Cooperative Caching Policy in Fog Computing for Connected Vehicles

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

In this era, the magnitude of data shared is enormous and raised the bar for the quality of service and maintenance it requires. This paved the road for the integration of Fog Computing, which is an extension of the Cloud. Fog Computing’s main advantage is the increased quantity in which it can be deployed while in close vicinity of the end-users, thus enhancing their Quality of Experience (QoE). The connected vehicles domain is one of many domains that can benefit from Fog Computing. Moreover, caching has been an area of study for many years by researchers that aim to increase cache hit rate and decrease request delays affecting Connected Vehicles networks. Many studies implemented Machine Learning models to enhance cache hit rate and request delays. In this thesis, we implemented cooperation between a Deep Reinforcement Learning (DRL) model and Federated Learning to improve caching in Connected Vehicles connected to fog nodes. Furthermore, the results showed the proposed model's effectiveness compared to traditional algorithms.

روابط وملفات

التعريف والنوع

رقم الوثيقة

0007befe-eca4-4f16-9226-379a169b8434

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

841743_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":"Ali Ghazleh","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"Ramzi A.Haraty","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

Cooperative Caching Policy in Fog Computing for Connected Vehicles

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Fog Computing Caching Connected Vehicles Machine Learning Deep Reinforcement Learning Federated Learning

إشراف وإعداد

الإشراف

Ramzi A.Haraty

الإعداد

Ali Ghazleh

الاقتباسات الببليوغرافية