@article {LHAZMIR2020102102,
	title = {A decision-making analysis in UAV-enabled wireless power transfer for IoT networks},
	journal = {Simulation Modelling Practice and Theory},
	volume = {103},
	year = {2020},
	pages = {102102},
	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.},
	keywords = {GMDP, Internet of things, Unmanned aerial vehicle, Wireless energy transfer},
	issn = {1569-190X},
	doi = {https://doi.org/10.1016/j.simpat.2020.102102},
	url = {https://www.sciencedirect.com/science/article/pii/S1569190X2030040X},
	author = {Safae Lhazmir and Omar Ait Oualhaj and Abdellatif Kobbane and Lynda Mokdad}
}
