Nokia, a telecommunications infrastructure company, is predicting a surge in network demand due to advancements in the metaverse, Web3, and artificial intelligence (AI). To address this increase, Nokia plans to fill the gap by 2030.
In its Technology Strategy 2030 report, Nokia focuses on developing a future network architecture to meet the needs of the emerging innovations market. The company expects a 22%-25% rise in network demand from 2022 to 2030. Federico Guillén, the president of network infrastructure at Nokia, attributes this growth to the mainstream adoption of generative AI and virtual reality devices.
Nokia’s strategy involves significant investments in its portfolio of network equipment and services in the coming years. The company will cater to six ecosystems, including the Internet of Value, which emphasizes decentralization, blockchain, and smart contracts. In the metaverse, Nokia will prioritize human augmentation, spatial computing, and split processing.
Other ecosystems that Nokia will focus on include AI, Industry 5.0, Application Programming Interface (API) economy, and the Cloud continuum.
In 2022, Nokia established two labs to study the metaverse and the underlying technologies. In 2023, the company conducted various industrial and wide-scale metaverse initiatives as part of its experimentation.
Nokia has developed a future network architecture based on network digital twin technology. A digital twin is a simulation of a physical object commonly used for prototyping and testing in industries like manufacturing and construction. In South Australia, Nokia utilized the metaverse to potentially assist Cessna aircraft technicians at remote airports. By using augmented reality, technicians were instructed on how to service a Cessna using a 5G connected Microsoft HoloLens.
Additionally, in Finland, two scientists experimented with digital twins called “Metahumans” to demonstrate how the metaverse can gamify employee management instead of traditional optimization programs. The researchers suggested that data collected from digital twins in industrial simulations could be used to determine optimal work strategies, implement safety measures, and improve performance and output.
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