Low-power IoT + Embedded edge processing using quaternary/reversible architecture for efficient sensing and monitoring

The proposed system presents a low-power, intelligent IoT-based edge processing architecture designed to enable efficient and sustainable resource monitoring for smart campus environments. The core idea is to reduce energy consumption and improve data efficiency at the device level by integrating quantum-inspired quaternary processing with embedded IoT systems. Conventional IoT devices use binary processing, which results in higher switching activity, increased power consumption, and frequent battery replacement. To address this limitation, the system converts incoming binary sensor data into quaternary representation, where each signal carries two bits of information, thereby increasing data density and reducing processing overhead. The architecture consists of sensor nodes connected to a low-power microcontroller (such as ESP32) that performs binary-to-quaternary encoding and optimized edge-level processing. Reversible logic principles are applied in software to minimize unnecessary computations and switching activity, resulting in lower dynamic power consumption and reduced heat generation. Processed data is then transmitted through Wi-Fi, BLE, or LoRa to a centralized dashboard for real-time monitoring and analysis. This approach enables longer device lifetime, reduced maintenance costs, and scalable deployment for applications such as energy monitoring, environmental sensing, and smart infrastructure management. The system supports sustainable campus operations by providing efficient, reliable, and future-ready edge intelligence for large-scale IoT networks.