Low-Cost Level Deduplication: Design and Inter-Node Consistency Evaluation in Indoor Industrial Environments
Downloads
Indoor air quality (IAQ) monitoring in industrial spaces is vital to protect workers from particulate matter exposure (PM₁, PM₂.₅, PM₁₀). Yet, many low-cost IoT systems prioritize outdoor, wide-area deployments and rarely confront two issues that matter indoors: inter-node measurement consistency when multiple identical sensors are co-located, and firmware-level transmission efficiency for Wi-Fi nodes operating under energy and bandwidth constraints. This work addresses both by presenting a reproducible, low-cost IAQ node built on an ESP32 (S3) and a PMS7003, coupled with a lightweight, on-device data-deduplication routine that suppresses redundant packets before they reach the network stack. The node integrates temperature–humidity sensing, RTC-GNSS for stable timestamps, local SD logging, a compact display for in-situ readouts, and standard Wi-Fi for infrastructure-friendly connectivity, enabling autonomous operation with optional MQTT back-end integration.
We evaluate the design via a 24-hour co-location test of four identical nodes in a controlled indoor room (5-minute sampling). Minute-aligned time series are analyzed using one-way ANOVA to quantify inter-node agreement. Results indicate no statistically significant differences among nodes for PM₁, PM₂.₅, and PM₁₀ (p > 0.05), confirming internal consistency suitable for simultaneous multi-point monitoring. The deduplication routine reduces transmissions by ≈3.2% without information loss, modest per device, but compounding across dense deployments to lower network load and energy use. Together, these outcomes validate (i) a practical hardware–firmware stack for low-cost IAQ sensing in indoor factories, (ii) a deployable firmware strategy for network-efficient reporting, and (iii) an empirical inter-node consistency assessment using co-location and ANOVA. The approach facilitates scalable, accurate, and efficient IAQ surveillance for occupational safety programs and compliance workflows. Future work will extend to longer horizons, drift characterization, and integration with adaptive, event-driven analytics and calibration pipelines for robust industrial rollouts.
Copyright (c) 2026 Agus Purnomo, Asep Andang, Siti Badriah (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
