@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 {11387931120160401,
	title = {Efficient deployment quality analysis for intrusion detection in wireless sensor networks.},
	journal = {Wireless Networks (10220038)},
	volume = {22},
	number = {3},
	year = {2016},
	pages = {991 - 1006},
	abstract = {The intrusion detection in a Wireless Sensor Network is defined as a mechanism to monitor and detect any intruder in a sensing area. The sensor deployment quality is a critical issue since it reflects the cost and detection capability of a wireless sensor network. The quality of deterministic deployment can be determined sufficiently by a rigorous analysis before the deployment. However, when random deployment is required, determining the deployment quality becomes challenging. In the intrusion detection application, it is necessary to define more precise measures of sensing range, transmission range, and node density that impact overall system performance. The major question is centred around the quality of intrusion detection in WSN, how we can guarantee that each point of the sensing area is covered by at least one sensor node, and what a sufficient condition to guarantee the network connectivity? In this paper, we propose an appropriate probabilistic model which provides the cover},
	keywords = {Graph theory, Integer programming, Intrusion detection probability, Intrusion detection systems (Computer security), Multiple access protocols (Computer network protocols), Network connectivity, Network coverage, Quality of service, Sensing range, Transmission range, Wireless sensor networks},
	issn = {10220038},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=113879311\&site=ehost-live},
	author = {Assad, Noureddine and Elbhiri, Brahim and Faqihi, Moulay and Ouadou, Mohamed and Aboutajdine, Driss}
}
@article {10001634720141101,
	title = {A Graph Based Approach to Trace Models Composition.},
	journal = {Journal of Software (1796217X)},
	volume = {9},
	number = {11},
	year = {2014},
	pages = {2813 - 2822},
	abstract = {A model driven engineering process involves different and heterogeneous models that represent various perspectives of the system under development. The model composition operation allows combining those sub-models into an integrated view, but remains a tedious activity. For that, traceability information must be maintained to comprehend the composition effects and better manage the operation itself. Against this context, the current paper describes a framework for model composition traceability. We consider the traces generation concern as a crosscutting concern where the weaving mechanism is performed using graph transformations. A composition specification case study is presented to illustrate our contribution. [ABSTRACT FROM AUTHOR]},
	keywords = {aspect oriented modeling, Computer engineering, Computer simulation, Graph theory, graph transformation, Heterogeneous computing, Information theory, model composition, model transformation, traceability},
	issn = {1796217X},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=100016347\&site=ehost-live},
	author = {Laghouaouta, Youness and Anwar, Adil and Nassar, Mahmoud and Coulette, Bernard}
}
