.png){kind=icon}
.png){kind=icon}
Return
Efficient Message Authentication Scheme for IoT Networks Using Precoded-Tag Embedding
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2026.3667441.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Message authentication using tag embedding has been widely adopted for Internet of Things (IoT) networks due to its efficient performance and lower resource consumption compared to other tag-appending approaches. Specifically, in tag embedding, the authentication tag and the message are superimposed in one signal to save time, energy, and spectral resources, making it an ideal alternative for networks with limited resources, such as IoT networks. However, due to the lower power allocated to the tag symbols (compared to the message symbols) in the conventional scheme, the received tag experiences higher transmission errors, increasing the authentication failure rate (AFR). Moreover, an Eve might be able to capture either the message, the tag, or both as they are transmitted unprotected in the conventional scheme. In this work, a novel message authentication scheme is proposed to enhance the performance of the tag-embedding scheme. Specifically, the proposed scheme involves precoding the tag signal prior to being superimposed with the message signal and emitted over the channel. The conducted analysis and evaluation reveal that the proposed scheme can significantly reduce both the tag error rate (TER) and the message error rate (MER), which should be directly reflected in improving the AFR. In addition, the performance at the eavesdropper is shown to be degraded due to the precoding of the tag signal induced in the proposed scheme. The included analysis and explored results investigate the impact of power allocation at the transmitter between the tag and message signals to minimize MER, TER, or AFR.
Keywords:
Internet of Things (IoT) networks
message authentication
physical layer security
tag embedding
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W