Software Design and Experimental Evaluation of a Reduced AES for IoT Applications

Malik Qasaimeh, Raad S. Al-Qassas and Mohammad Ababneh
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Malik Qasaimeh: Department of Software Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
Raad S. Al-Qassas: Department of Computer Science, Princess Sumaya University for Technology, Amman 11941, Jordan
Mohammad Ababneh: Department of Computer Science, Princess Sumaya University for Technology, Amman 11941, Jordan

Future Internet, 2021, vol. 13, issue 11, 1-21

Abstract: IoT devices include RFID tags, microprocessors, sensors, readers, and actuators. Their main characteristics are their limited resources and computing capabilities, which pose critical challenges to the reliability and security of their applications. Encryption is necessary for security when using these limited-resource devices, but conventional cryptographic algorithms are too heavyweight and resource-demanding to run on IoT infrastructures. This paper presents a lightweight version of AES (called LAES), which provides competitive results in terms of randomness levels and processing time, operating on GF(2 4 ). Detailed mathematical operations and proofs are presented concerning LAES rounds design fundamentals. The proposed LAES algorithm is evaluated based on its randomness, performance, and power consumption; it is then compared to other cryptographic algorithm variants, namely Present, Clefia, and AES. The design of the randomness and performance analysis is based on six measures developed with the help of the NIST test statistical suite of cryptographic applications. The performance and power consumption of LAES on a low-power, 8-bit microcontroller unit were evaluated using an Arduino Uno board. LAES was found to have competitive randomness levels, processing times, and power consumption compared to Present, Clefia, and AES.

Keywords: lightweight cryptography; randomness analysis; internet of things (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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