Citation

Phan, Raphael and Chang, Chin Chen and Lee, Wai Kong and Liu, Yanjun and Goi, Bok Min (2018) Signature Gateway: Offloading Signature Generation to IoT Gateway Accelerated by GPU. IEEE Internet of Things Journal, 6 (3). pp. 4448-4461. ISSN 2327-4662, 2372-2541

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Abstract

The emergence of Internet of Things (IoT) brings us the possibility to form a well connected network for ubiquitous sensing, intelligent analysis, and timely actuation, which opens up many innovative applications in our daily life. To secure the communication between sensor nodes, gateway devices and cloud servers, cryptographic algorithms (e.g., digital signature, block cipher, and hash function) are widely used. Although cryptographic algorithms are effective in preventing malicious attacks, they involve heavy computation that may not be executed efficiently in resource constraint sensor nodes. In particular, the authentication of a sensor node is usually performed through a digital signature (e.g., RSA and elliptic curve cryptography), which can be slow when executed on a microcontroller. In this paper, an IoT architecture that offloads the digital signature generation to a nearby signature gateway equipped with graphic processing unit (GPU) accelerator are proposed. The communication process for signature offloading, together with optimized implementation techniques for RSA in signature gateway, are also presented in this paper. We have evaluated two different ways to implement modular exponentiation in RSA, namely residue number system and multiprecision montgomery multiplication (MPMM). The experimental results show that our RSA implementation using MPMM is 10.1% faster than the best RSA implementation in GPU. Our proposed IoT architecture with signature gateway can successfully reduce the burden of sensor nodes to generate signatures, at the same time preserve the ability to authenticate the sensor nodes.

Item Type:	Article
Uncontrolled Keywords:	Cloud computing
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	03 May 2021 17:30
Last Modified:	03 May 2021 17:30
URII:	http://shdl.mmu.edu.my/id/eprint/7665

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