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| Titre : | Q-learning-based medium congestion control in WSN for smart farming applications | | Type de document : | document multimédia | | Auteurs : | Amel Cheifa, Auteur ; Rim Miloudi, Auteur ; Lakhdar Kamel Oulad Djedid, Directeur de thèse | | Editeur : | Laghouat : Université Amar Telidji - Département d'informatique | | Année de publication : | 2025 | | Importance : | 87 p. | | Accompagnement : | 1 disque optique numérique (CD-ROM) | | Note générale : | Option : Networks, distributed systems and applications | | Langues : | Anglais | | Mots-clés : | Wireless Sensor Networks (WSNs) Smart Farming Medium Access Control (MAC) Q-learning Reinforcement Learning (RL) Congestion Control (TDMA) | | Résumé : | This thesis proposes a novel Q-learning-based (MAC) protocol to address the challenge of medium congestion control in Wireless Sensor Networks (WSNs) for smart farming applications, where traditional static or centralized MAC protocols often fail to adapt to scalability and resource-constrained agricultural environments. By integrating reinforcement learning into a (TDMA) framework, the protocol enables sensor nodes to autonomously learn optimal transmission strategies, dynamically selecting time slots based on environmental feedback to minimize collisions and improve efficiency. The decentralized learning mechanism allows each node to maintain a Q-table, iteratively refining slot selection to enhance throughput and reduce energy consumption. Simulation results demonstrate significant improvements in collision probability, throughput, and fairness compared to conventional (TDMA) and contention-based protocols, making the approach particularly effective in dense deployments. The protocol’s adaptabil- ity and scalability highlight its suitability for rural agricultural settings, where reliability and energy efficiency are critical. | | note de thèses : | Mémoire de master en informatique |
Q-learning-based medium congestion control in WSN for smart farming applications [document multimédia] / Amel Cheifa, Auteur ; Rim Miloudi, Auteur ; Lakhdar Kamel Oulad Djedid, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 87 p. + 1 disque optique numérique (CD-ROM). Option : Networks, distributed systems and applications Langues : Anglais | Mots-clés : | Wireless Sensor Networks (WSNs) Smart Farming Medium Access Control (MAC) Q-learning Reinforcement Learning (RL) Congestion Control (TDMA) | | Résumé : | This thesis proposes a novel Q-learning-based (MAC) protocol to address the challenge of medium congestion control in Wireless Sensor Networks (WSNs) for smart farming applications, where traditional static or centralized MAC protocols often fail to adapt to scalability and resource-constrained agricultural environments. By integrating reinforcement learning into a (TDMA) framework, the protocol enables sensor nodes to autonomously learn optimal transmission strategies, dynamically selecting time slots based on environmental feedback to minimize collisions and improve efficiency. The decentralized learning mechanism allows each node to maintain a Q-table, iteratively refining slot selection to enhance throughput and reduce energy consumption. Simulation results demonstrate significant improvements in collision probability, throughput, and fairness compared to conventional (TDMA) and contention-based protocols, making the approach particularly effective in dense deployments. The protocol’s adaptabil- ity and scalability highlight its suitability for rural agricultural settings, where reliability and energy efficiency are critical. | | note de thèses : | Mémoire de master en informatique |
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