Development of a lightweight algorithm for real-time temperature monitoring using arduino-based IoT sensors

Resource-constrained microcontrollers present computational challenges for implementing sophisticated data processing algorithms in Internet of Things applications. This research describes the development and validation of a lightweight algorithm designed specifically for real-time temperature monitoring on Arduino-based platforms with limited memory and processing capabilities. The algorithm employs adaptive sampling techniques combined with efficient data compression to minimize transmission overhead while preserving measurement accuracy within acceptable tolerances for environmental monitoring applications. Experimental evaluation conducted at the Sydney Institute of Technology between February and October 2023 demonstrated that the proposed algorithm achieved processing times averaging 24.3 milliseconds per data packet, representing a 67.2% reduction compared to standard approaches running on identical hardware. Memory utilization remained below 1.8 kilobytes throughout testing, leaving sufficient resources for concurrent sensor management and communication tasks on ATmega328P microcontrollers with only 2 kilobytes of SRAM. Field deployment across a network of 32 sensor nodes monitoring greenhouse environmental conditions confirmed stable operation over 180 days of continuous service. Temperature measurement accuracy remained within ±0.4 degrees Celsius of reference instrumentation while data transmission volume decreased by 58.3% through intelligent aggregation of redundant readings. The algorithm demonstrates that careful optimization enables sophisticated monitoring capabilities on low-cost hardware platforms accessible to small-scale agricultural and research applications.