Taoyuan City uses edge computing to manage street lights
Enterprises in the Asia-Pacific region are seeking edge computing to achieve faster response speeds and cost savings, but they are also worried that security and latency issues will arise when processing large amounts of data on such platforms.
We often say that the main benefit of edge deployment is fast response speed. If data is sent back to a centralized network for processing, then a fast response cannot be obtained.
For example, Taoyuan City, Taiwan Province, used edge technology when it launched smart street lights in its Qingpu District: it used the HPE Edgeline EL10 IoT gateway.
Taiwan aspires to be a smart city and hopes to integrate multi-sensor information in edge products into a centralized platform to provide better resident services.
A spokesperson for the Taoyuan Municipal Government’s Works Bureau told ZDNet: “Some residents’ smart applications and services require timely response time, but if the data is transferred back to a centralized cloud platform for processing, it cannot be achieved.” The spokesperson explained that for applications running in an external environment, network connections may also be affected by external factors such as weather and road construction. She found that edge computing powered by machine learning algorithms can alleviate the disruption of the network during the transmission process. Additionally, processing data by using edge technology can reduce the amount of information that must be transmitted over the network, thereby reducing network and cloud storage costs.
In order to solve customers' concerns about external or physical factors, products designed by suppliers such as HPE can withstand a variety of external factors, such as dust, humidity, temperature and vibration. Jason Tan, general manager of IoT Enterprise Solutions Group,
HPE (Asia Pacific) IoT Enterprise Solutions Group, said vendor-designed edge products can operate in environments up to 70°C without passive cooling, which provides more flexibility when deployed on site.
When asked about the initial problems the Taoyuan Municipal Government encountered in deploying edge technology, the spokesperson pointed out that careful supervision of such systems is needed.
"Smart edge solutions often require a lot of data processing and network connections. Therefore, ensuring regular system updates and the stability of multiple distributed devices is crucial."
"Even, as more and more residents rely on such services, we need to ensure that the data collected from multiple sensor devices is stored in an appropriate and secure manner." Zheng Ke, chief engineer of the Internet of Things business department of Alibaba Cloud , said that customers' concerns about the accuracy of edge computing and the latency of cloud networks that support such devices are very common.
Since each network node runs separately, data differences and ensuring data are synchronized in an appropriate manner have become potential challenges in edge computing.
Zheng said that Alibaba solves this kind of problem by adopting a comprehensive approach, rather than treating each node as an independent function.
"Although we have enhanced edge capabilities, the data is also fed back to the cloud to ensure consistency and synchronization of the data. This allows customers to leverage the scalability and flexibility of the cloud to better meet dynamic needs," he added, adding that Alibaba also uses AI and machine learning to enhance the entire computing process.
Tan found that HPE's edge system supports unmodified enterprise software in its partner communities, including Citrix, SAP, GE Digital and Microsoft . This means that enterprise users can use the same application stack at the edge, in the data center, and in the cloud.
“It simplifies the sharing of important data and insights across the edges of different regions to enable data relevance, deep learning and coordination of processes,” he said. “For example, aggregation and analysis of predictive maintenance data selected from multiple oil rigs in a centralized area, enabling intelligent maintenance planning in multiple oil rigs."
He added that the emergence of blockchain technology has also opened up the way for distributed learning capabilities on edge computing platforms, so that every node can handle their learning and decision-making by using blockchain, and ensure data integrity and consistency.
Some important considerations to do before turning to the edge
The spokesperson said that the edge deployment of street light management in Taoyuan City is still in the pilot phase, and the government plans to deploy more street lights at other stages of the project.
She noted that the city hopes to launch more innovative services by analyzing the data collected during the deployment process (such as air quality, climate indicators and image analysis processing, etc.)
When deciding whether it should be done in the edge When analyzing the amount and type of data, she said that the Taoyuan government evaluated the network transmission bandwidth of field devices and the data management center.
She found that the government also considered the timeliness of application services, whether real-time processing and feedback are required, and whether edge computing can support the required speed and security.
She added that the external environment is more stringent than traditional data centers, and edge deployment in such environments may need to consider factors such as weather, dust environment, temperature, and stability of equipment power supply.
"At the same time, this solution has been deployed in many street lights, which limits the resources in processing power and configuration," she said. "So, the ability to analyze minimal functions and requirements is an important consideration when designing edge computing deployments. "
Alibaba's Zheng Ke also found that edge computing is also limited by physical factors such as the space required to place hardware. He added that in addition to relying on a powerful cloud to provide the required computing resources for more intensive analytics and processing capabilities, AI is also crucial to enhancing such deployments.
"Edge computing is suitable for enterprise applications that require processing, response and behavior speed, and AI also plays a vital role in it," he said. "Data can be analyzed at the edge to obtain faster response and behavior, while for AI training and analysis, a large amount of data is usually processed in the cloud. "
last month, Alibaba announced a cooperation with Intel to jointly develop a "data-centric" cloud-edge integrated edge computing product, using the chip manufacturer's software, hardware and AI technology, as well as Alibaba Cloud's Internet of Things products.
China Chongqing Ruifang Yumei Die Casting Co., Ltd. is the first customer to deploy Alibaba-Intel's new edge products. The company used this platform to discover production defects in parts casting without waiting for the production line to complete before performing manual inspection.
Original author: Eileen Yu
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