Digital twin–assisted urgency-aware task offloading in UAV–HAP edge networks / Hamida Tobbiche

Public ISBD Titre : Digital twin–assisted urgency-aware task offloading in UAV–HAP edge networks Type de document : document multimédia Auteurs : Hamida Tobbiche, Auteur ; Fatima Reche, Auteur ; Yousra Cheriguene, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2026 Importance : 59 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Networks, systems and distributed applications Langues : Anglais (eng) Mots-clés : Mobile edge computing  UAV–HAP networks  Digital twin  Task offloading  Contextual bandits  Online learning  Disaster response Résumé : One promising approach for enabling distributed intelligence at the aerial edge is the integration of Mobile Edge Computing (MEC) with hierarchical Unmanned Aerial Vehicle (UAV) and High-Altitude Platform (HAP) networks. Such architectures provide flexible and real-time computation offloading capabilities, making them particularly suitable for post-disaster rescue and emergency response operations where terrestrial infrastructure may be unavailable. However, optimal task offloading and resource allocation in these environments remain challenging due to dynamic wireless conditions, uncertain aerial channels, fluctuating link quality, and the strict latency requirements of safety-critical applications. This work investigates task offloading and resource scheduling in hierarchical UAV HAPassisted edge networks. After reviewing existing offloading and selection strategies, we propose an integrated online decision framework, termed DT-MAB, which combines a telemetry-driven Digital Twin (DT) layer with a Contextual Multi-Armed Bandit (MAB) learning algorithm. The framework employs an Exponentially Weighted Moving Average (EWMA)-based DT model to predict short term channel conditions and support proactive decision making. To ensure operational reliability, the system incorporates a hard safety filter that enforces communication constraints and an urgency-aware emergency bypass mechanism that prioritizes time-critical tasks without exploration delay. Extensive simulations conducted under complementary operating conditions demonstrate the effectiveness of the proposed approach. Under tight deadline and heavy workload conditions, DT-MAB achieves a 1.55 reduction in the Urgency-Weighted Deadline Violation Rate (UW-DVR) compared with the best static offloading policy. During severe channel degradation events, the framework detects deteriorating link conditions within 2–3 time slots, maintains zero reliability violations throughout the degradation period, and achieves a critical-task deadline violation rate of only 7.1%. These results demonstrate that combining predictive Digital Twin intelligence with contextual online learning and safetyaware decision making provides a robust, adaptive, and deployable solution for next generation UAV–HAP edge computing systems operating in mission-critical environments. note de thèses : Mémoire de master en informatique Digital twin–assisted urgency-aware task offloading in UAV–HAP edge networks [document multimédia] / Hamida Tobbiche, Auteur ; Fatima Reche, Auteur ; Yousra Cheriguene, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2026 . - 59 p. + 1 disque optique numérique (CD-ROM). Option : Networks, systems and distributed applications Langues : Anglais (eng) Mots-clés : Mobile edge computing  UAV–HAP networks  Digital twin  Task offloading  Contextual bandits  Online learning  Disaster response Résumé : One promising approach for enabling distributed intelligence at the aerial edge is the integration of Mobile Edge Computing (MEC) with hierarchical Unmanned Aerial Vehicle (UAV) and High-Altitude Platform (HAP) networks. Such architectures provide flexible and real-time computation offloading capabilities, making them particularly suitable for post-disaster rescue and emergency response operations where terrestrial infrastructure may be unavailable. However, optimal task offloading and resource allocation in these environments remain challenging due to dynamic wireless conditions, uncertain aerial channels, fluctuating link quality, and the strict latency requirements of safety-critical applications. This work investigates task offloading and resource scheduling in hierarchical UAV HAPassisted edge networks. After reviewing existing offloading and selection strategies, we propose an integrated online decision framework, termed DT-MAB, which combines a telemetry-driven Digital Twin (DT) layer with a Contextual Multi-Armed Bandit (MAB) learning algorithm. The framework employs an Exponentially Weighted Moving Average (EWMA)-based DT model to predict short term channel conditions and support proactive decision making. To ensure operational reliability, the system incorporates a hard safety filter that enforces communication constraints and an urgency-aware emergency bypass mechanism that prioritizes time-critical tasks without exploration delay. Extensive simulations conducted under complementary operating conditions demonstrate the effectiveness of the proposed approach. Under tight deadline and heavy workload conditions, DT-MAB achieves a 1.55 reduction in the Urgency-Weighted Deadline Violation Rate (UW-DVR) compared with the best static offloading policy. During severe channel degradation events, the framework detects deteriorating link conditions within 2–3 time slots, maintains zero reliability violations throughout the degradation period, and achieves a critical-task deadline violation rate of only 7.1%. These results demonstrate that combining predictive Digital Twin intelligence with contextual online learning and safetyaware decision making provides a robust, adaptive, and deployable solution for next generation UAV–HAP edge computing systems operating in mission-critical environments. note de thèses : Mémoire de master en informatique

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