Sprint is working alongside the Arizona State University (ASU) with the promise of 5G and IoT technologies and promoting innovation across campus.
The announcement, made at the recent MWC Los Angeles, revealed that the university will benefit from Sprint’s True Mobile 5G and Curiosity IoT’s advanced dedicated network and operating system.
The Kansas-based telecom operator, which already covers more than two million people in areas of Phoenix with its 5G service, will help launch ‘Curiosity University’ for its employees to promote a new generation of IoT experts and create a Sprint 5G Incubator at ASU’s Novus Innovation Corridor.
“The Internet of Things is driving the fourth industrial revolution right before our eyes, changing how we interact with everyday items and increasing the possibilities of technology and data,” said Ivo Rook, SVP of IoT and product development at Sprint. “We are proud to work with Arizona State University to help create an entire smart region ready to pave the way for the future.”
In March, Sprint launched the Curiosity IoT Estimation Tool that allows medium and small size business customers and enterprise customers a quick access to customised Sprint Curiosity IoT data pricing. The product allows users to choose one or multiple features to create custom access point names (APNs), as well as pricing information. The Tool offers international IoT data rates in six different regions across the globe to benefit organisations plan and build IoT solutions. It also benefits businesses swiftly scale IoT solutions by showing pricing estimates for adding any number of devices and services without having the need to go back and forth with a sales team.
Recently, a team of students at the Penn State World Campus developed a multi-pronged data analysis approach capable of averting cyber-attacks in IoT devices. To develop this solution, the team applied a combination of approaches mostly used in traditional network security management to an IoT network simulated by Australia’s UNSW Canberra. They demonstrated how statistical data, machine learning, and other data analysis methods could be applied to enhance security of IoT systems across their lifecycle. An intrusion detection and a visualisation tool were used to determine whether an attack had already occurred or was in progress within that network.
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