247
A Comprehensive Guide to 5G Security, First Edition. Edited by Madhusanka Liyanage, Ijaz Ahmad,
Ahmed Bux Abro, Andrei Gurtov, and Mika Ylianttila.
© 2018 John Wiley & Sons Ltd. Published 2018 by John Wiley & Sons Ltd.
11
11.1 Introduction
Mobile network operators should meet connectivity requirements for new applications,
which will be released in coming years. Comparing to Long Term Evolution (LTE)
networks, 5G will offer increased data rate, reduced end‐to‐end latency, and improved
coverage, which are essential factors for many Internet of Things (IoT) applications
such as unmanned cars, smart cities and intelligent transportation systems. Figure 11.1
shows the 5G use cases and requirements [1].
The existing technologies of cloud computing, IoT, and wireless controlled robots
combine to produce a new variant called Cloud Robotics. Cloud Robotics uses the big
data techniques and computing power of the cloud along with the connectivity provided by 5G, LTE and other wireless technologies to control the actions of wireless
robots. We shall refer to this technology as Mobile Cloud Robot (MCR), where the
robot‐cloud connectivity is provided by mobile networks. This technology has the
mutual attention of telecommunication vendors as well as different industries, including manufacturing, medicine and agriculture.
Because of the operational characteristics of resource sharing and a centralized
controller, MCR can act as a potential cost saver. On the other hand, with big data
techniques such as data mining and knowledge reuse, its efficiency and security could
be improved. Using all computing power has risks and it is critically important to secure
the communications path and efficiently utilize the wireless bandwidth. Big data and
the use of data intelligence can be applied to MCR to provide both services and security
support to an MCR network. Examples such as fault detection, service issue resolution,
network segmentation and security are common. For this purpose, we propose a distributed security platform including robots, Local Robot Controller (LRC), mobile
cloud, IoT anomaly detection module and IoT orchestrator. Here, we aim to reach high
efficiency from different perspectives to perform data analysis that will identify
improvements for an MCR environment, for example processing time, data analysis
accuracy, energy saving for different applications such as robot programming, monitoring,
security and fault management [2].
IoT Security
Mehrnoosh Monshizadeh
1,2
and Vikramajeet Khatri
1
1 Nokia Bell Labs, Finland
2 Aalto University, Finland
A Comprehensive Guide to 5G Security, First Edition. Edited by Madhusanka Liyanage, Ijaz Ahmad,
Ahmed Bux Abro, Andrei Gurtov, and Mika Ylianttila.
© 2018 John Wiley & Sons Ltd. Published 2018 by John Wiley & Sons Ltd.
11
11.1 Introduction
Mobile network operators should meet connectivity requirements for new applications,
which will be released in coming years. Comparing to Long Term Evolution (LTE)
networks, 5G will offer increased data rate, reduced end‐to‐end latency, and improved
coverage, which are essential factors for many Internet of Things (IoT) applications
such as unmanned cars, smart cities and intelligent transportation systems. Figure 11.1
shows the 5G use cases and requirements [1].
The existing technologies of cloud computing, IoT, and wireless controlled robots
combine to produce a new variant called Cloud Robotics. Cloud Robotics uses the big
data techniques and computing power of the cloud along with the connectivity provided by 5G, LTE and other wireless technologies to control the actions of wireless
robots. We shall refer to this technology as Mobile Cloud Robot (MCR), where the
robot‐cloud connectivity is provided by mobile networks. This technology has the
mutual attention of telecommunication vendors as well as different industries, including manufacturing, medicine and agriculture.
Because of the operational characteristics of resource sharing and a centralized
controller, MCR can act as a potential cost saver. On the other hand, with big data
techniques such as data mining and knowledge reuse, its efficiency and security could
be improved. Using all computing power has risks and it is critically important to secure
the communications path and efficiently utilize the wireless bandwidth. Big data and
the use of data intelligence can be applied to MCR to provide both services and security
support to an MCR network. Examples such as fault detection, service issue resolution,
network segmentation and security are common. For this purpose, we propose a distributed security platform including robots, Local Robot Controller (LRC), mobile
cloud, IoT anomaly detection module and IoT orchestrator. Here, we aim to reach high
efficiency from different perspectives to perform data analysis that will identify
improvements for an MCR environment, for example processing time, data analysis
accuracy, energy saving for different applications such as robot programming, monitoring,
security and fault management [2].
IoT Security
Mehrnoosh Monshizadeh
1,2
and Vikramajeet Khatri
1
1 Nokia Bell Labs, Finland
2 Aalto University, Finland