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, Nokia plans to bridge the gap by 2030.
In its Technology Strategy 2030 report, Nokia emphasizes the importance of developing a future network architecture that can support the emerging innovations market. The company expects a 22%–25% increase in network demand from 2022 to 2030. Federico Guillén, the president of network infrastructure at Nokia, attributes this rise to the growing popularity of generative AI and virtual reality devices.
Nokia’s strategy involves significant investments in its network equipment and services portfolio in the coming years. One of the key areas Nokia will focus on is the Internet of Value, which encompasses decentralization, blockchain, and smart contracts. In the metaverse, Nokia will prioritize human augmentation, spatial computing, and split processing.
The company will also cater to five other ecosystems: AI, Industry 5.0, Application Programming Interface (API) economy, and Cloud continuum.
In 2022, Nokia established two labs to study the metaverse and the underlying technologies. In 2023, the company conducted various industrial and large-scale metaverse initiatives to explore its potential.
Nokia has developed a future network architecture based on network digital twin technology. A digital twin is a simulation of a physical object used for prototyping and testing in industries like manufacturing and construction. In South Australia, Nokia has utilized the metaverse to potentially assist Cessna aircraft technicians at remote airports. By using augmented reality, technicians can receive instructions on servicing a Cessna through a 5G-connected Microsoft HoloLens.
In Finland, two scientists experimented with digital twins called “Metahumans” to demonstrate how the metaverse can gamify employee management instead of relying on traditional optimization programs. The research paper highlighted the potential of using data collected through digital twins in industrial simulations to determine optimal work strategies, implement new safety measures, and improve overall performance and output.
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