@article {Ben-Aboud2021107149,
	title = {Electro-Smog Monitoring Using Low-Cost Software-Defined Radio Dongles},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	note = {cited By 1},
	pages = {107149-107158},
	abstract = {With the increasing use of wireless communication technologies, it is important to monitor electromagnetic exposure (ideally with high temporal and spatial resolutions). In this paper, we explore the use of low-cost software-defined radio dongle for electro-smog measurements and more specifically for electro-magnetic fields power measurements and the estimation of the incident power density. We describe how the raw data is collected and then compute the average electromagnetic field power. We then compensate for the non-linearity of the amplifier and the antenna gain to get the corrected electromagnetic field power measurements. We use these measurements to estimate the incident power density which is the metric that we use to evaluate the electro-smog. The results show that the considered low-cost SDR dongle is stable and provides good quality power measurements. The estimation of the incident power density is shown to be accurate enough for monitoring the electro-smog. {\textcopyright} 2013 IEEE.},
	keywords = {Air pollution, Analog circuits, Antenna gains, Antennas, Costs, Electromagnetic exposure, Electromagnetic fields, Incident power, Low costs, Radio, Radio receivers, Software radio, Software-defined radios, Temporal and spatial, Wireless communication technology},
	doi = {10.1109/ACCESS.2021.3100773},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112673496\&doi=10.1109\%2fACCESS.2021.3100773\&partnerID=40\&md5=b7b53e18cf471222f475a67fe957cbee},
	author = {Ben-Aboud, Y. and Ghogho, M. and Pollin, 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.}
}
@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.}
}
