Professional Courses
Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.
Categories
Interactive Games
Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.
Typing
Memory
Math
English Adventures
Knowledge
Edge and fog computing emerge as vital paradigms in response to the challenges posed by the exponential growth of IoT devices. These models aim to enhance data processing by minimizing latency, bandwidth consumption, and improving responsiveness through local processing capabilities. The chapter discusses the architectural frameworks, benefits of real-time data processing, and various deployment models to illustrate the significance of edge and fog computing in modern applications.
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take mock test.
Sections
References
Untitled document (15).pdfClass Notes
Memorization
What we have learnt
- Edge Computing processes da...
- Fog Computing acts as a dis...
- Real-time data processing e...
Final Test
Revision Tests
What we have learnt
- Edge Computing processes data at or near its source to minimize latency and reduce network traffic.
- Fog Computing acts as a distributed layer between edge devices and the cloud, enabling additional processing and analytics.
- Real-time data processing enabled by edge and fog computing enhances responsiveness in critical applications across various industries.
Key Concepts
-
Term: Edge Computing
Definition: Processing data at or near the location where it is generated to allow local decision-making and reduce dependency on cloud resources.
-
Term: Fog Computing
Definition: A network architecture that provides services at an intermediate layer between the edge and the cloud, enhancing local data processing and analytics.
-
Term: Edge AI
Definition: The deployment of machine learning models on edge devices for real-time intelligent tasks such as image recognition and anomaly detection.
-
Term: Architecture of Edge/Fog Computing
Definition: A three-layer framework that includes edge, fog, and cloud layers, each serving distinct roles in data processing and analytics.
-
Term: Deployment Models
Definition: Various strategies for implementing edge and fog computing, including on-device AI/ML, gateway-centric processing, and hybrid models.