The Ideal Scenarios for using Edge Computing Solutions: As technological advancements continue to evolve, the significance of edge computing is on the rise, fundamentally altering the way we handle real-time data.
Understanding of Edge Computing
The primary focus of edge computing lies in processing data in close proximity to its source, thereby achieving higher speeds and volumes, resulting in real-time, action-oriented outcomes.
In simpler terms, edge computing entails delivering data closer to the device or source to facilitate real-time access and streamline data execution processes.
The global “Edge Computing Market” has experienced steady and robust growth in recent years, with projections indicating continued optimistic progression until 2032.
Notable trends within the Edge Computing market include a growing preference for sustainable and eco-friendly products, as well as an increasing integration of technology to enhance product quality and efficiency.
Edge computing assesses user performance over the internet and employs analytics to pinpoint the most dependable and low-latency network route for each user’s data. By optimizing the network accordingly, it enhances overall performance.
The potential applications of edge computing span across a wide array of fields, each presenting opportunities for game-changing contributions. Some of these scenarios include…
Ideal Scenarios for using Edge Computing Solutions in Industry and Manufacturing
Industries and manufacturing have numerous ideal scenarios for using edge computing solutions to achieve faster and real-time results
Industries are now able to have more realistic (real-time) data for analysis thanks to the introduction of edge computing. The edge devices solutions have lowered latency and are more realistic in handling the arising issues.
Real time Monitoring and Control
With the deployment of edge computing devices on the factory floor, data can be processed in real-time, allowing for faster decision-making and better resource allocation.
IOT applications can benefit from using edge computing to monitor machines, increase production, and prevent system downtime.
Predictive Maintenance
By utilizing edge analytics, data can be analyzed at the time of generation, which leads to a decrease in latency in the decision-making process of the connected device.
For an instance If a sensor from a manufacturing system detects the likelihood of a particular part failing, business rules built into the analytics algorithm interpreting the data at the network edge can automatically shutdown the machine and notify plant managers to replace the part.
By doing this, organizations can potentially minimize or prevent unplanned equipment downtime, which can save time compared to transmitting data to a central location for processing and analysis.
Autonomous Vehicles and Transportation
The ability to analyze all the necessary driving data in real-time is crucial for operating safely and reliably. The cloud’s real-time analysis of data can pose challenges. Unsafe delays are caused by the large amount of data generated by autonomous vehicles and the risk of latency or insufficient connectivity when sending data to the cloud.
Cloud-based data handling has many disadvantages that create ideal scenarios for using edge computing solutions. Edge computing facilitates the handling of this staggering amount of data, and real-time data availability for analysis at the spot is achievable.
With edge computing, autonomous cars can make faster and more accurate decisions based on real-time data, reducing the latency that is typically associated with cloud computing.
By doing so, navigation can be more precise, obstacles can be detected more accurately, and traffic flow can be managed better.
Low latency Decision-Making
Real-time decision-making is necessary for autonomous vehicles because they have crucial decisions that could mean the difference between life and death.
This proximity reduces the time it takes for data to travel to a centralized data center and back, thereby reducing latency.
Low Latency: Edge computing’s primary goal is to reduce latency, guaranteeing that data processing and decision-making can occur in near real-time.
Data processing at the edge
Edge computing can continuously monitor various parameters of the vehicle, such as ambient temperature, mileage, tire inflation, braking, acceleration, and speed/force.
The analytics model is able to determine if any component or part is likely to fail and notify the vehicle owner accordingly.
For instance, if the tire pressure or condition is not at a safe level. Notification/reminders to perform tire maintenance or replacement will be given by the Edge device to the vehicle owner.
Ideal Scenarios for using Edge Computing Solutions in Health and Tele-medicines
The healthcare industry is embracing machine learning, AI, augmented and virtual reality for patient care and training purposes at a rapid pace. Real-time data processing capabilities are necessary for all of these technologies to effectively produce useful and informative outputs in healthcare.
Due to the high costs of moving large quantities of data to a central cloud network, it is necessary to consider networking limitations and latency. This has created ideal scenarios for using edge computing in their existing systems.
Edge computing is a new frontier in healthcare systems, one that is being fueled and enabled by new mobile and point-of-care technologies.
With continuous advancements, technologies such as 5G, quantum computing and IoT, combined with edge computing and analytics, will help to considerably improve processes within IoT applications by enabling significantly low latency.
This will provide new chances to improve the functional, medical, and financial value of the healthcare system.
Read Also: Practical Application of Edge computing in health & Tele-medicine
Remote Patient Monitoring
The healthcare industry requires low-latency, remote, and real-time response solutions for different scenarios, including pop-up clinics, cancer-screening centers, patient-monitoring systems like pacemakers and insulin pumps.
Edge computing is the solution to this problem. A distributed system known as edge computing brings data storage and computation closer to data sources, such as IoT devices.
This takes away the need for central data storage and processing systems. Thus, real-time data analysis is not affected by slow network speeds and latency issues, thereby saving bandwidth.
Emergency Response System
Emergency doctors are usually only given a short description of the patient by paramedics in the current emergency care system. Thus, such patients can only receive relevant diagnostic treatments once the ambulance arrives at the hospital.
This can cause problems and delays in the transfer of patients to the correct wards, which could lead to a delay in their diagnosis. In emergency cases, such delays can result in fatal consequences
Edge computing at the network edge (together with 5G) can enable better and more precise treatment by on-site paramedics, as well as transmit more specific details on the status and location of patients arriving to the hospital owing to its low latency, mobility and data-processing capabilities.
To understand how important edge computing is in healthcare, you can refer to: https://www.pwc.in/assets/pdfs/emerging-tech/edge-computing-in-healthcare.pdf
Smart Cities and Infrastructure
A smart city’s purpose is to improve services for citizens and achieve greater efficiency, cost savings, safety, sustainability, and overall well-being in daily life.
Some have managed to create spaces inside the city where both humans and nature thrive (as evidenced by Singapore’s iconic Gardens by the Bay). Edge computing aids smart cities in achieving these advantages by enabling 5G and IoT systems to work more efficiently, essentially shaping and powering them.
There is a vast range of scenarios for using edge computing in smart cities and infrastructure.
Traffic Management
The growth of urban areas presents numerous challenges, and managing vehicular traffic is one of them. It hampers smooth traffic flow, wastes time, and threats of road safety.
The data analysis services are equipped with scenarios to utilize Edge Computing technology in urban spaces offer real-time robust and smart solutions to any challenges that urbanization may present.
Hence, The development of a smart traffic management system based on Edge Cloud-centric IoT is intended to predict traffic inflows and optimize the time-optimized smart navigation of vehicles. Long queues and congestion at intersections are avoided by the traffic inflow prediction that adjusts the traffic movement phase accordingly.
Public safety and security
Preventative measures, such as those provided in the traffic management model above, can help prevent accidents, as the saying goes “prevention is better than cure”.
Intelligent navigation enables the optimal distribution of traffic to possible paths and consequently enhances road safety at intersections also ensuring public safety and security.
By presenting scenarios that use edge computing in daily traffic issues, edge computing has the potential to revolutionize traffic management control, and safety methods.
This subject can also be further investigated in the research paper “ Smart vehicular traffic management: An edge cloud centric IoT based framework” – ScienceDirect
Retail and customer Experience
Edge computing in retail enhances shopping experiences for customers by making it easier, faster, and more personalized.
Using edge computing in retail is similar to using a minicomputer in the store instead of sending all the data to a large computer. This minicomputer called an “edge device”, aids in tasks such as quickly checking inventory, ensuring accurate prices, and monitoring customers’ preferences.
Using sensors, you can connect your devices to track customer behavior and preferences, making the shopping experience more engaging by providing more personalized recommendations based on individual tastes.
Retail and customer experience can benefit from using edge computing solutions in many scenarios. Some of them are listed below, but there are many more possibilities.
In Store Analytics
Imagine if you were in a store and you wanted to check if they have a particular item in stock. Rather than awaiting an answer from the store’s main computer, the edge device in the store can provide an immediate response. This enhances the speed and ease of shopping.
The store can provide an immediate answer to customer inquiries about item availability by performing a real-time inventory check. This improves efficiency and improves the shopping experience.
Inventory management
Edge computing is a tool for retailers to maintain accurate and up-to-date inventory in real-time. Sensors and edge devices that are placed throughout the store continuously monitor stock levels and trigger reorders when items are in short supply.
By doing this, they ensure that they always have the things you want to buy and don’t have excess inventory that can’t be sold, which ultimately saves costs and improves customer satisfaction.
Edge Computing in Smart Agriculture:
In Smart Agriculture, farmers leverage edge-computing solutions to revolutionize traditional farming practices. This cutting-edge approach empowers farmers with unparalleled insights into their crops and livestock. Equipped with specialized tools and gadgets, they meticulously monitor various parameters such as soil moisture, temperature, and crop health across different sections of their farm.
Armed with this data, farmers can make informed decisions in real-time, optimizing resource allocation such as water and fertilizers. By strategically timing irrigation and fertilization, they enhance food production while minimizing the usage of water and chemicals, promoting sustainability.
Picture a farm adorned with sophisticated sensors capable of assessing soil moisture levels, temperature variations, and crop vitality. Instead of transmitting this wealth of information to remote data centers, smaller on-site computers analyze the data swiftly and autonomously. These localized computing units swiftly detect issues like pest infestations or dehydration without relying on internet connectivity or distant servers.
By harnessing these decentralized computing capabilities within the farm premises, farmers expedite decision-making processes, reduce reliance on internet connectivity, and efficiently manage agricultural operations. It’s comparable to having a cognitive hub right amidst the fields, facilitating optimal farm management and productivity.
Conclusion
The development of edge computing is leading to more efficient distributed computing. Edge computing’s primary objective is to offload computations near edge devices. Edge computing is being embraced by numerous organizations across various fields due to its usefulness and productivity.
More innovative applications of edge computing are emerging to make human’s life safer and more by providing support for multiple devices like secure smart homes, automated vehicle insurance, safer remote surgeries, and so on.
Edge is becoming an emerging technology to meet the current requirements of increased data production. In all fields related to networks, edge computing has already fulfilled all the structures of transmissions, substations, energy productions, energy usages, energy supply, and transmittance.
In conclusion, edge computing is the foundation of the future’s hope and ideal scenarios for using edge computing will also increase gradually due to its faster processing of data for decision making in real time.
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