Name
Abstract | ||
|---|---|---|
Constant expansion of Internet of Things (IoT) devices creates considerable convenience for peoples' daily lives; however, it also brings about some challenges. Advanced modern techniques equip IoT devices with additional sensory and communication modules, enabling them to perform more functions than just sensing, but it also means that these devices may consume more energy. Many IoT devices are powered by batteries of limited life and composed of electronic parts of limited capacity. Therefore, energy efficiency is an essential requirement for any IoT device with limited resources in terms of storage space, power, and computing capability. Moreover, possible attack vectors and attack sources for vulnerabilities in the IoT environment present another great challenge for secure user authentication. To address the above issues, in this article we propose a privacy- preserving lightweight biometric system, specifically designed for resource-limited IoT devices to save memory and computational cost. The proposed system employs a block logic operation based algorithm to reduce the biometric feature size in a simple and efficient way. Experimental results demonstrate that with a much reduced feature size, the proposed lightweight biometric system still maintains high recognition accuracy. It is worth noting that any sizable reduction in memory and computing cost is beneficial for resource-constrained IoT devices. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/MCOM.2019.1800378 | IEEE Communications Magazine |
Keywords | Field | DocType |
Internet of Things,Authentication,Feature extraction,Fingerprint recognition,Green products,Measurement | Authentication,Efficient energy use,Computer science,Fingerprint recognition,Internet of Things,Computer network,Feature extraction,Biometrics,Biometric system | Journal |
Volume | Issue | ISSN |
57 | 3 | 0163-6804 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wencheng Yang | 1 | 85 | 10.34 |
| Song Wang | 2 | 321 | 16.09 |
| Guanglou Zheng | 3 | 57 | 7.07 |
| JuCheng Yang | 4 | 72 | 7.49 |
| Craig Valli | 5 | 132 | 33.90 |