ABSTRACT Background Blockchain, as a decentralized and distributed ledger, shows significant application potential and commercial value in the Internet of Things (IoT). However, the dynamicity of IoT nodes introduces significant challenges to traditional blockchain systems. Specifically, in dynamic environments involving node joins/leaves, mobility, and state changes, traditional solutions often suffer from poor security, significant consensus latency, and severe storage overhead. Although blockchain for IoT has been extensively surveyed, prior reviews fail to provide a comprehensive evaluation because the existing literature considers security, consensus, and scalability only as separate issues, while overlooking node dynamics as a fundamental dimension. Objective This paper aims to provide a systematic investigation of blockchain adaptation to node dynamics in IoT and to establish a comprehensive evaluation perspective for analyzing the impacts of node dynamics on security, consensus, and scalability. Methods To this end, this paper systematically investigates these challenges through three contributions. Firstly, a unified taxonomy of node dynamics is proposed to analyze the impacts on security, consensus, and scalability. Secondly, it synthesizes representative solutions across trust evaluation, consensus algorithms, and scalability techniques. Thirdly, it introduces a three‐dimensional evaluation approach to comprehensively analyze these solutions in terms of performance, security, and adaptability. Results The evaluation results illuminate a clear trade‐off among these three dimensions, demonstrating that improving only one aspect often degrades the overall system balance in dynamic environments. Conclusion Finally, this paper introduces critical research limitations and future research directions to provide practical suggestions for effectively deploying blockchain in highly dynamic IoT environments.

ABSTRACT This paper addresses the novel and challenging domain of graph entity dependencies (GEDs) discovery, which aims to identify dependencies in large graphs that are nearly satisfied despite the presence of errors, exceptions and ambiguities in real‐world data. We propose a unique error measure specifically designed for GED semantics and innovatively adapts concepts of disagreement and necessary sets to the realm of graph dependencies. Furthermore, we introduce the FastAGEDs+ algorithm, which significantly enhances efficiency in discovering approximate GEDs, employing a depth‐first search strategy for optimal candidate space traversal. Incorporating an innovative pruning strategy, F ast AGEDs+ efficiently narrows down the search space, significantly reducing computational overhead while maintaining accuracy. Through extensive experimentation on real‐world graphs, we demonstrate the feasibility and scalability of our approach, offering substantial improvements in data quality and management practices.

The cell-free DNA (cfDNA) methylation profile in liquid biopsy has been utilized to diagnose early-stage disease and estimate therapy response. However, typical clinical procedures are capable of purifying only very small amounts of cfDNA. Whole-genome bisulfite sequencing (WGBS) is the gold standard for measuring DNA methylation; however, WGBS using small amounts of fragmented DNA introduces a critical challenge for data analysis, namely a low-mapping ratio. The resulting low sequencing depth and low coverage of CpG sites genome-wide is a bottleneck for the clinical application of cfDNA-based WGBS assays. We developed LiBis (Low-input Bisulfite Sequencing), a novel method for low-input WGBS data alignment. By dynamically clipping initially unmapped reads and remapping clipped fragments, we judiciously rescued those reads and uniquely aligned them to the genome. By substantially increasing the mapping ratio by up to 88%, LiBis dramatically improved the number of informative CpGs and the precision in quantifying the methylation status of individual CpG sites. LiBis significantly improved the cost efficiency of low-input WGBS experiments by dynamically removing contamination introduced by random priming. The high sensitivity and cost effectiveness afforded by LiBis for low-input samples will allow the discovery of genetic and epigenetic features suitable for downstream analysis and biomarker identification using liquid biopsy.