Design and Performance Evaluation of a Low-Cost Edge-To-Cloud Telemetry System for Industrial Monitoring

Authors

Emina E. Etomi

Federal University of Petroleum Resources (FUPRE), P.M.B. 1221, Effurun, Nigeria (Nigeria)

Benjamin O. Akinloye

Federal University of Petroleum Resources (FUPRE), P.M.B. 1221, Effurun, Nigeria (Nigeria)

Donatus U. Onyishi

Federal University of Petroleum Resources (FUPRE), P.M.B. 1221, Effurun, Nigeria (Nigeria)

Article Information

DOI: 10.51584/IJRIAS.2026.11080034

Subject Category: Education

Volume/Issue: 11/8 | Page No: 460-473

Publication Timeline

Submitted: 2026-08-20

Accepted: 2026-08-25

Published: 2026-09-01

Abstract

Industrial facilities increasingly rely on continuous telemetry acquisition to support condition monitoring, operational awareness, and predictive maintenance. However, many existing edge-to-cloud monitoring systems employ complex communication architectures involving message brokers and middleware, which can increase deployment complexity and computational overhead for low-cost embedded platforms. This study presents the design, implementation, and experimental evaluation of a lightweight edge telemetry system that directly transmits industrial sensor measurements from an ESP32 DevKit V1 to the InfluxDB Cloud time-series database using HTTP. The developed platform integrates an MLX90614 infrared temperature sensor for real-time temperature monitoring, while vibration and electrical measurements were incorporated to validate the complete multi-parameter telemetry pipeline. Telemetry data were transmitted over Wi-Fi, stored in InfluxDB Cloud, and visualized through Grafana for continuous monitoring. Experimental validation was conducted for approximately eleven hours using a one-second sampling interval under representative industrial operating conditions. Performance evaluation demonstrated a mean sensor acquisition time of 1.62 ms, stable free heap memory averaging 230,129 bytes, a mean HTTP communication latency of 2.63 s, and a mean Wi-Fi received signal strength of −22.20 dBm. Successful HTTP response code 204 and continuous telemetry storage confirmed successful edge-to-cloud communication throughout the monitoring period. Analysis of the exported telemetry dataset further demonstrated stable sensor acquisition, firmware execution, wireless connectivity, and cloud data storage. Compared with conventional broker-based architectures, the developed system provides a simpler direct edge-to-cloud telemetry architecture while supporting continuous communication and scalable time-series data storage. The findings demonstrate the capability of low-cost embedded hardware to support continuous industrial telemetry and provide a practical foundation for scalable edge-to-cloud industrial monitoring applications.

Keywords

Edge computing, Industrial Internet of Things (IIoT), ESP32, industrial telemetry, InfluxDB, Grafana, time-series database, condition monitoring

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