Many-to-One Lightweight Batch Authentication Key Agreement for Wireless Body Area Networks

Wireless body area networks (WBANs) are widely used in the medical field, which makes it easier for doctors to gather patients’ physiological information to diagnose their physical status. Recently, a variety of lightweight authentication key agreement (AKA) protocols have been proposed for WBANs. However, the computational overhead of the authentication process for most of these protocols increases exponentially when the number of patients increases dramatically. Therefore, to improve the authentication efficiency, a many-to-one, lightweight batch AKA protocol based on the Chinese remainder theorem is proposed, which can realize that cloud server (CS) interacts with the gateway nodes once to obtain multiple session keys with different sensor nodes, so as to achieve the effect that multiple sensor nodes participate in the AKA at the same time. In addition, only hash function, XOR operation, and symmetric encryption are used in the proposed protocol. During the key agreement process, CS can compute the session key between it and the sensor node on demand. Sensor nodes can dynamically join and leave the WBAN, which increases the flexibility of the protocol. We verify the protocol’s security using formal security analysis tools, such as the real-or-random model, Burrows-Abadi–Needham logic, and the ProVerif tool. The experiment and comparison results indicate that the proposed protocol outperforms the other related protocols in terms of efficiency.

Authors

Institutions

Publication Details

Journal
IEEE Internet of Things Journal
Published
2024-11-07
DOI
https://doi.org/10.1109/jiot.2024.3493419
Citations
4
Primary Topic
Wireless Body Area Networks
Type
article
Field-Weighted Citation Impact
0.74

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Many-to-One Lightweight Batch Authentication Key Agreement for Wireless Body Area Networks

Dong Xie, Weixin Bian, Sanqiang Liu, Taochun Wang et al.
4 citations
IEEE Internet of Things Journal
Wireless Body Area Networks
0.74
article

Many-to-One Lightweight Batch Authentication Key Agreement for Wireless Body Area Networks

Dong Xie, Weixin Bian, Sanqiang Liu, Taochun Wang, Zhiyang Li, Fulong Chen
article en
4 citations

Abstract

Wireless body area networks (WBANs) are widely used in the medical field, which makes it easier for doctors to gather patients’ physiological information to diagnose their physical status. Recently, a variety of lightweight authentication key agreement (AKA) protocols have been proposed for WBANs. However, the computational overhead of the authentication process for most of these protocols increases exponentially when the number of patients increases dramatically. Therefore, to improve the authentication efficiency, a many-to-one, lightweight batch AKA protocol based on the Chinese remainder theorem is proposed, which can realize that cloud server (CS) interacts with the gateway nodes once to obtain multiple session keys with different sensor nodes, so as to achieve the effect that multiple sensor nodes participate in the AKA at the same time. In addition, only hash function, XOR operation, and symmetric encryption are used in the proposed protocol. During the key agreement process, CS can compute the session key between it and the sensor node on demand. Sensor nodes can dynamically join and leave the WBAN, which increases the flexibility of the protocol. We verify the protocol’s security using formal security analysis tools, such as the real-or-random model, Burrows-Abadi–Needham logic, and the ProVerif tool. The experiment and comparison results indicate that the proposed protocol outperforms the other related protocols in terms of efficiency.

IEEE Internet of Things JournalVol. 12(6)
Anhui Normal University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 31%
Wireless Body Area Networks
0.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.