Priyanka Rawat | Searcharxiv

arXiv2026

CBL: Compact Encoding of JSON-LD Data using CBOR and Bitmaps for Web of Things

The concept of Web of Things (WoT) merges web technologies with knowledge graphs in the context of Internet of Things. Given its widespread adoption in representing and exchanging structured data online, JSON-LD could be an effective format for WoT. Nevertheless, its verbose nature may present challenges for resource-constrained IoT devices with limited bandwidth and memory capacities. In this paper, we present a novel approach to compactly represent JSON-LD data using the Concise Binary Object Representation (CBOR) and bitmaps. Our proposed method is named as CBL which stands for CBOR, Bitmap and List of Key-value pairs. CBL leverages the ideas from CBOR and HDT to achieve an efficient encoding of JSON-LD data. Results demonstrate that our approach provides savings up to 95.1% in terms of network overhead. This could be especially beneficial for IoT devices exchanging data over wireless networks. Moreover, our approach is more efficient than the current approach known as CBOR-LD, which is used to compact JSON-LD data.

cs.NI↗

arXiv2025

TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data

Quality of Experience (QoE) modeling is crucial for optimizing video streaming services to capture the complex relationships between different features and user experience. We propose a novel approach to QoE modeling in video streaming applications using interpretable Machine Learning (ML) techniques over raw time series data. Unlike traditional black-box approaches, our method combines Kolmogorov-Arnold Networks (KANs) as an interpretable readout on top of compact frequency-domain features, allowing us to capture temporal information while retaining a transparent and explainable model. We evaluate our method on popular datasets and demonstrate its enhanced accuracy in QoE prediction, while offering transparency and interpretability.

cs.LG↗