Semantic Communication System for Standard Knowledge in Power Iot Networks

Abstract

The growing complexity of power Internet of Things (IoT) networks necessitates efficient and reliable communication capable of handling the continuous stream of data generated by distributed sensors, smart meters, and control systems. To handle this system, this paper proposes a semantic communication system for transmitting standard knowledge in power IoT networks, leveraging deep joint source-channel coding (Deep JSCC) to enhance communication efficiency and resilience. Unlike traditional communication approaches that prioritize bit-level accuracy, semantic communication focuses on conveying the meaning and relevance of information, ensuring that critical control signals and operational data are transmitted accurately, even under noisy channel conditions. The integration of Deep JSCC unifies data compression and error correction into a single neural network, enabling the system to dynamically balance the trade-off between compression efficiency and robustness to interference. The proposed semantic communication system also incorporates reinforcement learning (RL) to optimize network resource allocation on the bandwidth and transmission power, based on the semantic relevance of the transmitted knowledge. Experimental results demonstrate the effectiveness of the system in maintaining high reliability and low latency, even in resource-constrained environments, ensuring seamless grid operation and real-time decision-making. This research offers a novel framework for intelligent communication in power IoT networks, paving the way for sustainable energy management through efficient data handling, adaptive resource optimization, and improved communication reliability.