Focus on Edge Intelligence
Our day begins with our smartphones and ends with smart televisions, and in the middle, we use countless other technological provisions which we don’t even remember. With the advent of the internet, the world is more connected than ever. Throughout the day, we generate, use, and transmit a significant amount of data. In the year 2022, the global volume of data is forecasted to be 97 zettabytes (ZB), and by 2025 it is likely to reach 181 ZB. This data explosion will be the result of growth in IoT devices that is projected to reach 24.1 bn in 2030, which is 3 times its number in 2019. Be it in households or in industrial settings, data underpins modern life. So much so that the data is said to be the new gold.
Conventionally, data was stored locally and was managed by the data owner, but with the advancement of cloud computing technologies, the process of storing, securing, and managing the data revolutionized. However, the enormous generation and transmission of data exercises pressure on the network infrastructure as it requires high energy and is affected by latency. To ease this pressure from the cloud, networking and computing capacity should be closer to the locations of data generation. This also results in effectiveness in managing and processing data, and here is where edge computing comes into the picture.
Edge computing is "storing, processing, and analyzing data" in close proximity to the end devices, which are said to be at the "edge" of the network. With the help of edge strategy, businesses have gained significant benefits like enhancement in monitoring, response time, and site reliability. Consequently, Edge Computing has become a huge market that is likely to have a global revenue of USD 250.6 bn by the year 2024. However, this is a significant difference between Edge Computing and Edge Intelligence.
What is Edge Intelligence, and what are its benefits?
?????????Edge Computing + AI = Edge Intelligence
Edge intelligence refers to edge computing based on artificial intelligence. While edge computing is an extension of cloud computing to push cloud services to the proximity of end-users, on the other hand, edge intelligence pushes deep learning computations from the cloud to the edge at its best.
This results in more effective and intelligent services. Some of the benefits of edge intelligence are as follows:
●?????The problem of latency solved - With cloud computing, systems are centralized and suffer from the problem of latency. Latency means long delays in capturing and processing data. These delays act as obstacles when real-time decisions are to be made. High latency also results in lower communication bandwidth. Edge intelligence, unlike cloud computing, functions at low latency as data is processed near the source location itself.
●?????Low bandwidth required for data storage - For an IoT device to function efficiently, high bandwidth is required as data is transmitted to the centre from all the edge sources where the raw data is collected. As these IoT models increase, bandwidth requirements also increase. Additionally, devices located at remote locations might not even have sufficient bandwidth to transmit data. At a low bandwidth, edge devices themselves can capture and process data instead of sending and receiving data and instructions back and forth. Edge computing helps in taking real-time decisions and actions. It can store the collected data and reports, which can be collected later on.
●?????Decrease in Operational Costs - Edge devices are responsible for processing local data in real-time. Since cloud space is not utilized for the storage of local data, the central or cloud system gets more space for content-rich and relevant data. This results in a reduction of operational costs. Moreover, real-time decisions and actions by intelligent edges help professionals efficiently deal with all devices. IT professionals can work remotely with improved quality, speed and storage.
●?????Linear Scalability - Edge intelligence enables linear scalability as and when more IoT devices are deployed. It is capable of executing the heavy machine learning models reducing the burden of central systems in effect. The edge architectures can scale linearly and perform intelligent tasks and functions.?
How and When is Edge Intelligence being used?
For time-sensitive businesses, it is crucial that the data is processed quickly and adequately. And thus, for them deploying Edge Intelligence is critical as it avoids any delay and optimizes the web applications. The use-cases of edge computing were significant, but the growth of AI and machine learning capabilities have expanded the range of use cases for edge intelligence as well.
Edge intelligence has found its applications in different aspects of society. Industries are inclining toward integrating edge intelligence with their systems to realize its full potential with AI. Faster deployment and enhanced efficiency motivate different industries to utilize the tools of Edge intelligence.
Broadly, there are three categories of Edge Intelligence:
-???????Operational technology (OT) edges are conventionally limited to connectivity and computing. Applications include power plants and offshore oil rigs.
-???????IT edges that focus on distributed data processing, and the common applications are in telecom.
-???????IoT edges have gained enormous popularity and, in many cases, are a combination of OT and IT edges.
Source: IIOT-WORLD
Edge intelligence has found its uses in several societal aspects and industries like;
-???????Healthcare
The healthcare industry captures patient data by using sensors or machines. This data is later used for determining the proper treatment for the patient. Edge intelligence has made the analysis of this data very easy. By incorporating AI, edge intelligence has made data collection easier along with taking real-time action to resolve the issues in minimum responding time. Also, the integration of edge computing with edge intelligence has changed the representation and analysis of this data by making it more effective and efficient.
Edge Intelligence has specifically helped in diet monitoring of the patients, skin disease detection and treatment for diabetes. The application of edge intelligence can be seen in the pandemics as well. For instance, during Covid-19 numerous IoT-based smart architectures have been recommended for adequate screening, maintaining a 1-meter social distance, and the diagnosis of the symptoms.
-???????Public transit
Edge intelligence can help in reducing the cost of transport and logistics. With an accessible collection of data, it can help in finding the required information about the passengers and locations. With Edge intelligence, the authorities can track down the vehicles and get the correct arrival time, delay time, location, etc. It also enables fleet management with higher efficiency and optimized costs.
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-???????Industrial manufacturing
Industries require advanced data collection and analysis applications to utilize this information in their product or process development. Edge intelligence enables the industries to get complete automation with sensors, real-time monitoring, advanced dashboard representation of data, detailed and accurate time analysis, etc. In the Oil and Gas industry, pipeline safety and volatile prices are of utmost concern. Edge Intelligence help in addressing challenges of price movements, workers' safety, remote assets monitoring, etc. Moreover, through edge intelligence, the order picking process can be greatly improved as it offers real-time availability of an item's location and detects the nearest available asset.
-???????Government systems
Government systems must collect the citizens' data for making policies, research, etc. This becomes easier with Edge intelligence. It helps in collecting real-time data about the people to a considerable extent. This data can be utilized in utility management, public health care, transport, infrastructure, etc.
-???????Retail industry
The retail industry's edge intelligence helps manage advanced collection and data management. This enables the retail units to understand the issues faster and take preventive measures.
Challenges of Edge Intelligence
Edge intelligence has seen its application in different aspects of the industrial and societal atmosphere. It has an evident advantage over traditional data collection, analysis, and management methods. However, there are still a few challenges that edge intelligence faces. These can be some loopholes of lacking points of edge intelligence that need attention to realize its full potential. These challenges are;
●?????Limitation of Bandwidth
The application of edge intelligence comes with limited bandwidth and similar bottleneck restrictions. This creates issues in data transfer after collection from one point to another. The limited bandwidth also limits the quantity of data exchange, volume, and efficiency. Ultimately, this impacts the overall performance of the system. The system comes with higher efficiency, but due to limited data transfer, the utilization of the system also gets restricted, resulting in low productivity. This is especially challenging for larger systems with multiple data collection sensors.
●?????Advanced planning
The continuous developments in the industries also require faster adaptability in AI systems. However, AI processor chips are garnered with long deployment times. This creates a hindrance in the adaptability of the AI system of edge intelligence. The adaptability requires higher scalability scope with much better flexibility. This means the system needs to respond quickly to the changes. However, a longer deployment duration decreases the responding time. Also, this decreases the ability of the system to work with neural systems, which are still in the process of completion. This limitation restricts the expansion of edge intelligence in different fields for now.
●?????Requirement of highly secured systems
Security is the primary concern in Edge intelligence systems. The systems are being used for life-critical processes. They are used in transport, healthcare, manufacturing, etc. The slightest delay in right responding can cause severe damage in these domains. That’s why security issues, especially in automation applications of edge intelligence, are a significant concern.
Conclusion
Edge intelligence is still in its nascent stage. Although there are some headwinds, edge intelligence is transforming the computing landscape and driving businesses towards operational efficiency and the future of connectivity. By taking powerful computing capabilities in close proximity to where data generates and needs to be processed, edge intelligence has given the opportunity for faster, cost-effective, and more secure operations. This proposes the prospect of significant developments and evolutions in the upcoming years.
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2 年Hi Hrishabh, It's very interesting! I will be happy to connect.