@article {Saadaoui2022106436,
	title = {A Local PSO-Based Algorithm for Cooperative Multi-UAV Pollution Source Localization},
	journal = {IEEE Access},
	volume = {10},
	year = {2022},
	note = {cited By 0},
	pages = {106436-106450},
	abstract = {Recently, air pollution has grown significantly, and it can frequently be challenging to identify the sources of contaminants. This article studies the deployment of multi-cooperative unmanned aerial vehicles (UAVs) to look for sources of pollution in an unknown region. Specifically, a probabilistic search strategy based on the Local Particle Swarm Optimization (LoPSO) method is suggested to design an optimal strategy that enhances the cooperative search of drones while cutting down on overall search time and improving source detection efficiency. The entire strategy is divided into two phases: exploration and exploitation. In the first phase, the detection and tracing of the plume is the favored task, in which each UAV operates in either the Greedy or LoPSO mode and chooses its path based on plane coordinates generated according to the active mode. By utilizing the areas with a high probability of discovering the source on flight mode LoPSO, the search is focused during the exploitation phase on the precise search of the exact location of the pollutant source. This is done under the direction of a probabilistic computation that uses the Bayesian process model to create and update the probability map of the pollutant source location as new sensor data becomes available. The simulation data of the proposed technique demonstrates promising results in terms of the complexity and accuracy attained in identifying pollution sources. {\textcopyright} 2013 IEEE.},
	keywords = {Aerial vehicle, Air pollution, Aircraft detection, Antennas, Cooperative search, Decision making, Decisions makings, Drones, Fleet operations, Greedy, Job analysis, Location, Location awareness, Particle swarm, particle swarm optimization, Particle swarm optimization (PSO), Pollution sources, Robot kinematics, Swarm optimization, Task analysis, Unmanned aerial vehicle},
	doi = {10.1109/ACCESS.2022.3212079},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140203997\&doi=10.1109\%2fACCESS.2022.3212079\&partnerID=40\&md5=8b9e22a8dd7a3ee80fc14fb00819a50f},
	author = {Saadaoui, H. and El Bouanani, F.}
}
@conference {Saadaoui2021,
	title = {Communication and Energy Optimization of Local PSO-assisted Multi-UAVs for Moving Targets Exploration},
	booktitle = {Proceedings - 4th International Conference on Advanced Communication Technologies and Networking, CommNet 2021},
	year = {2021},
	note = {cited By 1},
	abstract = {Limited resources, such as energy, processing power, memory, and communication bandwidth, limit the usage of multi-UAV systems in the target search domain. The limitation of energy, in particular, has a significant impact on the system{\textquoteright}s performance, especially because the overall energy consumption is frequently dominated by the cost of communication, i.e. the computational and sensing energy are insignificant in comparison to the communication energy consumption. As a result, the system{\textquoteright}s lifetime may be considerably prolonged by reducing the communication as well as the volume of communication data, at the penalty of increased computing cost. This work presents a hierarchic approach for allocating multi-UAV resources based on a cooperative and competitive particle swarm optimization (PSO) algorithm, with the goal of achieving an optimal balance between communication and processing energy. According to the simulation findings, our technique can save a substantial amount of energy when compared to a benchmark strategy namely random search (RS) and PSO. {\textcopyright} 2021 IEEE.},
	keywords = {Antennas, Communication optimization, Cooperative communication, Energy, Energy optimization, Energy processing, Energy utilization, Moved target, Moving targets, Multi UAV, Particle swarm optimization (PSO), Power communications, Processing power, Unmanned aerial vehicle, Unmanned aerial vehicles (UAV), Vehicle to vehicle communications},
	doi = {10.1109/CommNet52204.2021.9641888},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124033702\&doi=10.1109\%2fCommNet52204.2021.9641888\&partnerID=40\&md5=6d7a08bcf8e4c74d296ed715e537932e},
	author = {Saadaoui, H. and El Bouanani, F.}
}
@article {Saadaoui2021134998,
	title = {Information sharing based on local pso for uavs cooperative search of moved targets},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	note = {cited By 3},
	pages = {134998-135011},
	abstract = {This paper proposes an optimization strategy for searching moving targets{\textquoteright} locations using cooperative unmanned aerial vehicles (UAVs) in an unknown environment. Such a strategy aims at reducing the overall search time and impact of uncertainties caused by the motion of targets, as well as improving the detection efficiency of UAVs. Specifically, we report, based on the UAV{\textquoteright}s scan of a location and taking into account (i) the detection and communication coverage limitations, and (ii) either a false alarm or inaccurate detection of the target, either the existence or the absence of the target. Moreover, leveraging a cooperative and competitive particle swarm optimization (PSO) algorithm, a decentralized target search model, relying on a real-time dynamic construction of cooperative UAV local sub-swarms (LoPSO), is proposed. Each sub-swarm strives to validate quickly the target location, updated based on the Bayesian theory. In such a strategy, each UAV operates in two flight modes, namely, either in swarm mode or in Greedy mode, and takes into consideration the received data from other UAVs to improve the overall environmental information. The simulation results revealed that the LoPSO outperforms other well-known searching methods of target methods for target search in unknown environments in terms of both performance and computational complexity. {\textcopyright} 2013 IEEE.},
	keywords = {Aircraft detection, Antennas, Cooperative search, Decision making, Decisions makings, Information sharing, Location, Moved target, Optimization strategy, Particle swarm optimization (PSO), Sub-swarms, Target location, Target search, Unknown environments, Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
	doi = {10.1109/ACCESS.2021.3116919},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117048719\&doi=10.1109\%2fACCESS.2021.3116919\&partnerID=40\&md5=9c295c1ff03ee777bc468333f6bc6f4e},
	author = {Saadaoui, H. and Bouanani, F.E. and Illi, E.}
}
@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}
}
