Introduction to Edge Computing - Revision history

Vrunda Kadam: /* 1.4 Different Edge Computing Paradigms */

2025-04-16T20:43:38Z

1.4 Different Edge Computing Paradigms

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	== 1.4 Different Edge Computing Paradigms ==		== 1.4 Different Edge Computing Paradigms ==
			===1.4.1 Fog Computing ===
			Fog computing is an architecture introduced by Cisco to extend cloud services to the edge of the network. Instead of sending data from edge devices directly to the cloud, fog computing enables processing, storage, and analysis to occur closer to the data source, such as on local routers, gateways, or switches. This paradigm creates a layered system where intermediate nodes handle computations, reducing latency and bandwidth usage. It supports a decentralized model, which is ideal for real-time applications like industrial automation, smart traffic lights, and autonomous systems. Fog computing enhances scalability and responsiveness while maintaining a link to the central cloud for more intensive processing or long-term storage.
			=== 1.4.2 Mobile Edge Computing (MEC) ===
			Mobile Edge Computing, now often referred to as Multi-access Edge Computing (MEC), is designed to bring computational capabilities directly into the radio access network (RAN), especially at 4G or 5G base stations. This paradigm enables real-time data processing with ultra-low latency and high bandwidth by placing services physically close to mobile users. MEC systems can take advantage of location and context information from the mobile network, making them ideal for time-sensitive and location-aware applications such as augmented reality (AR), virtual reality (VR), real-time video analytics, and connected vehicle systems. MEC is crucial in enabling the responsiveness and scalability expected from next-generation mobile networks.
			===1.4.3 Edge Cloud ===
			The edge cloud paradigm combines traditional cloud computing with edge computing in a federated or hybrid architecture. It enables dynamic resource sharing between cloud data centers and edge infrastructure, offering the best of both worlds: centralized power and local responsiveness. Edge cloud systems support elasticity, scalability, and fault tolerance while minimizing latency. This paradigm is especially relevant for large-scale applications that require real-time responsiveness with centralized oversight, such as smart city infrastructure, telemedicine, and distributed AI models. The edge cloud facilitates efficient load balancing, allowing tasks to be executed wherever it’s most optimal based on conditions like network congestion, location, and data urgency.
			=== 1.4.4 Cloudlet Computing ===
			Cloudlet computing introduces the idea of deploying small-scale cloud data centers known as "cloudlets" at the edge of the network, typically near mobile devices. Originally proposed by Carnegie Mellon University, cloudlets act as intermediate computation nodes that are more powerful than mobile devices but closer than the cloud. They enable mobile applications to offload resource-intensive tasks such as speech recognition, facial recognition, or gaming graphics rendering. Because cloudlets are located close to end users, they offer low-latency and high-bandwidth connections, which significantly improve performance and user experience compared to relying solely on distant cloud servers.

	= References =		= References =

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References

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	= References =		= References =
	# “ Edge Computing: Vision and ~~Challenges” -~~ IEEE Internet of Things Journal		# [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7488250&isnumber=7563473 W. Shi, J. Cao, Q. Zhang, Y. Li and L. Xu, "Edge Computing: Vision and Challenges," in IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646, Oct. 2016, doi: 10.1109/JIOT.2016.2579198]
	# "An Overview on Edge Computing Research"		# [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9083958&isnumber=8948470 ,K. Cao, Y. Liu, G. Meng and Q. Sun, "An Overview on Edge Computing Research," in IEEE Access, vol. 8, pp. 85714-85728, 2020, doi: 10.1109/ACCESS.2020.2991734]
	# "A Survey on Edge Computing for the Internet of Things" ~~– ACM Computing Surveys~~.		# [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8123913&isnumber=8274985 W. Yu et al., "A Survey on the Edge Computing for the Internet of Things," in IEEE Access, vol. 6, pp. 6900-6919, 2018, doi: 10.1109/ACCESS.2017.2778504]
	# "Edge Computing: Applications, State-of-the-Art and Challenges"		# Wang, Shufen. (2019). Edge Computing: Applications, State-of-the-Art and Challenges. Advances in Networks. 7. 8. 10.11648/j.net.20190701.12
	# "Edge Computing: Classification, Applications, and Challenges"		# [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9445331&isnumber=9445272 G. Kaur and R. S. Batth, "Edge Computing: Classification, Applications, and Challenges," 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 2021, pp. 254-259, doi: 10.1109/ICIEM51511.2021.9445331]
			# [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9863881&isnumber=9955314 X. Kong, Y. Wu, H. Wang and F. Xia, "Edge Computing for Internet of Everything: A Survey," in IEEE Internet of Things Journal, vol. 9, no. 23, pp. 23472-23485, 1 Dec.1, 2022, doi: 10.1109/JIOT.2022.3200431.]
	# https://aws.amazon.com/what-is/edge-computing/		# https://aws.amazon.com/what-is/edge-computing/
	# https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-edge-computing		# https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-edge-computing

Aayush: /* References */

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References

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	# "Edge Computing: Applications, State-of-the-Art and Challenges"		# "Edge Computing: Applications, State-of-the-Art and Challenges"
	# "Edge Computing: Classification, Applications, and Challenges"		# "Edge Computing: Classification, Applications, and Challenges"
			# https://aws.amazon.com/what-is/edge-computing/
			# https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-edge-computing

Aayush: /* Introduction to Edge Computing */

2025-04-06T18:40:50Z

Introduction to Edge Computing

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	[[File:~~intro_to_edge~~.png\|500px\|thumb\|center\|Real-Time Edge Adoption]]		[[File:intro_to_edge_1.png\|500px\|thumb\|center\|Real-Time Edge Adoption]]


	With the rise of the Internet of Things (IoT) in the early 2000s, experts predicted that by 2019, a significant share of IoT data would be processed at the edge rather than in centralized cloud data centers. This shift was driven by the need for rapid responses in applications like self-driving cars and smart cities, where even minor delays could have serious consequences. Unlike traditional cloud computing, edge computing enhances security, reduces latency, and processes data locally, relieving pressure on the internet while improving overall efficiency. As technology continues to evolve, edge computing plays a crucial role in enabling faster, safer, and more reliable digital experiences.		With the rise of the Internet of Things (IoT) in the early 2000s, experts predicted that by 2019, a significant share of IoT data would be processed at the edge rather than in centralized cloud data centers. This shift was driven by the need for rapid responses in applications like self-driving cars and smart cities, where even minor delays could have serious consequences.

			Unlike traditional cloud computing, edge computing enhances security, reduces latency, and processes data locally, relieving pressure on the internet while improving overall efficiency. As technology continues to evolve, edge computing plays a crucial role in enabling faster, safer, and more reliable digital experiences.

	== 1.1 What is Edge Computing? ==		== 1.1 What is Edge Computing? ==
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	While cloud computing manages data at a central location, edge computing brings the power of computing closer to the source, making it quicker and more efficient for local tasks.		While cloud computing manages data at a central location, edge computing brings the power of computing closer to the source, making it quicker and more efficient for local tasks.

	=== Importance: ===		=== 1.1.1 Importance ===

	Businesses use edge computing to make their devices respond faster and to get quick, valuable insights from data collected on-site. It helps process information in real-time, even in places where cloud connections are slow or unavailable, and prevents networks from getting overloaded with too much data.		Businesses use edge computing to make their devices respond faster and to get quick, valuable insights from data collected on-site. It helps process information in real-time, even in places where cloud connections are slow or unavailable, and prevents networks from getting overloaded with too much data.

	Without edge computing, companies could face higher IT costs, slow systems, and even risks to worker safety in industries like healthcare or manufacturing. By analyzing data right where it’s generated, companies can make better decisions, improve safety, boost performance, and offer smoother experiences for users.		Without edge computing, companies could face higher IT costs, slow systems, and even risks to worker safety in industries like healthcare or manufacturing. By analyzing data right where it’s generated, companies can make better decisions, improve safety, boost performance, and offer smoother experiences for users.

	Edge computing is already powering critical systems in places like hospitals and factories. It helps businesses act faster, automate more processes, and build smarter environments. This opens up chances to launch new products quicker, improve customer experiences, and create new ways to earn revenue.		Edge computing is already powering critical systems in places like hospitals and factories. It helps businesses act faster, automate more processes, and build smarter environments. This opens up chances to launch new products quicker, improve customer experiences, and create new ways to earn revenue.

	=== Working: ===		=== 1.1.2 Working ===

	Edge computing makes apps and smart devices faster and more efficient by processing data closer to where it’s generated — like on the device itself or a nearby local server — instead of relying only on faraway cloud servers.		Edge computing makes apps and smart devices faster and more efficient by processing data closer to where it’s generated — like on the device itself or a nearby local server — instead of relying only on faraway cloud servers.

	[[File:Screenshot 2.png\|550px\|thumb\|center\|~~Fig2:~~ Working of Edge Computing.]]		[[File:Screenshot 2.png\|550px\|thumb\|center\|Working of Edge Computing.]]

	'''Step 1:''' Data Is Generated at the Edge		'''Step 1:''' Data Is Generated at the Edge
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	* Summary Data: Information like completed routes, driving patterns, and system performance is collected and sent periodically to the manufacturer or service provider for analysis and maintenance planning.		* Summary Data: Information like completed routes, driving patterns, and system performance is collected and sent periodically to the manufacturer or service provider for analysis and maintenance planning.

	=== Two Main Uses of Edge Computing: ===		=== 1.1.3 Two Main Uses of Edge Computing: ===

	==== Upstream Applications (Devices > Cloud) ====		==== Upstream Applications (Devices > Cloud) ====
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	== 1.2 Why we need Edge Computing? ==		== 1.2 Why we need Edge Computing? ==

	===The Need for Edge Computing===		=== 1.2.1 The Need for Edge Computing ===

	The exponential growth of data generated by IoT devices, autonomous systems, and real-time applications has placed significant strain on traditional cloud computing architectures. Edge computing emerges as a solution to process data closer to its source, reducing latency, improving efficiency, and enhancing security. This paper explores the necessity of edge computing by analyzing its benefits, key applications, and challenges.		The exponential growth of data generated by IoT devices, autonomous systems, and real-time applications has placed significant strain on traditional cloud computing architectures. Edge computing emerges as a solution to process data closer to its source, reducing latency, improving efficiency, and enhancing security. This paper explores the necessity of edge computing by analyzing its benefits, key applications, and challenges.

	[[File:edge computing screenshot 1.png\|450px\|thumb\|center\|~~Fig1:~~ Comparison between edge computing spectrum and cloud computing.]]		[[File:edge computing screenshot 1.png\|450px\|thumb\|center\|Comparison between edge computing spectrum and cloud computing.]]

	=== ~~1.2.1~~ Latency Reduction and Real-Time Processing ===		==== Latency Reduction and Real-Time Processing ====

	One of the primary drivers for edge computing is the need for real-time data processing. Applications such as autonomous vehicles, healthcare monitoring, and industrial automation require immediate responses, which centralized cloud computing fails to provide due to network latency. By processing data at the edge, delays are minimized, ensuring faster decision-making.		One of the primary drivers for edge computing is the need for real-time data processing. Applications such as autonomous vehicles, healthcare monitoring, and industrial automation require immediate responses, which centralized cloud computing fails to provide due to network latency. By processing data at the edge, delays are minimized, ensuring faster decision-making.

	===~~1.2.2~~ Bandwidth Optimization===		==== Bandwidth Optimization ====
	With billions of IoT devices transmitting data, traditional cloud systems face bottlenecks in network bandwidth. Edge computing mitigates this by processing essential data locally and only sending relevant insights to the cloud, reducing overall bandwidth consumption.		With billions of IoT devices transmitting data, traditional cloud systems face bottlenecks in network bandwidth. Edge computing mitigates this by processing essential data locally and only sending relevant insights to the cloud, reducing overall bandwidth consumption.

	===~~1.2.3~~ Enhanced Security and Privacy===		==== Enhanced Security and Privacy ====
	Data transmitted over the cloud is susceptible to breaches. Edge computing reduces exposure by keeping sensitive data closer to the source, thereby lowering the risk of cyberthreats. This is particularly important in healthcare and financial sectors, where data privacy is crucial.		Data transmitted over the cloud is susceptible to breaches. Edge computing reduces exposure by keeping sensitive data closer to the source, thereby lowering the risk of cyberthreats. This is particularly important in healthcare and financial sectors, where data privacy is crucial.

	===~~1.2.4~~ Scalability and Cost Efficiency===		==== Scalability and Cost Efficiency ====
	Deploying edge computing reduces infrastructure costs by minimizing the need for high-bandwidth connectivity and extensive cloud storage. Additionally, edge nodes can be deployed dynamically based on application needs, offering a scalable solution.		Deploying edge computing reduces infrastructure costs by minimizing the need for high-bandwidth connectivity and extensive cloud storage. Additionally, edge nodes can be deployed dynamically based on application needs, offering a scalable solution.

			=== 1.2.2 Benefits ===
			While edge computing offers numerous advantages, it also presents challenges such as:

	~~===Benefits===~~		* Infrastructure Complexity: Managing distributed edge nodes requires sophisticated coordination and maintenance.
	~~While edge computing offers numerous advantages, it also presents challenges such as:~~		* Security Vulnerabilities: Edge devices, if not properly secured, can become points of failure.
	• Infrastructure Complexity: Managing distributed edge nodes requires sophisticated coordination and maintenance.		* Interoperability Issues: Different vendors and technologies may create integration challenges.
	• Security Vulnerabilities: Edge devices, if not properly secured, can become points of failure.		* Power and Resource Constraints: Edge devices must balance performance with power consumption.
	• Interoperability Issues: Different vendors and technologies may create integration challenges.
	• Power and Resource Constraints: Edge devices must balance performance with power consumption.

	Edge computing is essential for modern digital ecosystems, enabling real-time processing, bandwidth efficiency, security enhancements, and cost-effective scalability. As industries increasingly adopt edge technologies, addressing its challenges will be crucial to maximizing its potential.		Edge computing is essential for modern digital ecosystems, enabling real-time processing, bandwidth efficiency, security enhancements, and cost-effective scalability. As industries increasingly adopt edge technologies, addressing its challenges will be crucial to maximizing its potential.

	~~[[File:Screenshot 2.png\|450px\|thumb\|center\|Fig2:Basic edge computing]]~~

	== 1.3 Edge Computing application Domains and Typical Applications ==		== 1.3 Edge Computing application Domains and Typical Applications ==

Vrunda Kadam: /* 1.3.2 Autonomous Vehicles & Transportation */

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1.3.2 Autonomous Vehicles & Transportation

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	== 1.3 Edge Computing application Domains and Typical Applications ==		== 1.3 Edge Computing application Domains and Typical Applications ==
	Edge computing processes data closer to the source, reducing latency, bandwidth usage, and reliance on cloud computing. This approach is transforming various industries by enabling real-time analytics, automation, and enhanced decision-making. Below is a detailed explanation of each domain and how edge computing is applied within them.		Edge computing processes data closer to the source, reducing latency, bandwidth usage, and reliance on cloud computing. This approach is transforming various industries by enabling real-time analytics, automation, and enhanced decision-making. Below is a detailed explanation of each domain and how edge computing is applied within them.
	=== 1.3.2 Autonomous Vehicles & Transportation ===		=== 1.3.1 Autonomous Vehicles & Transportation ===
	The transportation sector requires real-time data processing for safety, efficiency, and automation. Autonomous vehicles, smart traffic systems, and connected infrastructure rely on low-latency computing to function effectively.		The transportation sector requires real-time data processing for safety, efficiency, and automation. Autonomous vehicles, smart traffic systems, and connected infrastructure rely on low-latency computing to function effectively.
	==== Platooning:====		==== Platooning:====

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1.3.1.1 Platooning:

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	=== 1.3.2 Autonomous Vehicles & Transportation ===		=== 1.3.2 Autonomous Vehicles & Transportation ===
	The transportation sector requires real-time data processing for safety, efficiency, and automation. Autonomous vehicles, smart traffic systems, and connected infrastructure rely on low-latency computing to function effectively.		The transportation sector requires real-time data processing for safety, efficiency, and automation. Autonomous vehicles, smart traffic systems, and connected infrastructure rely on low-latency computing to function effectively.
	====~~1.3.1.1~~ Platooning:====		==== Platooning:====
	Platooning refers to a group of vehicles, typically trucks, traveling closely together in a synchronized convoy. This approach minimizes wind resistance and saves fuel. Edge computing plays a vital role here by processing sensor data from radar, cameras, and LIDAR in real-time, allowing the vehicles to maintain tight coordination. Using edge devices, the trucks communicate with each other instantly without relying on distant cloud servers. This ultra-low latency ensures synchronized braking and acceleration, significantly improving road safety and fuel efficiency.		Platooning refers to a group of vehicles, typically trucks, traveling closely together in a synchronized convoy. This approach minimizes wind resistance and saves fuel. Edge computing plays a vital role here by processing sensor data from radar, cameras, and LIDAR in real-time, allowing the vehicles to maintain tight coordination. Using edge devices, the trucks communicate with each other instantly without relying on distant cloud servers. This ultra-low latency ensures synchronized braking and acceleration, significantly improving road safety and fuel efficiency.

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1.3.1.2 Smart Traffic Infrastructure:

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	Platooning refers to a group of vehicles, typically trucks, traveling closely together in a synchronized convoy. This approach minimizes wind resistance and saves fuel. Edge computing plays a vital role here by processing sensor data from radar, cameras, and LIDAR in real-time, allowing the vehicles to maintain tight coordination. Using edge devices, the trucks communicate with each other instantly without relying on distant cloud servers. This ultra-low latency ensures synchronized braking and acceleration, significantly improving road safety and fuel efficiency.		Platooning refers to a group of vehicles, typically trucks, traveling closely together in a synchronized convoy. This approach minimizes wind resistance and saves fuel. Edge computing plays a vital role here by processing sensor data from radar, cameras, and LIDAR in real-time, allowing the vehicles to maintain tight coordination. Using edge devices, the trucks communicate with each other instantly without relying on distant cloud servers. This ultra-low latency ensures synchronized braking and acceleration, significantly improving road safety and fuel efficiency.

	==== ~~1.3.1.2~~ Smart Traffic Infrastructure: ====		==== Smart Traffic Infrastructure: ====
	Smart traffic systems use edge-enabled devices embedded within traffic lights, intersections, and pedestrian crossings to analyze traffic flow and pedestrian movement. By processing video and sensor data locally, these systems dynamically adjust traffic signals, detect jaywalkers, or prioritize emergency vehicles. Edge computing reduces reliance on cloud networks, enabling rapid decision-making and improving road safety and traffic efficiency.		Smart traffic systems use edge-enabled devices embedded within traffic lights, intersections, and pedestrian crossings to analyze traffic flow and pedestrian movement. By processing video and sensor data locally, these systems dynamically adjust traffic signals, detect jaywalkers, or prioritize emergency vehicles. Edge computing reduces reliance on cloud networks, enabling rapid decision-making and improving road safety and traffic efficiency.

Vrunda Kadam: /* 1.3.2.1 Visual Quality Inspection: */

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1.3.2.1 Visual Quality Inspection:

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	===1.3.2 Industrial IoT (IIoT) & Manufacturing ===		===1.3.2 Industrial IoT (IIoT) & Manufacturing ===
	Factories and industrial plants increasingly integrate IoT devices for automation, quality control, and predictive maintenance. These operations require real-time decision-making to minimize downtime and optimize efficiency.		Factories and industrial plants increasingly integrate IoT devices for automation, quality control, and predictive maintenance. These operations require real-time decision-making to minimize downtime and optimize efficiency.
	====~~1.3.2.1~~ Visual Quality Inspection: ====		==== Visual Quality Inspection: ====
	High-resolution cameras capture product images. Instead of uploading gigabytes of images to the cloud, edge devices run AI models to catch defects immediately—critical for high-speed production lines.		High-resolution cameras capture product images. Instead of uploading gigabytes of images to the cloud, edge devices run AI models to catch defects immediately—critical for high-speed production lines.

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1.3.2.2 Digital Twins:

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	High-resolution cameras capture product images. Instead of uploading gigabytes of images to the cloud, edge devices run AI models to catch defects immediately—critical for high-speed production lines.		High-resolution cameras capture product images. Instead of uploading gigabytes of images to the cloud, edge devices run AI models to catch defects immediately—critical for high-speed production lines.

	====~~1.3.2.2~~ Digital Twins: ====		==== Digital Twins: ====
	A digital twin is a real-time digital replica of a physical asset or system. In industrial settings, edge devices gather data from machines and feed it into a simulated model, which mirrors the behavior of the real-world counterpart. This enables operators to monitor performance, diagnose issues, and even simulate future scenarios on the fly. Edge computing makes these digital twins more responsive, as data is processed locally, offering real-time feedback and facilitating predictive maintenance and operational efficiency.		A digital twin is a real-time digital replica of a physical asset or system. In industrial settings, edge devices gather data from machines and feed it into a simulated model, which mirrors the behavior of the real-world counterpart. This enables operators to monitor performance, diagnose issues, and even simulate future scenarios on the fly. Edge computing makes these digital twins more responsive, as data is processed locally, offering real-time feedback and facilitating predictive maintenance and operational efficiency.

Vrunda Kadam: /* 1.3.2.3 Worker Safety: */

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1.3.2.3 Worker Safety:

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	A digital twin is a real-time digital replica of a physical asset or system. In industrial settings, edge devices gather data from machines and feed it into a simulated model, which mirrors the behavior of the real-world counterpart. This enables operators to monitor performance, diagnose issues, and even simulate future scenarios on the fly. Edge computing makes these digital twins more responsive, as data is processed locally, offering real-time feedback and facilitating predictive maintenance and operational efficiency.		A digital twin is a real-time digital replica of a physical asset or system. In industrial settings, edge devices gather data from machines and feed it into a simulated model, which mirrors the behavior of the real-world counterpart. This enables operators to monitor performance, diagnose issues, and even simulate future scenarios on the fly. Edge computing makes these digital twins more responsive, as data is processed locally, offering real-time feedback and facilitating predictive maintenance and operational efficiency.

	====~~1.3.2.3~~ Worker Safety: ====		==== Worker Safety: ====
	Industrial environments often pose risks to workers due to hazardous machinery or toxic substances. Edge computing is employed in wearable technologies like smart helmets and vests that monitor workers’ vital signs, movements, and environmental conditions such as gas levels. When abnormalities are detected, edge devices can immediately send alerts to supervisors or trigger emergency protocols. This real-time processing capability is essential for accident prevention and ensures rapid response to potential threats.		Industrial environments often pose risks to workers due to hazardous machinery or toxic substances. Edge computing is employed in wearable technologies like smart helmets and vests that monitor workers’ vital signs, movements, and environmental conditions such as gas levels. When abnormalities are detected, edge devices can immediately send alerts to supervisors or trigger emergency protocols. This real-time processing capability is essential for accident prevention and ensures rapid response to potential threats.