A Measurement-Based Study of Data Transmission  Energy in Long-Term IoT Environmental

Jaratpong Tepmanee; Dumrongsak Wongta; Satawat Muangchuen

doi:10.55003/ETH.430207

Authors

Jaratpong Tepmanee Faculty of Engineering, Chiangrai College, Thailand
Dumrongsak Wongta Faculty of Engineering, Chiangrai College, Thailand
Satawat Muangchuen Faculty of Engineering, Chiangrai College, Thailand

DOI:

https://doi.org/10.55003/ETH.430207

Keywords:

Energy Consumption Measurement, Long-Term Environmental Monitoring, Low-Power Wireless Sensor Networks, Measurement-Based Energy Analysis, Internet of Things (IoT)

Abstract

Long-term Internet of Things (IoT) deployments require predictable and realistic energy consumption estimates to ensure sustainable operation and effective maintenance planning. While many existing studies focus on energy-efficient protocols duty cycling or adaptive sensing strategies communication energy consumption is often estimated using analytical models or datasheet-based assumptions rather than direct hardware measurements. This limits the reliability of long-term energy estimation in real-world deployments. This paper presents a measurement-based study of data transmission energy consumption in a long-term IoT environmental monitoring system using an ESP32-based platform. The system was deployed continuously for approximately one year to collect environmental data while transmission current was measured experimentally using an external ammeter. By isolating the incremental current associated with each data transmission from baseline standby and sensing consumption, the per-transmission energy cost was experimentally characterized. Statistical analysis of the measured transmission current demonstrates low variability and stable behavior across multiple transmission samples enabling reliable estimation of communication energy over extended operational periods. Based on the measured per-transmission energy a practical framework is established to estimate monthly and annual communication energy consumption under a fixed transmission interval. Rather than proposing new optimization algorithms, this work provides empirical energy measurements derived from real hardware and long-term operation, offering a realistic reference for system designers and researchers. The results support informed battery capacity planning lifetime estimation, and energy-aware system design in environmental and agricultural IoT monitoring applications.

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