الحوسبة الطرفية

Edge Computing

• Edge computing moves data processing and storage away from the cloud, closer to the source or user.
• Instead of sending all data to a remote data center, it's processed partially or fully on devices at the network's edge.
• Location: Network edge (IoT devices, cellular towers).

Edge Computing Advantages

• Reduced response time: Processing data locally reduces transmission time.
• Improved reliability: Less dependence on internet connection. Local connection makes the system more reliable in areas with poor connectivity. Business continuity.
• Enhanced data protection: Minimizes data breach risks. Data is stored and processed locally.
• Reduced network load: Less data transmission reduces the burden on network infrastructure, thereby saving costs.

Edge Computing Disadvantages

• Cost: Requires significant investment in infrastructure (devices, servers, networks). Maintenance costs, including software updates, add to operational costs.
• Complexity: Managing multiple devices spread across various locations poses a challenge.
• Security: Increased attack surface with more connected devices. Robust security measures are necessary.
• Scalability: Limited resources in edge devices can hinder system expansion.
• Dependence on infrastructure: Network quality greatly affects performance.

Edge Computing Applications

• Internet of things (IoT): Smart sensors use edge computing for local data analysis and quick decision-making.
• Augmented and virtual reality (AR/VR): Real-time processing of visual data is needed for these applications.

Fog Computing in IoT

• Role: Acts as an intermediary between IoT devices and the cloud, processing and analyzing data before sending to the cloud.
• Operation: Deploys fog servers near IoT devices which handle initial data analysis. Tasks: data filtering, pattern detection, and simple decisions.

Cloud Computing in IoT

• Role: Provides a robust infrastructure for vast-scale data storage and analysis.
• Operation: Collects IoT device data, stores, analyzes it using sophisticated algorithms. Further use includes machine learning model training, decision-making, and useful insight generation.

Integration of different models

• Data collection from IoT devices.
• Initial processing on the edge or fog layer.
• Advanced analysis in the cloud.
• Decision-making happens at various levels: the device, fog layer, and cloud.

Example: Smart Home Monitoring

• Edge: Sensors gather data on temperature, humidity, and lighting.
• Fog: Smart router processes local data; simple decisions are made, e.g., adjusting temperature automatically.
• Cloud: Historical data is sent to the cloud for analysis and pattern identification, leading to energy efficiency and convenience.