Organisations in Asia-Pacific are seeking out edge computing in search of faster response and cost savings, but they also have concerns about security and latency when large volumes of data are processed on such platforms.
A primary, and often cited, benefit of edge deployments are the rapid response times that would not be possible if data is sent back to a centralised network for processing.
Taiwan’s Taoyuan City, for instance, turned to edge technology in rolling out smart streetlights in its Qingpu district, using HPE’s Edgeline EL10 Internet of Things (IoT) Gateway.
The Taiwanese city has ambitions of becoming a smart city and is looking to deploy and integrate multi-sensor information from edge products into a centralised platform to deliver better citizen services.
“Certain citizen intelligence applications and services require an almost immediate response time [and] this cannot be achieved if data needs to be transmitted back to a centralised cloud for processing,” a spokesperson for Taoyuan City Government’s Public Works Department told ZDNet.
For applications that operate in an outdoor environment, network connectivity also might be affected by external factors such as weather and road construction, the spokesperson explained, noting that edge computing powered by machine learning algorithms were able to mitigate disruptions in network transmissions.
In addition, processing data via edge technology reduced the amount of information that had to be transmitted over a network, offering cost savings in network and cloud storage, she said.
See: Edge computing: The state of the next IT transformation
To address customer concerns about outdoor or physical attributes, vendors such as HPE have designed their products to withstand various external factors such as dirt, humidity, temperatures, and vibration.
Jason Tan, HPE’s Asia-Pacific general manager of its IoT enterprise solution group, said the vendor’s edge products were built to operate in environments with temperatures as high as 70 degrees Celsius as well as operate “fan-less”, which provides more flexibility in site deployment.
When asked about the initial concerns that the Taoyuan government may experience when deploying the edge technology, the spokesperson pointed to the need to closely monitor such systems.
“Intelligent edge solutions typically require massive data processing and network connectivity. Hence, ensuring regular system updates as well as stability of the various decentralised devices is critical,” she said.
“Furthermore, as citizens increasingly rely more on such services, we need to ensure the data collected from multiple sensor devices is stored properly and securely.”
According to Zhen Ke, principal engineer of Alibaba Cloud’s IoT business unit, customer concerns about the accuracy of edge computing and latency of the cloud network supporting such devices were not uncommon.
As each node operates independently, data disparity and ensuring data is properly synchronised have been cited as potential challenges with regards to edge computing.
Alibaba addressed such concerns by adopting an integrated approach, instead of treating each node as an independent and isolated function, Zhen said.
“While we are empowering the edge, data will still be fed back to the cloud to ensure data consistency and synchronisation. This [will allow users] to tap cloud’s scalability and flexibility to better address dynamic needs,” he said, adding that Alibaba also leveraged AI and machine learning to enhance the entire compute process.
Also: What you need to know before implementing edge computing
Tan noted that HPE’s edge systems supported unmodified enterprise software from its partner community, including Citrix, SAP, GE Digital, and Microsoft. This meant that enterprise customers could use the same application stacks at the edge, in datacentres, as well as cloud.
“[It] simplifies the sharing of critical data and insights from the edge across locations to enable data correlation, deep learning, and process coordination,” he said. “For instance, selected predictive maintenance data from several oil rigs can be aggregated and analysed in a central location to enable intelligent maintenance scheduling across oil rigs.”
He added that the emergence of blockchain technology also paved the away for distributed learning capabilities on edge computing platforms, thereby enabling each node to process their learning and decision making using blockchain and ensure data integrity and consistency.
Key considerations before going to the edge
Taoyuan City’s streetlight management edge deployment is still currently in its pilot phase and the government has plans to deploy more streetlights over the next few phrases of the project, according to the spokesperson.
She noted that the city government is hoping to introduce more innovative services by analysing the data collected in the deployment, spanning parameters such as air quality, climate indicators, and image analysis processing.
In deciding the volume and type of data that should and should not be analysed at the edge, she said the Taoyuan government assessed the network transmission bandwidth of the field device as well as the data management centre.
It also considered the immediacy of the application service, whether it required real-time processing and feedback, and whether edge computing could support the required speed and security, she noted.
She added that, compared to traditional datacentres, outdoor environments are harsher and edge deployments in such situations would need to consider factors such as weather, dust conditions, temperature as well as stability of power supply to the device.
“At the same time, the solution is deployed over a large number of streetlights, which limits resources in terms of processing power and configuration,” she said. “Hence, the ability to analyse the smallest function and need is an important consideration when designing an edge computing deployment.”
Alibaba’s Zhen also noted that edge computing is restricted by its physical limitations of requiring space to house the hardware. Apart from relying on a robust cloud to provide the computing resources required for more intensive processing and analysis, he added that AI is essential to enhance such deployments.
“Edge computing is for business applications requiring speed in processing, response, and action, and AI plays an integral role here,” he said. “Data can typically be analysed at the edge for faster responses and quicker actions, whereas for AI training and analysis, the large volume of data will usually be processed at the cloud.”
Alibaba last month announced a partnership with Intel to jointly develop “data-centric” computing products, including a Joint Edge Computing Platform, which features the chipmaker’s software, hardware, and AI technologies as well as Alibaba Cloud’s IoT offerings.
China’s Chongqing Refine-YuMei Die Casting (YuMei) was the first customer to deploy the new Alibaba-Intel edge product, using the platform to identify defects while parts were cast rather than have to wait until the end of the manufacturing line before they were manually inspected.
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2021 Range Rover Evoque gains new technology and refinement
Land Rover has announced the 2021 Range Rover Evoque. For 2021, the vehicle gets more refinement and enhance technology. It’s now fitted with the Pivi Pro system as a standard feature bringing a redesigned intuitive menu structure with the most popular features and functions accessible from a single home screen.
Land Rover says that the design of its Pivi Pro system allows users to access functions in two taps or less. The 2021 Evoque is also available within Online Pack with a data plan featuring integrated Spotify within the infotainment menu for the first time. The system is also able to connect wirelessly via Bluetooth to phones at the same time.
Smartphone users also have available wireless smartphone charging with signal boosting capability. The vehicle features a dual-modem embedded SIM allowing for scheduled vehicle software updates over the air. That means no visiting dealerships to have the software updated when needed.
2021 Evoque buyers can also get an optional Advanced Cabin Air Filtration system designed to filter out fine particulate matter, allergens, and strong odors. The system also displays interior and exterior and quality information. Land Rover fits the vehicle with the newly expanded suite of driver assistance technology, including a standard 3D Surround Camera and available Rear Collision Monitor.
Multiple models will be available in the US, starting with the Evoque S P250 featuring a 2.0-liter turbo inline-four-cylinder engine making 264 horsepower and 269 pound-foot of torque. The base 2021 Evoque S P250 is priced at $43,300. A total of five models are available, with the most expensive being the Evoque R-Dynamic HSE P250 featuring the same engine starting at $53,400. The prices exclude the $1050 destination and delivery fee. There are several options available that can drive the starting price up significantly.
2021 Honda N-One minicar goes on sale in Japan
Honda’s second-gen N-One minicar (Kei car) is now on sale in Japan. According to the Japanese carmaker, the all-new N-One is available at Honda dealerships across Japan beginning November 20, 2020.
First seen in 2012 together with the quirky Honda N-Box, the N-One spearheaded Honda’s next generation of small city cars for the Japanese market. The 2021 N-One has retained the ‘circle, square, trapezoid’ design idiom of the outgoing model and is now available with more standard safety features and driving tech.
Standard in the new N-One is Honda Sensing which includes automatic high beams, collision mitigating braking system, false start prevention, traffic sign recognition, road departure mitigation, lane-keeping assist, and plenty more.
Additionally, manual-equipped versions of the N-One are the first mini vehicles in Japan to feature adaptive cruise control and lane-keeping assist. Also standard in the N-One is a rear seat reminder feature to alert the driver of unattended objects, pets, or children in the rear seats.
The new Honda N-One is a proper mini car. Measuring 11.14-feet (3,395 mm) long and 4.84-feet (1,475 mm) wide, the second-gen model retains the familiar styling cues of the outgoing version with clean lines and round headlights similar to the Honda E electric car. However, the new N-One has larger front air intakes, LED headlights, and a black horizontal strip in the rear bumper.
The interior, however, is as fresh as morning dew. The N-One has a larger infotainment screen, a new instrument console, and a new steering wheel with control switches and buttons. Typical of a Honda, the cabin has a bevy of USB ports, cubby holes, and storage pockets for maximum convenience.
The 2021 Honda N-One is available in base, premium, and RS trims. Standard fare is a 660cc three-cylinder naturally-aspirated gasoline engine with 58 horsepower, front-wheel drive, and a CVT gearbox. The RS trim has a six-speed manual transmission mated to a turbocharged 660cc motor producing 68 horsepower. All-wheel-drive is optional across the board.
Honda’s newest N-One will only be sold in Japan. Base prices start at the equivalent of $15,406 (¥1,599,400) based on current exchange rates.
Ford Bronco R conquers the 2020 Baja 1000
The second time is the charm for the Ford Bronco R prototype. Last year, the Bronco R was unable to complete the Baja 1000 due to mechanical failures. But this year, Ford’s second attempt at conquering the 1,000-mile off-road endurance race is a successful one.
“When Bronco returned we said it would follow in the legacy of the first-generation Broncos that forever changed the off-road landscape – and today’s finish demonstrates we’re continuing the ‘Built Wild’ pedigree of Bronco,” said Mark Rushbrook, global director, Ford Performance motorsports.
Driven by a team of seasoned off-roaders led by Cameron Steele, Shelby Hall, Johnny Campbell, Curt LeDuc, and Jason Scherer, the Ford Bronco R prototype crossed the finish line in just over 32 hours. And unlike last year, the Bronco R was accompanied by a pair of drool-worthy support vehicles in its quest for off-road supremacy: The F-150 Raptor and a 2021 F-Series Super Duty Tremor truck.
Ford’s racing history is brimming with stories of triumphant comebacks. After dealing with instability issues, engine failures, and plain ol’ bad luck in its attempt to unseat Ferrari’s dominance at Le Mans, the Ford GT40 came back with a vengeance. Ford broke Ferrari’s impressive five-year winning streak at the 24 Hours of Le Mans in 1966. From that point forward, the GT40 won the endurance race consecutively for the next three years until 1969.
Meanwhile, the Ford Bronco has an illustrious legacy at Baja. The first-gen Bronco won five Baja 1000 class wins from 1967 to 1972, and it also won the first-ever overall production 4×4 class win in 1969. Additionally, second to fifth-gen Broncos scored two Baja 500 victories in 1970 and 1973 followed by nine Baja 500 Class 3 wins from 1004 to 2015.
Ford’s Baja-conquering Bronco R prototype is built atop the T6 chassis of a production-spec Bronco. It also has the same 2.7-liter EcoBoost engine and 10-speed automatic gearbox as the production variant. More than a quest for glory, Ford is using this year’s race as a chance to optimize the ‘Baja Mode’ for its new Terrain Management System – a feature of which customers can enjoy once the all-new Ford Bronco arrives at dealerships in spring of 2021.
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