Physical Layer Authentication for Extending Battery Life

Loading...
Publication Logo

Date

2021

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Open Access Color

Green Open Access

No

OpenAIRE Downloads

OpenAIRE Views

Publicly Funded

No
Impulse
Average
Influence
Average
Popularity
Top 10%

Research Projects

Journal Issue

Abstract

Increasing population density in cities, and the increasing demand for efficiency in resource usage call for architectures enabling smart cities, such as the Internet of Things (IoT). In most such scenarios, the data generated by IoT sensors is not confidential, but its integrity is critical. Data integrity can be achieved by establishing certification mechanisms that provide cryptographic message authentication protocols; however, this requires relatively expensive components for storing and processing the encryption key on the sensor and consumes more power while processing and transmitting data, which leads to the renunciation of security issues in cost sensitive deployments. In this paper, we propose a security solution that provides data integrity without draining the batteries of IoT sensors. Our solution consists of, (i) differentiating legitimate sensors by taking advantage of their impurities formed during the manufacturing process of the transceiver components, and (ii) eliminating the complex components that carry out cryptography as well as the redundant packet header fields, thereby yielding power savings. The testbed implementation of the proposed solution yields power measurement results providing an estimate of 2.52 times improvement in battery life without compromising the integrity of communications in the system, in addition to offering an increase in spectral efficiency and a decrease in the overall IoT device cost.

Description

Gelal Soyak, Ece/0000-0003-2410-6267; Cetin, Ramazan/0000-0002-5371-3106; Ayyildiz, Cem/0009-0009-7297-916X;

Keywords

Internet of Things (IoT), Smart City, Physical Layer Security, Rf Fingerprinting, Battery Life, Convolutional Neural Networks (Cnn), Energy Efficient

Fields of Science

0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Citation

WoS Q

Q1

Scopus Q

Q1
OpenCitations Logo
OpenCitations Citation Count
4

Source

Ad Hoc Networks

Volume

123

Issue

Start Page

End Page

PlumX Metrics
Citations

CrossRef : 4

Scopus : 3

Captures

Mendeley Readers : 25

SCOPUS™ Citations

4

checked on Apr 18, 2026

Web of Science™ Citations

2

checked on Apr 18, 2026

Page Views

3

checked on Apr 18, 2026

Downloads

2

checked on Apr 18, 2026

Google Scholar Logo
Google Scholar™
OpenAlex Logo
OpenAlex FWCI
0.6113

Sustainable Development Goals

AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES