Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/115667
Title: Research on Energy Efficiency of Wi-Fi IoT Systems on Renesas DA16200 Platform
Author(s): Kondratiev, Serhii
Koshutina, Daria
Liubomska, Olha
Baskov, Illia
Issue Date: 2024
Language: English
Subjects: Informationstechnik
Datenverarbeitung
Abstract: This research focuses on a comprehensive analysis of the energy efficiency of the Renesas DA16200 microcontroller. The investigation adopts a comparative approach, directly contrasting the power consumption of the DA16200 with the widely used ESP8266 controller under identical operating conditions. The primary metric employed to assess energy efficiency is average battery life. Additionally, a detailed examination of current consumption is conducted across various operational modes, encompassing active states like data exchange, reception, and transmission, as well as low-power sleep mode. This analysis extends beyond simply measuring peak current draw. Transient current profiles are captured, providing time-resolved insights into how current consumption fluctuates throughout different operational phases. This granular data enables a deeper understanding of the microcontrollers' energy utilization patterns. Furthermore, the research explores and evaluates techniques for minimizing energy consumption specifically in the ESP8266. These findings are then juxtaposed against the inherent energy-saving features of the DA16200 microcontroller. To facilitate a precise and verifiable comparison, a custom test bench accommodating both the DA16200 and ESP8266 is designed and implemented. his controlled environment ensures consistency in operating conditions and minimizes external variables that could influence the results. The culmination of this research is the presentation of a comprehensive analysis, detailing the comparative energy consumption profiles of the studied microcontrollers. This data forms the foundation for objectively evaluating their suitability for various low-power the Internet of Things applications.
URI: https://opendata.uni-halle.de//handle/1981185920/117622
http://dx.doi.org/10.25673/115667
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Appears in Collections:International Conference on Applied Innovations in IT (ICAIIT)

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