arXiv:2510.21781v1 Announce Type: new 
Abstract: Real-time video analytics systems typically deploy lightweight models on edge devices to reduce latency. However, the distribution of data features may change over time due to various factors such as changing lighting and weather conditions, leading to decreased model accuracy. Recent frameworks try to address this issue by leveraging remote servers to continuously train and adapt lightweight edge models using more complex models in the cloud. Despite these advancements, existing methods face two key challenges: first, the retraining process is compute-intensive, causing significant delays in model updates; second, the new model may not align well with the evolving data distribution of the current video stream. To address these challenges, we introduce EdgeSync, an efficient edge-model updating approach that enhances sample filtering by incorporating timeliness and inference results, thus ensuring training samples are more relevant to the current video content while reducing update delays. Additionally, EdgeSync features a dynamic training management module that optimizes the timing and sequencing of model updates to improve their timeliness. Evaluations on diverse and complex real-world datasets demonstrate that EdgeSync improves accuracy by approximately 3.4% compared to existing methods and by about 10% compared to traditional approaches.

تحدث EdgeSync ثورة في عالم تحليلات الفيديو في الوقت الحقيقي من خلال تحسين كيفية تكيف نماذج الحافة مع ظروف البيانات المتغيرة. حيث يمكن أن تؤثر عوامل مثل الإضاءة والطقس على دقة النموذج، فإن هذا النهج المبتكر يسمح بالتعلم المستمر والتحديثات من الخوادم البعيدة. وهذا أمر حاسم لأنه يضمن أن تظل أجهزة الحافة عالية الأداء دون تأخير الطرق التقليدية، مما يحسن في النهاية تجربة المستخدم وكفاءة العمليات.

EdgeSync está revolucionando el mundo de la analítica de video en tiempo real al mejorar la forma en que los modelos de borde se adaptan a las condiciones cambiantes de los datos. Dado que factores como la iluminación y el clima pueden afectar la precisión del modelo, este enfoque innovador permite el aprendizaje continuo y las actualizaciones desde servidores remotos. Esto es crucial porque asegura que los dispositivos de borde mantengan un alto rendimiento sin la latencia de los métodos tradicionales, mejorando en última instancia la experiencia del usuario y la eficiencia operativa.

EdgeSync fait sensation dans le domaine de l'analyse vidéo en temps réel en améliorant la façon dont les modèles de périphérie s'adaptent aux conditions de données changeantes. Étant donné que des facteurs tels que l'éclairage et la météo peuvent affecter la précision des modèles, cette approche innovante permet un apprentissage et des mises à jour continus à partir de serveurs distants. Cela est crucial car cela garantit que les appareils de périphérie maintiennent des performances élevées sans le retard des méthodes traditionnelles, améliorant ainsi l'expérience utilisateur et l'efficacité opérationnelle.

EdgeSync is making waves in the world of real-time video analytics by enhancing how edge models adapt to changing data conditions. As factors like lighting and weather can affect model accuracy, this innovative approach allows for continuous learning and updates from remote servers. This is crucial because it ensures that edge devices maintain high performance without the lag of traditional methods, ultimately improving user experience and operational efficiency.

EdgeSync: Accelerating Edge-Model Updates for Data Drift through Adaptive Continuous Learning

arXiv:2511.19103v1 Announce Type: new 
Abstract: The rapid growth of IoT devices has led to an enormous amount of sensor data that requires transmission to cloud servers for processing, resulting in excessive network congestion, increased latency and high energy consumption. This is particularly problematic in resource-constrained and remote environments where bandwidth is limited, and battery-dependent devices further emphasize the problem. Moreover, in domains such as agriculture, consecutive sensor readings often have minimal variation, making continuous data transmission inefficient and unnecessarily resource intensive. To overcome these challenges, we propose an analytical prediction algorithm designed for edge computing environments and validated through simulation. The proposed solution utilizes a predictive filter at the network edge that forecasts the next sensor data point and triggers data transmission only when the deviation from the predicted value exceeds a predefined tolerance. A complementary cloud-based model ensures data integrity and overall system consistency. This dual-model strategy effectively reduces communication overhead and demonstrates potential for improving energy efficiency by minimizing redundant transmissions. In addition to reducing communication load, our approach leverages both in situ and satellite observations from the same locations to enhance model robustness. It also supports cross-site generalization, enabling models trained in one region to be effectively deployed elsewhere without retraining. This makes our solution highly scalable, energy-aware, and well-suited for optimizing sensor data transmission in remote and bandwidth-constrained IoT environments.

تم اقتراح خوارزمية جديدة للتنبؤ التحليلي لبيئات الحوسبة الطرفية في الزراعة، تهدف إلى تقليل النقل المفرط لبيانات المستشعرات الناتجة عن أجهزة إنترنت الأشياء. تستخدم هذه الطريقة فلترًا تنبؤيًا لتوقع قراءات المستشعر، مما يؤدي إلى تفعيل نقل البيانات فقط عند حدوث انحرافات كبيرة، وبالتالي معالجة مشكلات ازدحام الشبكة واستهلاك الطاقة في البيئات ذات الموارد المحدودة.

Se ha propuesto un nuevo algoritmo de predicción analítica para entornos de computación en el borde en la agricultura, con el objetivo de reducir la transmisión excesiva de datos de sensores generados por dispositivos IoT. Este enfoque utiliza un filtro predictivo para prever las lecturas de los sensores, activando la transmisión de datos solo cuando ocurren desviaciones significativas, abordando así problemas de congestión de red y consumo de energía en entornos con recursos limitados.

Un nouvel algorithme de prédiction analytique a été proposé pour les environnements de calcul en périphérie dans l'agriculture, visant à réduire la transmission excessive des données des capteurs générées par les dispositifs IoT. Cette approche utilise un filtre prédictif pour prévoir les lectures des capteurs, déclenchant la transmission des données uniquement lorsque des écarts significatifs se produisent, abordant ainsi les problèmes de congestion du réseau et de consommation d'énergie dans des environnements à ressources limitées.

A new analytical prediction algorithm has been proposed for edge computing environments in agriculture, aimed at reducing the excessive transmission of sensor data generated by IoT devices. This approach utilizes a predictive filter to forecast sensor readings, triggering data transmission only when significant deviations occur, thereby addressing issues of network congestion and energy consumption in resource-constrained settings.

EdgeSync: Accelerating Edge-Model Updates for Data Drift through Adaptive Continuous Learning

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