The Development of Edge Computing: The New Era of Internet of Things
The world has changed due to the internet and cloud computing and the traditional ways of storing, processing, and accessing data has gone through transformation. However, the emergence of smart devices, IoT, AI, and real-time applications, the cloud computing model is continuously challenged by traditional monitors and processes due to latency, bandwidth, and security issues.
In this piece, we will answer what edge computing is, how it's done, its advantages and the technology’s real world applications alongside how the future might look like because of it.
What is Edge Computing?
Edge Computing is known for being a somewhat new development in computing infrastructure which brings data closer to the processing edge devices and users as opposed to solely generic cloud servers. Data is processed locally on edge devices, routers, or nearby servers.
How Does Edge Computing Work?
Traditional Cloud Model – A smart device such as a smart camera captures data and transmits it to a server in the cloud, where processing occurs, but the results contain more latency than needed.
Edge Computing Model – Rather than shipping data off to a remote cloud, edge computing allows for a camera to process information locally or on a nearby edge server which saves time retrieving information.
Key Components of Edge Computing
Edge Devices – Any smart device that produces data. These devices can include smartphones, sensors, and cameras, along with IoT gadgets.
Edge Servers – Local routers, servers, or small data centers that are capable of processing data closer to the source.
Cloud Integration – Edge computing does rely less on the cloud, but integrates with it for storing and analyzing data.
Why is Edge Computing Gaining Popularity?
Reduced Latency & Faster Processing – In modern applications such as autonomous vehicles, smart cities, and healthcare, milliseconds count. If real time data is sent to a cloud server for processing, it is detrimental, as the cloud server is located thousands of miles away.
In contrast to traditional means, edge computing enables data to be processed locally. This results in a significant reduction in latency, which greatly improves response times.
Example:
Self-Driving Cars - Accidents can occur if even a few milliseconds of analysis delay occurs while processing sensor data. Edge computing maintains a balance of effective decision making and timing.
- Bandwidth Optimization & Cost Reduction
Watching the videos of a channel consumes data at an instant while uploading the videos on the cloud increases the expenses for both businesses and service providers.
With edge computing, data is worked on locally, eliminating the necessity to send unimportant data to the cloud. Only the data that is required is sent to the cloud which helps in saving expenses on bandwidth and reduction in the expenses on cloud storage.
Example:
Smart Surveillance Cameras - Rather than constantly transmitting a video feed in high resolution to the cloud, edge computing allows the cameras to be able to analyze video footage in the devices themselves, and only alert for suspicious activity.
- Improved Security & Data Privacy
In a traditional cloud system, sensitive data is prone to send into various remote servers, making them vulnerable to cyberattacks, breaches, and unauthorized access.
With edge computing, there are always regulations placed to control access to sensitive data where data processing is done locally meaning that no data is opened to external networks. Security will always be improved and compliance with laws like GDPR will also be confirmed.
Example:
Healthcare IoT Devices – Wearable health monitors process healthcare IoT from the patients will be done locally with the insights anonymized when the data is sent to the cloud servers. This minimizes mortality of security risks.
- Enhancing The Expansion of IoT Devices
Cloud computing by itself would be unable to efficiently handle the influx of information knowing billions of IoT devices are predicted to be connected in the following years.
Edge computing allows IoT devices work smoothly without burdening the cloud infrastructure because the computing power is spread across several locations.
Illustration:
Smart Homes & Smart Cities – Edge computing facilitates the instantaneous processing of smart thermostats, smart lighting, smart traffic controls and environmental monitoring systems.
Edge Computing Use Case Scenarios
- Autonomous Vehicles and Smart Transport
Smart transportation systems and self-driving cars functions fully through real-time analysis for quicker and safer results.
Edge computing provides assistance to vehicles enabling them to instantly process data from sensors, thus lessening the reliance on distant cloud servers.
Traffic controllers utilize real time data to manage traffic effectively to alleviate congestion and accidents.
- Healthcare and Patients Monitoring Remotely
Edge computing enables mobile medical data transmission without violating patient confidentiality, Imagine a world where privacy and medical care coexist.
Wearable health devices (smartwatches, fit bands) share processed insights with doctors after analyzing the data locally.
Edge computing boosts the speed of medical diagnostics, robotic ministrations, and patient supervision for smart hospitals.
- Industrial and Automated Smart Factories
Edge computing allows manufacturing industries to carry out greater energy efficiencies, maintenance around machines, and even optimize their production lines.
IIoT or industrial IoT sensors help in monitoring processes on machines and registers a malfunction immediately, thus, it minimizes the period for which the operations are halted and maximizes the productivity.
Automated inspection systems analyze images at the site, detecting faulty items immediately.
- AR & VR
Edge computing improves augmented reality and virtual reality functionalities by decreasing delay and enhancing the speed of graphics processing.
Gaming & Entertainment – With VR and AR games, graphics are processed on site instead of the far away cloud servers increasing the user experience.
Retail & E-Commerce – Augmented reality shopping applications enable a user to see the items in the store and how they appear on them instantly.
- Smart Cities & Environmental Monitoring
Edge computing helps in the formation of smart cities by providing real time data processing for the urban facilities and services.
The traffic control systems using edge computing optimize the timing of signals depending on the actual traffic situation.
Pollution detectors exam the emission parameters of the air at the certain location and transmit a signal if the quantitation is dangerous.
Challenges & Limitations of Edge Computing
Despite its advantages, edge has its challenges as well:
High Capital Expenditure – The deployment of edge servers and local processors requires spending money.
Data Maintenance – Ensuring consistency of data between edge devices and cloud systems is challenging.
Security Risks at the Edge – Even though localized computing improves privacy, devices at the edge can still be attacked remotely. Edge AI devices may pose additional threats.
Limited Processing Power – Edge devices may not be as powerful as centralized cloud data centers, limiting their capabilities for complex tasks.
The Future of Edge Computing
Apart from IoT and AI development, the growing need for real time apps will also contribute to the significance of edge computing.
What’s Next for Edge Computing?
Integration of 5G with edge computing – More advanced wireless networks will enhance edge computing and make real time apps more efficient.
Edge AI – AI based edge devices will allow eliminating dependency on AI cloud models for making decisions.
Increased deployment of edge data centers – More companies will establish edge data center to back up the edge computing infrastructure.
Energy efficient edge devices – New low power computing devices will make edge computing more efficient.
Conclusion
With the help of edge computing, companies are able to improve response times when they're needed as well as bandwidth consumption, which at the same time allows for more security and effective IoT management. This ultimately leads to the alteration of data processing.
The EEG, AR/VR and automated industries, along with self-driving cars and nano robots are at the fingertips of edge computing technological advancement.
As numerous industries implement edge computing, it will continue to grow and improve how we interact with the virtual world in terms of speed, intelligence, and efficiency.