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Defending Trace-Back Attack in 3D Wireless Internet of Things.

Authors :
Fu, Junsong
Wang, Na
Nie, Leyao
Cui, Baojiang
Bhargava, Bharat K.
Source :
IEEE/ACM Transactions on Networking; Aug2022, Vol. 30 Issue 4, p1765-1779, 15p
Publication Year :
2022

Abstract

With the development of 5G, it is unsurprising that most of the smart devices in the Internet of Things (IoT) will be wirelessly connected with each other in the near future. This kind of lightweight, scalable and green network architecture will be well-received. In a wide variety of IoT application scenarios, sensor nodes deployed in a local space, such as a multistory building, automatically form a distributed 3D wireless IoT and it can be employed to collect and analyze environmental information. Source-location privacy protection is of great importance in these networks and however, most existing schemes focus on only planar distributed networks which are not suitable for the 3D networks. In this paper, we consider a novel trace-back attack for 3D wireless IoT and then design a source-location privacy protection scheme, named DMR-3D, to defend this kind of novel attacks. In DMR-3D, the source node first selects a set of virtual locations to indirectly choose a set of agent nodes based on the cold start sphere structure and the ellipsoid communication pipeline. Then, a sophisticated mechanism is designed based on both the connected graph and Multiple Delaunay Triangulation (MDT) structure of the network to deliver packets from the source node to the destination node via these agent nodes in a relay manner. Analysis and simulation results illustrate that the proposed scheme can effectively protect source-location privacy with a moderate increment of path stretch, time delay and data transmission amount. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10636692
Volume :
30
Issue :
4
Database :
Complementary Index
Journal :
IEEE/ACM Transactions on Networking
Publication Type :
Academic Journal
Accession number :
158603752
Full Text :
https://doi.org/10.1109/TNET.2022.3149293