User:Farhan - Revision history

Farhan: /* 2.2 Cloud */

2025-04-25T08:59:24Z

2.2 Cloud

← Older revision		Revision as of 08:59, 25 April 2025
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	Cloud computing is an on-demand model of ubiquitous, easy, network access to shared pools of programmable computer infrastructure such as networks, servers, storage, software applications, and services that may be rapidly de-provisioned and provisioned with little administrative effort. Cloud computing provides access to high-bandwidth computational power without the necessity of owning and maintaining physical infrastructures.		Cloud computing is an on-demand model of ubiquitous, easy, network access to shared pools of programmable computer infrastructure such as networks, servers, storage, software applications, and services that may be rapidly de-provisioned and provisioned with little administrative effort. Cloud computing provides access to high-bandwidth computational power without the necessity of owning and maintaining physical infrastructures.

			===='''Why the Continuum Will Dominate the Future'''====

			'''1. Proximity and Real-Time Responsiveness'''
			Edge computing brings computation closer to data sources—ideal for latency-sensitive applications such as autonomous vehicles, augmented reality, and real-time video analytics. The continuum optimizes where computation occurs—at the edge, near edge, or cloud—based on performance needs.

			'''2. Scalability Across Diverse Infrastructures'''
			From smart homes to industrial automation to smart cities, edge devices vary widely in capabilities. The continuum provides a unified model to scale applications from small edge sensors to large cloud-hosted AI models.

			'''3. Flexibility and Adaptability'''
			Workloads can dynamically shift based on resource availability, cost, network conditions, or privacy constraints. For instance, video streams can be pre-processed at the edge and fully analyzed in the cloud only when needed, reducing bandwidth usage and improving efficiency.

			'''4. Support for Advanced Use Cases'''
			Emerging applications such as connected autonomous systems, Industry 4.0, and real-time federated learning rely on the interplay between edge and cloud. The continuum supports these by allowing AI, analytics, and orchestration tools to operate fluidly across environments.

			'''5. Improved Security, Privacy, and Data Sovereignty'''
			The continuum allows sensitive data to be processed locally, enforcing data residency laws and reducing exposure to breaches. Meanwhile, global-scale analytics can be performed on anonymized or aggregated data in the cloud.

			'''6. Optimized Resource Utilization and Cost Efficiency'''
			The continuum benefits from statistical multiplexing, allowing workloads to be scheduled and shifted to underutilized resources whether at the edge or in the cloud, enhancing overall utilization and reducing operational costs.

			===='''Technical Innovations:'''====
			*'''Federation Standards (e.g., IEEE 2302-2021):''' Enables trust and interoperability across diverse environments,
			*'''Zero-Trust Security Models:''' Secure workloads dynamically across distributed nodes,
			*'''Microservices and Containers:''' Allow modular, portable deployments across cloud and edge,
			*'''Orchestration Platforms (like Kubernetes and KubeEdge):''' Manage application components across the continuum.

			===='''Broader Impact and Vision'''====
			The edge–cloud continuum supports global digital transformation. It’s essential to sectors like '''smart healthcare, national disaster response systems, autonomous transportation, and cyber-physical systems,''' all of which need '''reliability, responsiveness, and scalability''' that neither cloud nor edge can provide alone.

	===='''Characteristics of Cloud Computing:'''====		===='''Characteristics of Cloud Computing:'''====

Farhan: /* Digital Twins */

2025-04-25T08:36:18Z

Digital Twins

← Older revision		Revision as of 08:36, 25 April 2025
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	==='''Digital Twins'''===		==='''Digital Twins'''===
			===='''What Is a Digital Twin?'''====
			A Digital Twin is a virtual replica of a physical object, system, or process that is continuously updated with real-time data. It uses sensors, connectivity, and analytics to mirror and simulate the behavior and performance of its physical counterpart. This enables organizations to monitor, diagnose, predict, and optimize operations virtually before applying changes in the real world.

			===='''How Digital Twins Relate to IoT'''====
			The Internet of Things (IoT) plays a foundational role in enabling Digital Twin technology. IoT devices—such as sensors, actuators, smart meters, and wearables—collect data from the physical environment and stream it into the digital twin model in real time. Here’s how the connection works:
			#'''Data Collection:''' IoT sensors gather metrics such as temperature, vibration, pressure, humidity, or motion,
			#'''Data Transmission:''' These metrics are transmitted over a network to a central system,
			#'''Real-Time Sync:''' The digital twin receives this data to update its model, reflecting the real-time status of the asset,
			#'''Insight Generation:''' Analytics, AI/ML models, and simulations applied to the digital twin enable predictive maintenance, performance tuning, and scenario testing without affecting the physical system.

			===='''Practical Use Cases'''====
			*'''Smart Manufacturing:''' IoT-enabled machinery is mirrored by digital twins to optimize workflows, reduce downtime, and simulate design changes,
			*'''Smart Cities:''' Digital twins of buildings, transport systems, or power grids leverage data from IoT sensors to improve efficiency and public safety,
			*'''Healthcare:''' Wearables and connected medical devices provide biometric data to patient-specific digital twins for personalized treatment simulations.

			===='''Benefits:'''====
			* Real-Time Monitoring,
			* Predictive Analytics & Maintenance
			* Virtual Experimentation
			* Enhanced Decision-Making
			* Enhanced Decision-Making

			This foundation makes it easy to later explore where digital twins should be deployed (edge vs cloud), based on '''latency, bandwidth, data sensitivity''', and '''computational demand'''.


	Digital twins are virtual representations of physical objects that are regularly updated with real-time data so as to replicate, predict, and maximize functionality. Digital twins are increasingly applied in manufacturing, healthcare, and smart city projects that enable intelligent cities, where predictive maintenance and systems optimization can be realized.		Digital twins are virtual representations of physical objects that are regularly updated with real-time data so as to replicate, predict, and maximize functionality. Digital twins are increasingly applied in manufacturing, healthcare, and smart city projects that enable intelligent cities, where predictive maintenance and systems optimization can be realized.

141.215.80.152: /* 1.4 Edge-Cloud Continuum */

2025-04-09T20:04:39Z

1.4 Edge-Cloud Continuum

← Older revision		Revision as of 20:04, 9 April 2025
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	----		----

	=='''1.4 Edge-Cloud Continuum'''==		=='''2.4 Edge-Cloud Continuum'''==
	Edge-Cloud Continuum is defined as an adaptive and dynamic computational paradigm that balances edge, fog, and cloud computation workloads. Through this integration of environments, applications can toggle between local and remote processing depending on workload, latency constraints, and network status.		Edge-Cloud Continuum is defined as an adaptive and dynamic computational paradigm that balances edge, fog, and cloud computation workloads. Through this integration of environments, applications can toggle between local and remote processing depending on workload, latency constraints, and network status.

141.215.80.152: /* 1.3 Edge and Fog */

2025-04-09T20:04:31Z

1.3 Edge and Fog

← Older revision		Revision as of 20:04, 9 April 2025
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	----		----

	=='''1.3 Edge and Fog'''==		=='''2.3 Edge and Fog'''==
	===='''Edge Computing:'''====		===='''Edge Computing:'''====
	Edge computing handles data at or close to the place where the data is situated, minimizing the latency in transmitting data to the cloud servers centrally. It is critical for applications that need real-time data processing and response, including autonomous vehicles, industrial automation, and medical monitoring.		Edge computing handles data at or close to the place where the data is situated, minimizing the latency in transmitting data to the cloud servers centrally. It is critical for applications that need real-time data processing and response, including autonomous vehicles, industrial automation, and medical monitoring.

141.215.80.152: /* 1.1 IoT, Mobile, and Digital Twins */

2025-04-09T20:03:56Z

1.1 IoT, Mobile, and Digital Twins

← Older revision		Revision as of 20:03, 9 April 2025
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	The confluence of IoT, mobile computing, digital twins, and cloud infrastructure creates a robust edge computing architecture. We present in this chapter the basic building blocks and layers of edge computing, such as IoT and digital twins, cloud infrastructure, fog computing, and the edge-cloud continuum.		The confluence of IoT, mobile computing, digital twins, and cloud infrastructure creates a robust edge computing architecture. We present in this chapter the basic building blocks and layers of edge computing, such as IoT and digital twins, cloud infrastructure, fog computing, and the edge-cloud continuum.
	----		----
	=='''1.1 IoT, Mobile, and Digital Twins'''==		=='''2.1 IoT, Mobile, and Digital Twins'''==
	==='''Internet of Things (IoT)'''===		==='''Internet of Things (IoT)'''===

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	----		----

	==='''1.2 Cloud'''===		==='''2.2 Cloud'''===

	Cloud computing is an on-demand model of ubiquitous, easy, network access to shared pools of programmable computer infrastructure such as networks, servers, storage, software applications, and services that may be rapidly de-provisioned and provisioned with little administrative effort. Cloud computing provides access to high-bandwidth computational power without the necessity of owning and maintaining physical infrastructures.		Cloud computing is an on-demand model of ubiquitous, easy, network access to shared pools of programmable computer infrastructure such as networks, servers, storage, software applications, and services that may be rapidly de-provisioned and provisioned with little administrative effort. Cloud computing provides access to high-bandwidth computational power without the necessity of owning and maintaining physical infrastructures.

Farhan at 03:14, 7 April 2025

2025-04-07T03:14:57Z

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Farhan at 19:02, 4 April 2025

2025-04-04T19:02:31Z

← Older revision		Revision as of 19:02, 4 April 2025
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	'''Diagram 1: Digital Twin Edge Network Architecture'''		'''Diagram 1: Digital Twin Edge Network Architecture'''
	[[File:\|500px\|thumb\|center\|Real-Time Edge Adoption]]		[[File:Intro_to_edge.png\|500px\|thumb\|center\|Real-Time Edge Adoption]]

	This diagram illustrates a layered architecture where virtual twins communicate with physical IoT devices		This diagram illustrates a layered architecture where virtual twins communicate with physical IoT devices
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	'''Diagram 2: Edge Computing Architecture'''		'''Diagram 2: Edge Computing Architecture'''
	[[File:\|500px\|thumb\|center\|Real-Time Edge Adoption]]		[[File:Diagram_2_Edge_Computing_Architecture.png\|500px\|thumb\|center\|Real-Time Edge Adoption]]

	This diagram represents the layered structure of edge computing, highlighting how the cloud layer		This diagram represents the layered structure of edge computing, highlighting how the cloud layer
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	----		----

	==='''1.3 Edge and Fog'''===		=='''1.3 Edge and Fog'''==
	===='''Edge Computing:'''====		===='''Edge Computing:'''====
	Edge computing handles data at or close to the place where the data is situated, minimizing the latency in		Edge computing handles data at or close to the place where the data is situated, minimizing the latency in
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	'''Diagram 3: Edge-Fog-Cloud Network Architecture'''		'''Diagram 3: Edge-Fog-Cloud Network Architecture'''
	[[File:\|500px\|thumb\|center\|Real-Time Edge Adoption]]		[[File:Diagram_3_Edge_Fog_Cloud_Network_Architecture.png\|500px\|thumb\|center\|Real-Time Edge Adoption]]

	The diagram shows the interplay between cloud, fog, and edge layers, emphasizing the data flow from		The diagram shows the interplay between cloud, fog, and edge layers, emphasizing the data flow from

Farhan at 02:31, 4 April 2025

2025-04-04T02:31:36Z

← Older revision		Revision as of 02:31, 4 April 2025
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	intelligent orchestration		intelligent orchestration

	===='''Applications:'''		===='''Applications:'''====
	*'''Smart Grids:''' Real-time monitoring and load balancing between local power sources and		*'''Smart Grids:''' Real-time monitoring and load balancing between local power sources and
	centralized control		centralized control

Farhan at 02:31, 4 April 2025

2025-04-04T02:31:14Z

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Farhan at 02:19, 4 April 2025

2025-04-04T02:19:01Z

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