Semantic communication has emerged as a promising paradigm to tackle the challenges of massive growing data traffic and sustainable data communication. It shifts the focus from data fidelity to goal-oriented or task-oriented semantic transmission. While deep learning-based methods are commonly used for semantic encoding and decoding, they struggle with the sequential nature of time series data and high computation cost, particularly in resource-constrained IoT environments. Data compression plays a crucial role in reducing transmission and storage costs, yet traditional data compression methods fall short of the demands of goal-oriented communication systems. In this paper, we propose a novel method for direct analytics on time series data compressed by the SHRINK compression algorithm. Through experimentation using outlier detection as a case study, we show that our method outperforms baselines running on uncompressed data in multiple cases, with merely 1% difference in the worst case. Additionally, it achieves four times lower runtime on average and accesses approximately 10% of the data volume, which enables edge analytics with limited storage and computation power. These results demonstrate that our approach offers reliable, high-speed outlier detection analytics for diverse IoT applications while extracting semantics from time-series data, achieving high compression, and reducing data transmission.

翻译：语义通信已成为应对海量增长数据流量和可持续数据通信挑战的一种有前景的范式。它将关注点从数据保真度转向以目标或任务为导向的语义传输。虽然基于深度学习的方法常用于语义编码与解码，但它们难以处理时序数据的序列特性且计算成本高昂，尤其在资源受限的物联网环境中。数据压缩在降低传输与存储成本方面起着关键作用，然而传统的数据压缩方法无法满足目标导向通信系统的需求。本文提出了一种新颖的方法，用于对由SHRINK压缩算法压缩后的时序数据进行直接分析。通过以异常检测作为案例研究的实验，我们证明该方法在多种情况下优于在未压缩数据上运行的基线方法，在最坏情况下仅存在1%的性能差异。此外，其平均运行时间降低了四倍，且仅访问约10%的数据量，从而实现了在存储和计算能力有限的边缘侧进行分析。这些结果表明，我们的方法能够从时序数据中提取语义、实现高压缩比并减少数据传输，同时为多样化的物联网应用提供可靠、高速的异常检测分析。