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COMPARE PERFORMANCE METRICS OF DIFFERENT EMBEDDED SYSTEM
ARCHITECTURES FOR SPECIFIC IOT USE CASES
M.M. Usmonov
L.J. Asretdinova
Department of Automatic Control and Computer Engineering
Turin Polytechnic University in Tashkent
https://doi.org/10.5281/zenodo.14555097
Abstract
: The growing IoT landscape demands embedded system architectures that
balance performance and resource constraints. This study compares ARM Cortex-M, RISC-V,
and ESP32 architectures in industrial automation and healthcare applications. Key metrics,
including latency, power consumption, and throughput, were evaluated using benchmarks
and real-world workloads. Results show ESP32 achieving TCP throughput of 12–15 Mbps and
UDP throughput of 35–40 Mbps, ideal for wireless communication. ARM Cortex-M offers
versatility, RISC-V excels in energy efficiency, and ESP32 leads in connectivity. These findings
guide IoT system architects in selecting hardware tailored to specific requirements, advancing
embedded IoT system design.
Index Terms:
Embedded Systems, Internet of Things (IoT), Performance Metrics,
System Benchmarking, IoT Architectures, Power Consumption, Real-time Systems.
I. Introduction:
The rapid growth of the Internet of Things (IoT) has connected devices across sectors
like industrial automation and healthcare, relying heavily on embedded systems for
computational and interfacing capabilities. The choice of an embedded system architecture
significantly affects IoT performance, energy efficiency, and reliability.
Key architectures in IoT deployments include ARM Cortex-M, RISC-V, and ESP32. ARM
Cortex-M balances performance and power efficiency, making it versatile for various
applications [1]. RISC-V, an open-source architecture, offers customization and energy
efficiency, gaining popularity in embedded design [2]. ESP32, with integrated Wi-Fi and
Bluetooth, is widely used in applications requiring wireless connectivity [3].
Choosing the right architecture is challenging due to varying performance demands and
constraints across use cases. IoT systems face limited computational resources, memory, and
power supply, requiring efficient designs [4]. Security and privacy must be maintained
without compromising performance [4], and interoperability between heterogeneous devices
is essential [4]. Scalability is also critical as IoT networks grow, requiring architectures to
handle increased devices and data without performance loss [4]. Real-time performance is
vital in areas like industrial automation and healthcare, where timely processing is mandatory
[5].
Evaluating architectures involves benchmarking key metrics like latency, power
consumption, and throughput to provide empirical comparisons [6]. Real-world workload
testing ensures practical relevance [7], while power consumption analysis identifies energy-
efficient designs critical for battery-powered devices [8]. These evaluations help system
architects select architectures aligned with application-specific requirements, ensuring
optimal performance and reliability.
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II. Methodology:
Numerous studies have compared embedded system architectures in IoT applications,
employing various methodologies to evaluate metrics like processing efficiency, power
consumption, and application suitability. Guth et al. developed a reference architecture to
benchmark platforms such as OpenMTC, FIWARE, and AWS IoT, offering insights into the
interoperability and heterogeneity challenges of IoT platforms [9]. Banu analyzed cloud-
centric and fog computing models, examining how data processing locations affect
performance, scalability, and real-time capabilities, emphasizing the critical role of
architectural choices [10]. Safaei et al. evaluated IoT operating systems (OSs) by assessing
their architectural features, power consumption, CPU utilization, and memory efficiency in
real-world settings, highlighting OS design impacts in resource-constrained environments
[11]. Domínguez-Bolaño et al. reviewed IoT platforms and technologies, conducting a
comparative analysis to guide organizational platform selection based on essential
characteristics [12]. Fahmideh and Zowghi applied an evaluation framework to assess nine
IoT architectures for smart city applications, identifying strengths and weaknesses to aid
stakeholders in architectural decision-making [13]. These studies collectively provide diverse
perspectives on evaluating embedded system architectures for IoT applications.
These studies collectively contribute to the understanding of embedded system
architectures in IoT applications, offering diverse methodologies for evaluating and
comparing performance metrics. The insights gained from these analyses assist in making
informed decisions regarding architecture selection tailored to specific IoT use cases and
requirements.
III. Results
This section compares ARM Cortex-M, RISC-V, and ESP32 architectures in industrial
automation and smart healthcare, focusing on processing latency, power consumption, and
data throughput. ARM Cortex-M, particularly the Cortex-M4, achieves low
latency
with 1.25
DMIPS/MHz at 225 MHz, supported by DSP instructions [1]. The ESP32, with a dual-core
design at 240 MHz, offers 600 DMIPS but may show higher latency than ARM Cortex-M due to
memory and cache limitations [1]. RISC-V, customizable for specific tasks, often lags ARM
Cortex-M in general-purpose performance due to less optimization [1]. For
power efficiency
,
ARM Cortex-M consumes ~100 mA, aided by energy-saving features. ESP32 uses ~70 mA
under standard operations, though power increases with peripherals, offset by configurable
low-power modes [1]. RISC-V minimizes power through streamlined designs, with efficiency
varying by implementation [1]. In
data throughput
, ARM Cortex-M efficiently handles data-
heavy tasks, while ESP32 achieves TCP speeds of 12–15 Mbps and UDP speeds of 35–40 Mbps
via integrated wireless modules [1]. RISC-V throughput depends on core design and
peripherals [1].
Overall, ARM Cortex-M is ideal for low-latency, energy-efficient tasks; ESP32 excels in
wireless data handling; and RISC-V offers flexibility with performance depending on
implementation. These results emphasize aligning architecture choice with application
needs.The following table summarizes the comparative performance metrics of the ARM
Cortex-M4, RISC-V-based platforms, and ESP32 systems across key parameters:
IV. Conclusions
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This study provides a comparative evaluation of ARM Cortex-M, RISC-V, and ESP32
architectures, focusing on key metrics such as processing latency, power consumption, and
data throughput within the contexts of industrial automation and smart healthcare. ARM
Cortex-M proved highly versatile and efficient for low-latency, power-sensitive applications,
while ESP32 demonstrated exceptional wireless connectivity and data handling capabilities.
RISC-V's flexibility and customization potential make it suitable for energy-constrained and
specialized use cases, though its general-purpose performance depends heavily on
implementation. The findings emphasize the importance of aligning architecture selection
with specific application demands, offering valuable insights to IoT system architects. This
comparative analysis contributes to optimizing embedded system design for diverse IoT
deployments.
References:
1.
"Arm Cortex M4 vs ESP32," SoC, [Online]. Available: https://s-o-c.org/arm-cortex-m4-
vs-esp32/
2.
"RISC-V vs. ARM: Who wins in 8 categories?," Paessler Blog, [Online]. Available:
https://blog.paessler.com/risc-v-vs-arm-who-wins
3.
"Espressif SoC Family Comparison: Find the Ideal SoC for Your Next IoT Project," Ineltek,
[Online].
Available:
https://www.ineltek.co.uk/post/espressif-soc-family-comparison-
applications
4.
A. Chauhan, "Top 10 Challenges in IoT Embedded System Design and How to Overcome
Them,"
TechAhead,
Aug.
2023.
[Online].
Available:
https://www.techaheadcorp.com/blog/top-10-challenges-iot-embedded-system-design-how-
overcome-them/
5.
U. Eswaran, "Real-Time Operating Systems in the Era of IoT: Challenges and Solutions
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and Real-world Challenges of IoT-Based Smart Fuel Filling Systems with Embedded
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8.
Y. Chen, W. Li, and X. Zhang, "The Challenges and Emerging Technologies for Low-Power
Artificial Intelligence IoT Systems," IEEE Access, vol. 9, pp. 110513–110524, 2021.
9.
J. Guth, U. Breitenbücher, M. Falkenthal, F. Leymann, and L. Reinfurt, "Comparison of IoT
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N. M. M. Banu, "IoT Architecture: A Comparative Study,"
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B. Safaei, M. A. Zamani, and M. Conti, "From Kernel to Cloud: A Concise Comparative
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28–34, 2020.
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12.
T. Domínguez-Bolaño, O. Campos, V. Barral, C. J. Escudero, and J. A. García-Naya, "An
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M. Fahmideh and D. Zowghi, "IoT Smart City Architectures: An Analytical Evaluation,"
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