Highly efficient key agreement for remote patient monitoring in MEC-enabled 5G networks
|Author:||Braeken, An1; Liyanage, Madhusanka2,3|
1Industrial Engineering Department (INDI), Vrije Universiteit Brussel (VUB), Belgium
2School of Computer Science, University College Dublin, Ireland
3Center for Wireless Communications, University of Oulu, Finland
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020111790865
|Publish Date:|| 2021-11-09
Remote patient monitoring is one of the cornerstones to enable Ambient Assisted Living. Here, a set of devices provide their corresponding input, which should be carefully aggregated and analysed to derive health-related conclusions. In the new Fifth-Generation (5G) networks, Internet of Things (IoT) devices communicate directly to the mobile network without any need of proxy devices. Moreover, 5G networks consist of Multi-access Edge Computing (MEC) nodes, which are taking the role of a mini-cloud, able to provide sufficient computation and storage capacity at the edge of the network. MEC IoT integration in 5G offers a lot of benefits such as high availability, high scalability, low backhaul bandwidth costs, low latency, local awareness and additional security and privacy. In this paper, we first detail the procedure on how to establish such remote monitoring in 5G networks. Next, we focus on the key agreement between IoT, MEC and registration center in order to guarantee mutual authentication, anonymity, and unlinkability properties. Taking into account the high heterogeneity of IoT devices that can contribute to an accurate image of the health status of a patient, it is of utmost importance to design a very lightweight scheme that allows even the smallest devices to participate. The proposed protocol is symmetric key based and thus highly efficient. Moreover, it is shown that the required security features are established and protection against the most of the well-known attacks is guaranteed.
Journal of supercomputing
|Pages:||1 - 23|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is supported by Academy of Finland in 6Genesis Flagship (grant no. 318927) and 5GEAR projects, and European Union in RESPONSE 5G (Grant No: 789658) project.
© Springer Science+Business Media, LLC, part of Springer Nature 2020. This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-020-03472-y.