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How can 5G and edge computing help the development of smart cities?

Author:QINSUN Released in:2024-01 Click:102

At a time of changing development environment, we have the opportunity to rethink our city. During the pandemic, we rely more on local communities and technology, and these two efforts will work together to create smarter cities. As more and more people become aware of the reality of climate change, future cities may have vastly different energy and transportation settings.

All automatic transportation and many technologies of future smart cities will depend on 5G and edge computing. The latter is essentially a technical version that we have been working on throughout the pandemic, relying on nearby situations. This can improve resilience and add more information to the system to create smarter cities.

5G networks provide higher cell density, higher data speed, and lower network latency. In 5G, more processing is pushed to the network edge, enabling the implementation of low latency applications. In addition, the density of community sites provides consumers with increased network capacity, more data bandwidth, and higher mobile data speeds. This network density will enable advanced analysis for real-time decision-making. The application of 5G technology can help cities save money and resources, and create cleaner, safer, and healthier living spaces for people.

5G and edge computing go hand in hand. 5G increases the amount of data that can be communicated, while edge computing uses data to run calculations locally instead of sending data to other places for analysis and processing. This is usually faster and more resistant to damage. Combined, these two technologies have broad prospects and are outstanding emerging technologies today.

According to IDC's research, the number of 5G connections will grow to 1.01 billion by 2023, and the expenditure on edge computing will reach 250 billion dollars by 2024. This provides a huge ecosystem opportunity to transform cities by injecting next-generation technology.

Smart cities aim to improve the quality of life for residents. Key technologies such as the Internet of Things (IoT), blockchain, artificial intelligence, and analytics can be utilized to cover the entire range, including waste management, smart parking, e-government, electricity and public lighting, education, health, traffic management, and smart buildings. The combination of edge computing, 5G functions and industrial IoT devices has laid the foundation for the effective use of technology, and it is possible to realize a more intelligent supply chain and enable us to better cope with service interruptions.

Public utilities and infrastructure

Edge analysis can help smart city municipalities better manage and save valuable resources, including energy, water, and fresh air. The analysis function based on IoT sensors in water systems and waste management systems can achieve better monitoring and management, while innovative power grids can improve energy efficiency for businesses and consumers. Edge analysis also helps to monitor and control the operation of buildings, such as heating, ventilation, air conditioning, lighting, and safety, automatically achieving a good living environment in a virtual way.

Economic Development and Citizen Management

By using IoT sensors on 5G networks, traffic flow, parking space availability, utility usage, and public street light management can be monitored. The regulatory authorities can use edge analysis to find practical solutions to save energy, optimize water and electricity resources, and reduce environmental impacts. During the pandemic, we have seen some trends where people move from crowded cities to areas with smaller populations and better services. Using technology to minimize traffic congestion and improve waste management can attract new residents and increase economic opportunities within the community.

The advantages of edge computing may extend to all aspects of smart cities:

Public safety and crime control

Edge analysis and edge AI enable advanced and secure video, sensor, and communication systems to actively monitor public places, as well as law and order. By using edge AI, crime or other catastrophic events can be prevented or mitigated before endangering public safety. Sensors embedded in critical infrastructure such as bridges and power plants can monitor structural data to identify potential hazards and protect the economic well-being of citizens and cities. Drones equipped with sensors can monitor vehicle traffic, crowds, construction sites, and disaster areas to help continuously monitor the situation and support emergency personnel. Overall, communities benefit from trust in law enforcement and disaster management.

Intelligent transportation and autonomous driving intelligent vehicles

Edge analysis will become the key driving force for the revolution of interconnected autonomous vehicle. Road vehicles will communicate with each other and infrastructure, and improve overall road safety. It will also reduce traffic congestion and improve driver comfort.

The 'vehicle to cloud' solution based on edge computing provides edge cloud functions for different levels of automated driving through different services (such as high-definition real-time maps, real-time traffic monitoring and alerts). The distributed AI application in the car will send the video data to the edge computing site inside the telecom network through 5G radio. Video data will be processed in near real-time through machine learning algorithms on edge clouds. The results of real-time image processing will be sent to the car, which will be compared with local analysis and make final decisions, such as instructions to the driver.

Intelligent healthcare

The medical industry is seeing a surge in the number of connected devices. Edge computing and edge analysis can greatly reduce this burden. Clinical doctors' mobile devices can capture patient data in real-time to edge connectivity analysis platforms. Patients will no longer need to wait for analysis results, which will greatly reduce their number of visits.

The concept of collaborative edge will become another driver, where geographically dispersed data will be integrated into the consumable views of combined edge devices. For example, due to its ability to detect clinically important features for diagnosis and prognosis, deep learning has recently become increasingly important in the field of ophthalmology. This leads to embedding various deep learning systems into ophthalmic imaging devices, thereby achieving automatic image acquisition. The same operations can be performed at the smartphone based device level, such as by using a high-resolution fundus imaging system connected to the smartphone.

Let the city think for itself

The transformative potential of edge analysis is almost infinite. The speed of 5G networks and the fact that local processing work can be transferred to the network edge make the proposition of edge analysis very powerful. The reduced latency and interconnected smart devices have made a huge leap in the current model, which requires sending large amounts of data back to the cloud for analysis, processing, and insight. Autonomous decision-making and cognitive intelligence on devices or edge networks can reduce processing and decision delays. This means that decisions can be made decisively in real-time.

Edge analysis in 5G will not be limited to traditional descriptive analysis, but will continue to develop. This powerful technology has the ability to learn from scenarios, predict what will happen next, set the next good action or decision, and learn from past behavior patterns to make good decisions. For fully autonomous applications, edge analysis will automatically perform the next steps in real-time.

With the development of 5G, more information will be collected and processed, and the insights generated by edge analysis will increasingly drive decision-making, leading to the application of cognitive intelligence. In short, the future of smart cities will use 5G and edge computing to help cities think for themselves.