Internet of Things Security: Encryption Capacity Comparison for IoT Based on Arduino Devices.
2020 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Student thesis
Abstract [en]
Background: IoT is a system of devices with unique identifiers (UIDs) and can transfer data over a network. They are widely used in various sectors such as Health, Commercial, Transport, etc. However, most IoT devices are being exploited, as it is being recorded for the past few years, on how vulnerable users can be if they have any of these devices in their network. Arduino is one of the most commonly used IoT devices, notable products such as Uno and Mega2560 is highly acceptable in the market and the research world. It is important to know how these devices react to security measures such as encryptions.
Objectives: To carry out a theoretical study and performance comparison on Arduino devices and standard cryptographic encryption. The devices and encryption used are Arduino Uno, Mega2560 and AES, XXTEA respectively.
Methods: To gain knowledge and information about the selected algorithms and devices, a literature analysis was adopted. An experiment was also carried out to get measurements and record how the algorithms perform on these devices.
Results: The literature analysis provides the design similarities and differences of the algorithms and devices. The controlled experiment shows the measurement of the stated encryptions on the Arduino devices.
Conclusions: The conclusion is that Arduino Uno and Mega2560 have a similar design but differ in their memory allocation. The AES and XXTEA algorithm have different designs and performances. The result in the controlled experiment shows that the XXTEA outperforms the AES algorithm in terms of Memory and Time consumption significantly in both devices. The Arduino Uno device is slightly ahead of Mega2560 when comparing the result.
Place, publisher, year, edition, pages
2020. , p. 56
Keywords [en]
Encryption, Security, Arduino, Performance
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-21183OAI: oai:DiVA.org:bth-21183DiVA, id: diva2:1534907
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVADA Master Qualification Plan in Computer Science
Supervisors
Examiners
2021-03-092021-03-052025-09-30Bibliographically approved