Architectures and strategies for low power optimization in the ESP32 ecosystem: A systematic review of the literature
DOI:
https://doi.org/10.37636/recit.v9n4e505Keywords:
ESP32 , low power, embedded systems, hardware and software co-design, Internet of ThingsAbstract
The objective is to identify the optimal configurations and strategies for low power optimization in the ESP32 series microcontroller ecosystem, addressing the need to balance computing power with energy autonomy in autonomous systems. The methodology employed consisted of a structured analysis based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, selecting twenty-eight technical studies. Key findings reveal that efficiency depends not only on deep sleep states but also on a comprehensive, hardware-sensitive co-design paradigm. The reviewed studies demonstrate that the use of native compiled languages achieves execution speeds up to fifteen times faster than interpreted options, while the application of eight-bit quantization techniques reports reductions of up to seventy-three percent in memory footprint. Furthermore, the hybridization of short- and long-range communication protocols optimizes operational resilience in remote environments. It is concluded that these strategies transform the device from a prototyping tool into a robust scientific instrument for edge artificial intelligence. Practical implications include the development of medical biosensors with months of autonomy and low-cost industrial monitoring networks, democratizing access to advanced digital technologies in critical sectors and regions with limited infrastructure.
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References
[1] V. V et al., «A Hybrid IoT and Machine Learning Framework for Smart Greenhouse Automation in Sustainable Agriculture», Int. Res. J. Multidiscip. Technovation, pp. 58-69, jul. 2025, doi: 10.54392/irjmt2545. DOI: https://doi.org/10.54392/irjmt2545
[2] C. A. Popa, I. Dogaru, y R. Dogaru, «An End-to-End Automated Pipeline for EEG Classification on TinyML Platforms: From Signal to On-Device Inference», IEEE Access, vol. 14, pp. 8918-8937, 2026, doi: 10.1109/ACCESS.2026.3651653. DOI: https://doi.org/10.1109/ACCESS.2026.3651653
[3] J. Tepmanee, «A Measurement-Based Study of Data Transmission Energy in Long-Term IoT Environmental», Eng. Technol. Horiz., vol. 43, n.o 2, jun. 2026, doi: 10.55003/ETH.430207. DOI: https://doi.org/10.55003/ETH.430207
[4] E. Arrieta Rodriguez et al., «A Platform for Inpatient Safety Management Based on IoT Technology», Inventions, vol. 7, n.o 4, p. 116, dic. 2022, doi: 10.3390/inventions7040116. DOI: https://doi.org/10.3390/inventions7040116
[5] V. Ieropoulos, E. Anthi, T. Spyridopoulos, P. Burnap, P. Carnelli, y A. Khan, «Federated Detection at the Edge: Collaborative Anomaly Detection for Resource-Limited IoT», IEEE Internet Things J., vol. 13, n.o 11, pp. 24205-24218, jun. 2026, doi: 10.1109/JIOT.2026.3674644. DOI: https://doi.org/10.1109/JIOT.2026.3674644
[6] J. A. De La Torre, F. Rincón, S. Escolar, A. Caruso, J. Caba, y J. Barba, «Extreme Edge Computing for Secure and Private Multimodal Biometric Identification in Intelligent IoT Systems», Sensors, vol. 26, n.o 12, p. 3756, jun. 2026, doi: 10.3390/s26123756. DOI: https://doi.org/10.3390/s26123756
[7] P. Rojas-Carrasco y M. Guinaldo, «Multi-Objective Evaluation of Lightweight AI Models on Low-Cost Edge Devices Using Pareto Fronts», Appl. Sci., vol. 16, n.o 8, p. 3679, abr. 2026, doi: 10.3390/app16083679. DOI: https://doi.org/10.3390/app16083679
[8] A. Abdelli et al., «Field Validated Hybrid ESP-NOW and Long Range IoT Monitoring System for Energy Autonomous Precision Agriculture», IEEE Access, vol. 13, pp. 207511-207526, 2025, doi: 10.1109/ACCESS.2025.3641487. DOI: https://doi.org/10.1109/ACCESS.2025.3641487
[9] A. K. Anim, W. Li, X. Zhao, J. Ma, R. Liu, y D. Sun, «Design and Laboratory Validation of a Low-Cost Vision-Based Strain Monitoring System Using ESP32-CAM with Centralized Processing», Buildings, vol. 16, n.o 9, p. 1681, abr. 2026, doi: 10.3390/buildings16091681. DOI: https://doi.org/10.3390/buildings16091681
[10] M. Kazeneza, A. S. Bosman, D. K. Amenyedzi, D. Hanyurwimfura, E. Ndashimye, y A. Vodacek, «Balancing Complexity and Performance of Machine Learning Models for Avian Pests Sound Detection in Agricultural Environments», IEEE Access, vol. 13, pp. 105813-105827, 2025, doi: 10.1109/ACCESS.2025.3580620. DOI: https://doi.org/10.1109/ACCESS.2025.3580620
[11] V.-K. Nguyen, V.-K. Tran, y C.-N. Nguyen, «Fruit Fly Detection Based on Wingbeat Sound Using Embedded Artificial Intelligence», Int. J. Technol., vol. 17, n.o 3, p. 1012, may 2026, doi: 10.14716/ijtech.v17i3.8284. DOI: https://doi.org/10.14716/ijtech.v17i3.8284
[12] I. Saradopoulos, I. Potamitis, S. Ntalampiras, A. I. Konstantaras, y E. N. Antonidakis, «Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities», Sensors, vol. 22, n.o 5, p. 2006, mar. 2022, doi: 10.3390/s22052006. DOI: https://doi.org/10.3390/s22052006
[13] L.-C. Duan, N.-N. Minh, T.-C. Dung, y T.-T.-T. Linh, «Energy-Efficient TinyML Approach for Wearable Fall Detection on Edge Devices Using Spatial-Temporal Deep Learning», Int. J. Technol., vol. 17, n.o 3, p. 919, may 2026, doi: 10.14716/ijtech.v17i3.8445. DOI: https://doi.org/10.14716/ijtech.v17i3.8445
[14] K. Liu, Q. Zhao, R. Wang, Y. Lin, J. Yu, y S. J. Fong, «STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments», Sensors, vol. 26, n.o 12, p. 3692, jun. 2026, doi: 10.3390/s26123692. DOI: https://doi.org/10.3390/s26123692
[15] H. Najeh, C. Lohr, y B. Leduc, «Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study», Appl. Sci., vol. 14, n.o 6, p. 2377, mar. 2024, doi: 10.3390/app14062377. DOI: https://doi.org/10.3390/app14062377
[16] A. A. Gilani, S. I. Butt, S. H. I. Jaffery, A. Abid, y S. Ali, «Design, development and evaluation of solar powered DC cold box for sustainable supply chain of food and medicine», Int. J. Sustain. Eng., vol. 19, n.o 1, p. 2619276, dic. 2026, doi: 10.1080/19397038.2026.2619276. DOI: https://doi.org/10.1080/19397038.2026.2619276
[17] N. E. Guevara, Y. H. Bolaños, J. P. Diago, y J. M. Segura, «Development of a low-cost IoT system based on LoRaWAN for monitoring variables related to electrical energy consumption in low voltage networks», HardwareX, vol. 12, p. e00330, oct. 2022, doi: 10.1016/j.ohx.2022.e00330. DOI: https://doi.org/10.1016/j.ohx.2022.e00330
[18] A. H. Kumar et al., «WiMapper: A Lightweight Kernel-Based Framework for Rogue Access Point Detection on Edge Devices», IEEE Access, vol. 14, pp. 57806-57827, 2026, doi: 10.1109/ACCESS.2026.3682631. DOI: https://doi.org/10.1109/ACCESS.2026.3682631
[19] J. Paguay, A. Cuenca-Sánchez, y P. Proaño, «Smart IoT-Enabled Embedded Platform for Real-Time Energy Efficiency Assessment in Heat Pumps», en The XXXIII Conference on Electrical and Electronic Engineering, MDPI, nov. 2025, p. 14. doi: 10.3390/engproc2025115014. DOI: https://doi.org/10.3390/engproc2025115014
[20] M. Waqas y M. Jamil, «Smart IoT SCADA System for Hybrid Power Monitoring in Remote Natural Gas Pipeline Control Stations», Electronics, vol. 13, n.o 16, p. 3235, ago. 2024, doi: 10.3390/electronics13163235. DOI: https://doi.org/10.3390/electronics13163235
[21] E. Djafsia Souboura, D. Wohwe Sambo, P. Dayang, M. V. Komguep, y L. Clavier, «FragCast: A Scalable BLE‐Based OTA Framework for Constrained IoT Devices», IET Wirel. Sens. Syst., vol. 16, n.o 1, p. e70031, ene. 2026, doi: 10.1049/wss2.70031. DOI: https://doi.org/10.1049/wss2.70031
[22] K. Gac, J. Gorski, G. Gora, y J. Iwaniec, «Energy-Oriented Wireless Communication Platform Selection System in the Internet of Things», Sensors, vol. 26, n.o 10, p. 3158, may 2026, doi: 10.3390/s26103158. DOI: https://doi.org/10.3390/s26103158
[23] G. Kumar et al., «Design of a Lightweight Edge-AI System for Predictive Maintenance on ESP32-S3», Appl. Sci., vol. 16, n.o 11, p. 5287, may 2026, doi: 10.3390/app16115287. DOI: https://doi.org/10.3390/app16115287
[24] D. Sarabia-Jácome, E. Grasa, y M. Catalán, «RINAsense: An open-source, low-power IoT sensor node for Recursive InterNetwork Architecture (RINA) Experimentation», HardwareX, vol. 24, p. e00719, dic. 2025, doi: 10.1016/j.ohx.2025.e00719. DOI: https://doi.org/10.1016/j.ohx.2025.e00719
[25] J. Pei, M. Feng, P. Muthukumar, P. V. Krishna, y M. Dai, «Generative AI-Native Edge Sensors: Collaborative Data Reconstruction via Lightweight Federated Learning», IEEE J. Sel. Areas Sens., vol. 3, pp. 180-190, 2026, doi: 10.1109/JSAS.2026.3680380. DOI: https://doi.org/10.1109/JSAS.2026.3680380
[26] J. García-González, C. Q. Gómez Muñoz, D. Gachet Páez, y J. Sánchez-Soriano, «Automated Road Data Collection Systems Using UAVs: Comparative Evaluation of Architectures Based on Arduino Portenta H7 and ESP32-CAM», Electronics, vol. 14, n.o 21, p. 4165, oct. 2025, doi: 10.3390/electronics14214165. DOI: https://doi.org/10.3390/electronics14214165
[27] A. Pérez-González, A. F. Villa-Salazar, I. N. Gomez-Miranda, J. D. Velásquez-Gómez, A. F. Romero-Maya, y Á. Jaramillo-Duque, «An IoV-Based Real-Time Telemetry and Monitoring System for Electric Racing Vehicles: Design, Implementation, and Field Validation», Vehicles, vol. 7, n.o 4, p. 128, nov. 2025, doi: 10.3390/vehicles7040128. DOI: https://doi.org/10.3390/vehicles7040128
[28] H. M. Vasconcelos et al., «A Low-Power Architecture for Passive Acoustic Autonomous Maritime Surveillance», J. Mar. Sci. Eng., vol. 14, n.o 9, p. 815, abr. 2026, doi: 10.3390/jmse14090815. DOI: https://doi.org/10.3390/jmse14090815
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