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Please use this identifier to cite or link to this item: https://repository.esi-sba.dz/jspui/handle/123456789/680
Title: Optimization methods for LPWAN networks
Authors: LATRECHE, YAssine
Keywords: Internet of Things (IoT)
Long Range Wide Area Network (LoRaWAN)
Spreading Factor (SF) Allocation
Packet Delivery Ratio (PDR)
Network Optimization
Simulation Environments
Genetic Algorithm (GA)
Simulated Annealing (SA)
Differential Evolution (DE)
Surrogate Models
Machine Learning
Issue Date: 2024
Abstract: The Internet of Things (IoT) has brought unprecedented internet connectivity opportunities across various domains with the needs for efficient and scalable communication technologies. Long Range Wide Area Network (Lo- RaWAN) stands out for its capability of long-range, low-power consumption at a cost-effective manner, and it is preferred in several IoT applications. However, optimizing IoT network performance is still a critical challenge, especially through appropriate Spreading Factor (SF) allocation. Through a comprehensive review of the state-of-the-art research of SF optimization in LoRaWAN networks, this thesis has explored a variety of SF optimization methodologies and their implications on Packet Delivery Ratio (PDR), throughput, energy consumption, and network scalability. In addition to reviewing existing SF optimization methodologies, this thesis has covered optimization algorithms, such as Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), which have shown promise in outperforming in network performance through SF optimization. It has also explored surrogate models for rapid optimization by estimating the performance of complex simulations. These optimization algorithms and surrogate models offer large improvements in terms of convergence speed and solution quality. More importantly, they provide us with the necessary tools to address the challenges of dense IoT environments. These findings serve as an essential guidance for network planners and operators, and pave the way for enhanced LoRaWAN deployments. *** L’Internet des Objets (IoT) a ouvert des opportunit´es sans pr´ec´edent pour la connectivit´e dans divers domaines, n´ecessitant des technologies de communication efficaces et ´evolutives. Le r´eseau longue port´ee (LoRaWAN) se distingue par ses capacit´es `a longue port´ee, sa faible consommation d’´energie et son coˆut abordable, en faisant un choix privil´egi´e pour de nombreuses applications IoT. Cependant, l’optimisation des performances du r´eseau, en particulier par une allocation efficace du facteur d’´etalement (SF), demeure un d´efi crucial. Cette th`ese m`ene une revue exhaustive des recherches de pointe sur l’optimisation du SF dans les r´eseaux LoRaWAN, en examinant diverses m´ethodologies et leurs implications sur le taux de livraison des paquets (PDR), le d´ebit, la consommation d’´energie et l’´evolutivit´e globale du r´eseau. En synth´etisant les insights de neuf articles de r´ef´erence, l’´etude met en ´evidence les complexit´es et les compromis impliqu´es dans l’allocation du SF, soulignant la n´ecessit´e de disposer d’environnements de simulation r´ealistes et de strat´egies d’optimisation globales. En plus de revoir les approches existantes, cette th`ese explore des algorithmes d’optimisation avanc´es tels que l’algorithme g´en´etique (GA), le recuit simul´e (SA) et l’´evolution diff´erentielle (DE), qui ont montr´e leur potentiel pour am´eliorer les performances du r´eseau grˆace `a une allocation optimale du SF. L’´etude examine ´egalement le potentiel des mod`eles substituts pour acc´el´erer le processus d’optimisation en approximant les performances de simulations complexes. Ces algorithmes et mod`eles sophistiqu´es offrent des am´eliorations significatives en termes de vitesse de convergence et de qualit´e des solutions, en faisant des outils pr´ecieux pour relever les d´efis des environnements IoT denses et h´et´erog`enes. Les r´esultats fournissent des conseils pr´ecieux pour les planificateurs et les op´erateurs de r´eseaux, ouvrant la voie `a des d´eploiements LoRaWAN am´elior´es capables de r´epondre aux exigences des environnements IoT de plus en plus denses et h´et´erog`enes.
Description: Encadreur : M KHALDI Miloud
URI: https://repository.esi-sba.dz/jspui/handle/123456789/680
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