By: Bibhu Prasad Ganthia and rosalin pradhan
Assistant Professor, Electrical Engineering, Indira Gandhi Institute of Technology, Sarang Dhenkanal, Odisha, India .
The booming implementation of Internet of Things (IoT) and edge computing devices is requiring the
implementation of efficient signal transmission mechanisms to balance between performance and energy
efficiency. The current paper introduces an AI-assisted conversion and optimization system of the analog-to-digital (A/D) signal transmission, aimed at low-power IoT and edge devices. The given strategy combines both machine learning algorithms and adaptive sampling and quantization methods to optimize the A/D conversion process in real-time depending on the context data patterns and the network characteristics. The framework uses reinforcement learning and neural network models to predict optimal sampling rates and bit resolutions that use the least amount of energy at the expense of signal fidelity. The experimental measurements show a maximum reduction of power consumption (up to 35 percent) and 25 percent more efficiency in the transmission of any conventional fixed-rate A/D systems. Moreover, AI-aided system guarantees reliability in communication when the environmental and channel conditions vary, which can be applied in settings with limited resources. This study will bring a scalable, intelligent, and energy-conscious signal processing paradigm that is capable of supporting the increasing need to have sustainable and autonomous IoT and edge device ecosystems.
AI-assisted A/D conversion; signal-to-noise ratio (SNR); edge computing; low-power IoT; adaptive signal
processing; energy efficiency; intelligent transmission; machine learning; quantization optimization; embedded intelligence .
Citation:
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