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@article{194795,
        author = {Shreeyansh Srivastava and Nitin Kamia and Mr. Saharsh Gera},
        title = {Design and Deployment of a Multi-Parameter IoT-Based Weather Monitoring System Using ESP32 Microcontroller, Sensor Fusion and Cloud Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {5295-5305},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194795},
        abstract = {A key component of contemporary agricultural management, urban infrastructure planning, disaster preparedness, and climate change mitigation is environmental monitoring. Despite their high accuracy, traditional weather stations have limited spatial coverage, high installation and maintenance costs, and substantial data latency that makes it difficult to make decisions in real time. The complete design, hardware building, firmware development, and field implementation of an inexpensive, multi-parameter Internet of Things (IoT) weather monitoring system based on the ESP32 dual-core microcontroller are presented in this paper. Five complementary modules are integrated into the sensing subsystem: a resistive rain sensor, a light-dependent resistor (LDR) module, a BMP180 barometric pressure sensor, a DHT11 temperature and relative-humidity sensor, and a 16x2 LCD display for local visualization. A five-stage acquisition cycle—sampling, validation, local display, cloud transmission, and delay—is implemented by firmware created in the Arduino IDE and is carried out at a 30-second cadence. Real-time cloud visualization, threshold-based alerting, and MATLAB-driven analytics are made possible by publishing sensor readings to Thing Speak via the lightweight MQTT protocol over IEEE 802.11 Wi-Fi. At a height of 495 meters above sea level, a 21-day field assessment produced 60,480 planned transmissions, of which 59,484 were successfully recorded, resulting in a packet delivery ratio (PDR) of 98.3%. Within the specified ±2 °C tolerance of the sensor, the DHT11 temperature channel obtained a mean absolute error (MAE) of 1.4 °C with respect to a calibrated reference thermometer. The suggested architecture achieves a competitive PDR and sensor accuracy at a total bill-of-materials cost of about INR 1,462, according to a comparison with twelve published IoT weather monitoring systems. This makes it especially appropriate for large-scale distributed deployment in resource-constrained environments.},
        keywords = {Internet of Things (IoT), ESP32 microcontroller, DHT11 sensor, BMP180 barometric sensor, Thing Speak cloud platform, MQTT protocol, sensor fusion, weather monitoring, embedded systems, precision agriculture},
        month = {March},
        }