Method of power supply optimization for iot climate monitoring system based on adaptive algorithms

Abstract

Relevance. The rapid growth of the Internet of Things (IoT) has led to the massive deployment of autonomous sensor nodes in remote locations, such as precision agriculture and environmental monitoring. These devices rely heavily on battery power, making energy efficiency a critical factor for system viability and maintenance costs. Traditional static data transmission schedules are inefficient, wasting energy during stable conditions or missing critical data during rapid environmental changes. Therefore, developing adaptive energy management strategies is highly relevant.

Goal. The study aims to develop a method for optimizing the power supply of an IoT climate monitoring system based on adaptive algorithms and to conduct a comparative analysis of the energy efficiency of different communication architectures (Wi-Fi, BLE, LoRaWAN) to identify optimal solutions for various operational scenarios.

Research methods. An experimental-analytical approach was used. The hardware platform was built on the ESP32 microcontroller and BME680 sensor. A finite state machine model was proposed to manage device states, implemented in two paradigms: local adaptation (decision-making on the device) and cloud adaptation (control via AWS Lambda). A series of field measurements were conducted for five communication protocols: HTTP, MQTT, CoAP (over Wi-Fi), BLE, and LoRaWAN, testing four evolutionary software versions from basic to fully adaptive.

Results. The experiments confirmed the effectiveness of the proposed approach. For Wi-Fi networks, switching to the CoAP protocol with an adaptive algorithm reduced the average current consumption from 60.46 mA (baseline) to 12.47 mA, achieving savings of about 79%. For the LoRaWAN architecture, a reduction from 96.78 mA to 12.63 mA (87% savings) was achieved. It was found that cloud-based adaptation is less effective for "heavy" protocols like MQTT due to latency.

Conclusions. The integration of adaptive algorithms that dynamically control the sleep interval allows for a reduction in energy consumption by 70-87% compared to baseline modes. For systems with Wi-Fi infrastructure, the CoAP protocol is the most energy-efficient. For tasks requiring maximum autonomy and range, LoRaWAN with a local adaptive algorithm is the optimal choice.