Collaborative Edge Computing In IOT: Audio Signal Processing With Low-Power VLSI Implementation

Abstract

  This paper analyzes the synthesis of collaborative edge computing and low-power VLSI design for audio in the IoT paradigm. As the need for real-time audio processing on low-power platforms grows, the use of these CPUs seems to offer important benefits in terms of latency, power usage and added capacity. Collaborative edge computing allows the audio data to be processed through near processors hence cutting down on the utilization of cloud resources while at the same time enhancing the systems response rate and ensuring privacy. These systems are then augmented with low-power VLSI design to ensure that applications with high demand for voice will run for a long time without draining battery power.

This paper reviews the essential signal processing techniques common in IoT including noise attenuation, spoken word recognition, and audio categorization, and provides insight into their adoption in numerous domains including home automation, healthcare, industry, and automotive product markets. Moreover, it also examines issues like security threats, numerical capability restrictions, and integration issues Lastly, it describes new prospects like AI implementation and the development of neuromorphic computing.

In conclusion, this report demonstrates that collaborative edge computing and low-power VLSI can revolutionize the authenticated and efficacy of audio processing in IoT applications of different fields laying a paradigm towards more intelligent, efficient, and sustainable systems.