-Based MDP to Mobile Source with Energy Harvesting in Delay Tolerant Networks System},
	booktitle = {2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings},
	volume = {2018-January},
	year = {2018},
	pages = {1-6},
	doi = {10.1109/GLOCOM.2017.8253948},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046359869\&doi=10.1109\%2fGLOCOM.2017.8253948\&partnerID=40\&md5=ad621053c85f2d4d1fe75a91423a6718},
	author = {Oualhaj, O.A. and Elmachkour, M. and Kobbane, A. and Ben-Othman, J.}
}
@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.}
}
@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.}
}
