Tech
IOWN 2.0 – the next applications for the next-gen network architecture | Computer Weekly
In the first part of our round-up of the Dallas meeting of the Innovative Optical and Wireless Network (IOWN) project’s Global Forum conference in October 2025, we looked at the technological development of the association’s all-photonics network (APN) – and the continued work in essentially “moving from electronics to photons”, as a representative of one of its key member companies insisted its mission boils down to.
One of the most important developments over the past year was using the APN to support a theatre performance that synchronised live and virtual performers in Japan and Taiwan, running over a network that was over 3,000km long.
In addition to validating a very important use case, the Cho Kabuki project also saw the APN being at the heart of a digital twin, whereby the producers could digitise the performance character and manipulate it in the computing space while being certain that latency would not adversely affect synchronisation. The lessons from this area are likely to be applied to digital twins in the industrial space. Smart warehouses are one key example cited.
NTT’s Masahisa Kawashima, IOWN technology director and head of the technology working group at the IOWN Global Forum, says plans also include demonstrating commercial operability of the APN and defining a new functional architecture for artificial intelligence (AI) computing platforms using co-packaged optics and optical circuit switches.
The agile deployment of optical fibres is seen as essential for forthcoming 6G networks. IOWN APN provides a virtualisation layer on top of physical fibre infrastructure. This allows multiple mobile operators to share fibre infrastructure, mitigating availability issues and costs.
It is worth noting that soon after the Dallas conference, IOWN forum member Nokia announced a strategic partnership with Nvidia to add the latter’s AI-powered radio access network (RAN) products to Nokia’s RAN portfolio, enabling communications service providers to launch AI-native 5G Advanced and 6G networks on Nvidia platforms.
Modes of modernisation
Looking at a broader perspective on what enterprises will likely need to do to tap into this revolution, Jefferson Wang, chief strategy officer for cloud first at Accenture, noted in Dallas that having made a significant $3bn investment in AI, including classical AI, generative AI and physical AI, his company was aiming to solve the big problems faced by industries, governments and societies alike, and assist them in their transformation. In short, exactly what IOWN sets out to do.
Accenture has identified five key areas for modernisation: new architecture; application refactoring; data and AI flow; infrastructure changes; and operational model changes. Wang acknowledges that to address these enterprise and societal challenges, Accenture’s cloud modernisation practice is focusing on infrastructure, network security and operational changes – and these plans will only be realised with modernised networks and compute solutions.
“If we’re helping industries, governments and societies change, there could be a challenge with where is the modernised network, where is the actual compute and how do you think about the storage? So, in our cloud practice, one of the things we identified is that modernisation is not just a lift and shift story to transform these companies,” says Wang.
“The first [challenge] is to figure out this new architecture. The second is how to think through the actual applications. Do you refactor them? Do you just lift and shift them? What do you do with the applications? The third is your data and AI. How do you do your data flow? How do you think through these different forms of AI? The fourth is what infrastructure changes, network modernisation, and security [measures] you have to put in place. And the last one is how to think about your ways of working that have to change.”
As a result, change is an imperative. It could be a change in a business’s operational models. It could be that a full stack of financial operations is needed for a company to make sure the transformation economically still makes sense.
A question of compute
When Accenture looked at all of these things, and looked at the permutations and big macro trends, Wang recalls it mostly came down to a need for more compute, and more compute close enough to where data is created or used. And that means population centres. Wang notes that firms were having a hard time finding affordable real estate and power for datacentres near population centres, and that at the time, there wasn’t a good answer for that issue.
However, what is a good answer for the Accenture cloud modernisation practice is optical networks. That is replacing the expensive and energy-hungry electronics, solving the problem at the optical layer of networks, and innovating on the transponder. Wang references Cho Kabuki as a great example of what is currently possible and where the use of an APN could lead, such as, again, digital twins in manufacturing.
“Proving 3,000km between Taiwan and Japan with 17 milliseconds [latency] and no jitter [is] a big deal. It changes the actual economics of what we’re trying to do. That’s game-changing. I can do ‘what if’ scenarios on the digital twin. A manufacturing industry cares about the number of shifts you run, the flexibility of your line and, ultimately, worker safety. If you can’t do ‘what if’ scenario planning, it becomes very hard to be flexible. A digital twin is generally a big, heavy compute [operation], so if I can’t move it back and forth quickly, it becomes static, and it doesn’t help with the ‘what if’ scenario planning,” says Wang.
“So, knowing the value drivers of an industry is incredibly important for us, and then figuring out how we’re going to transform it. We orchestrate the ecosystem, and we’ll find an operator with the right spectrum holdings for factories, and then we’ll orchestrate the solution to figure out the architecture. That is a private network, millimetre-wave for this piece of video analytics, sub-6G comms for that piece, the communication trunk. And then we might want to be able to say, here’s the right edge solution [and] build a computer vision solution on top of it, and then we wrap the solution together. But that also requires [thinking about] what you are doing with the trunk, the optical layer of the network.”
Accenture’s commitment to IOWN spans around four years, and before this time, a number of the company’s clients were members of the association. Wang observes that when Accenture looked at the macros and big trends, such as what AI was going to do in all its forms, the result was that firms needed to think about cloud posture and compute.
Accenture then began to identify some of the potential choke points to infrastructures, some of which could also be control points given the right technological basis. How to unlock those was really the impetus to diving deeper into IOWN, which has the mission to address the three pillars of capacity, latency and energy consumption. Wang stresses what these should mean as regards a true business solution.
“Conditioning the enterprises to understand the value of each [pillar] is as hard as pulling the solution together. You say the word ‘latency’, and if you go to a CEO and you say, ‘I can reduce your latency down to one or two milliseconds,’ they might say, ‘Okay, great. What does that mean?’ And then you have to say, ‘In a fast-moving production line, here’s your error, and then, to create an automated solution, you need your latency or jitter to be at this threshold, and currently your baseline is three seconds.’ So conditioning, the value of that is actually quite hard. So those three pillars, to me as an engineer, are exciting, and the innovation that’s happening at IOWN is really exciting and fast paced, but actually the value chain of what you’re putting it into takes awareness and education, it takes a business case, and it takes conditioning to value it.
“Because one of the things that really fascinates me about IOWN [version] 2.0 is looking at the deterministic part of the [technology]. You can now have that conversation. It’s deterministic quality service, deterministic latency. [In] finance, if the regulator says [minimum network latency level] is ‘that’, then you work around that. That’s a different combination than [saying] it’s just a bit faster network. This is completely different. We have found that that level of understanding is clearer [to CEOs], it’s more quantitative. When you get deterministic, that’s where it gets very exciting.”
Moving to real-world applications
This is also where it gets more practical, as far as the association is concerned. Kawashima says that for the next 12 months, the emphasis for IOWN will be on one thing: commercialisation. The plan is simply to pass from showing a robust and well-thought-out proof of concept to accelerate into real-world applications.
He says: “To me, proof of concept is like the Wright Brothers’ first flight. It’s a great achievement, but we cannot do business right after that. We need to find business use cases that make airplane travel worth the cost. [We have to] demonstrate that telecoms operators around the world can use APNs without the risk of losing their customers. So this is the journey between proof of concept and proof of business. And of course, we will do a lot of experimentation and many proof works to prove those points.”
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An unknown technical problem caused a number of robotaxis owned by the Chinese tech giant Baidu to freeze on Tuesday in the middle of traffic, trapping some passengers in the vehicles for more than an hour.
In Wuhan, a city in central China where Baidu has deployed hundreds of its Apollo Go self-driving taxis, people on Chinese social media reported witnessing the cars suddenly malfunction and stop operating. Photos and videos shared online show the Baidu cars halted on busy highways, often in the fast lane.
A college student in Wuhan tells WIRED that she was stuck in a Baidu robotaxi with two friends for about 90 minutes on Tuesday. (She asked to be only identified with her last name, He, to protect her privacy.) The student says the car malfunctioned and stopped four or five times during the trip before it eventually parked in front of an intersection in eastern Wuhan. Luckily, it was not a busy road, and the group was not in immediate danger. The screen display in the car asked the passengers to remain in the car with seatbelt on and wait for a company representative to come “in five minutes,” according to a photo He shared with WIRED.
He says it took about 30 minutes to reach a Baidu customer representative on the phone. “They kept saying it would be reported to their superior. But they didn’t explain what caused [the outage] or let us know how long we needed to wait for the staff to come,” He says. But no one ever came, and after another hour of waiting, the three passengers decided to just get out and go home by themselves (the doors weren’t locked).
On Chinese social media, other passengers also complained about being unable to reach Baidu’s customer support. “I tried every way I could think of to call for help using the options the app showed, but the phone line wouldn’t go through, and when I pressed the SOS button it told me it was unavailable. So then what exactly is the SOS for?” wrote one person in a post on RedNote alongside a video showing the button not working. She said she had to force the door to open and get out of the car as traffic halted to a complete stop behind her robotaxi. “Apollo Go, you really owe me an apology,” she wrote.
Baidu didn’t immediately respond to a request for comment. Local police in Wuhan issued a statement around midnight in China that said the situation was “likely caused by a system malfunction,” but the incident is still under investigation. No one was injured and all passengers have exited the vehicles, the police added. It’s unclear how many of Baidu’s robotaxis may have been impacted.
One dash cam recording posted to RedNote shows a car passing 16 Apollo Go vehicles parked on the road in the span of 90 minutes. On several occasions, the video shows the driver narrowly avoiding hitting the robotaxis by braking or changing lanes at the last minute.
Others were apparently not as fortunate. In another RedNote post, a man claimed he crashed into one of the malfunctioning Baidu vehicles. The man wrote in the caption that he was driving over 40 mph on a highway when the car in front of him suddenly changed lanes to avoid the stopped robotaxi. He couldn’t react fast enough and ended up running into the self-driving car. Photos of the man’s orange SUV being towed away show that the car’s front-right fender was completely torn off, and other parts appeared to have sustained major damage.
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