The escalating adoption of Internet of Things (IoT) technologies produces vast amounts of real-time data, which can be shared and analyzed to enhance productivity and product quality across various industries. Unfortunately, the current IoT systems rely heavily on cloud servers for centralized data management. These systems not only suffer from the single point of failure problems but also have data security issues. The malicious users may compromise user privacy or inject corrupted data during the data sharing process. In this article, we propose a secure and efficient data sharing scheme for IoT. First, the scheme integrates blockchain with the distributed database, utilizing smart contracts to ensure secure data storage, querying, and sharing in IoT. Second, we incorporate a reputation mechanism into the data sharing process. This allows IoT users to receive reputation feedback from their partners based on their behaviors, which is dynamically updated as a reputation score on the blockchain by smart contracts. Therefore, honest users get more opportunities to share data, while malicious users are held accountable and revoked from the IoT system. We also harness advanced cryptographic techniques to ensure the submission of reputation feedback does not disclose the user’s private information, preventing vindictive actions from malicious users. Finally, we demonstrate the security and effectiveness of the proposed scheme by conducting the security analysis and the detailed experiments.

With the rapid progress of technology, Web 3.0 has emerged as a transformative force in the digital realm. It is characterized by decentralization, user-centric data ownership, and the implementation of cryptographic techniques. Smart contracts, as a core component of Web 3.0, play a pivotal role in driving its evolution by enabling novel functionalities and various application. However, given the substantial financial significance of smart contracts and their inherent transparency, the accessibility of their source code to all opens potential avenues for attackers to identify and exploit vulnerabilities. Therefore, the detection of security vulnerabilities in smart contracts has become significantly important. Existing smart contract vulnerability detection tools mostly rely on expert-defined rules, leading to high false positive rates. To address this problem, this paper proposes an efficient and automated framework that combines Graph and Attention for detecting smart contract vulnerabilities. This framework takes into account the code structure of smart contracts, extracts nodes, and constructs a contract graph, utilizing dataflow to represent the different semantics of variable nodes at different locations. Additionally, a bidirectional multilayer Transformer framework is constructed and trained with our dataset, utilizing the information from the nodes. The framework achieves state-of-the-art levels of $Accuracy$ 92.72%, $Recall$ 82.81%, and $F1_{score}$ 87.54%, respectively. These results show that our framework can effectively detect security vulnerabilities in smart contracts and has the potential to improve their security.

RPL(Routing Protocol for Low-Power and Lossy Networks) is a standardized routing protocol of IoT proposed by IETF(Internet Engineering Task Force) Working Group. The escalating surge of cyberattacks targeting IoT systems has exposed critical security vulnerabilities in RPL-based networks. Attackers exploit routing spoofing, resource depletion tactics, and topology manipulation strategies, while inherent constraints of resource-limited devices and scalability challenges in mass deployments amplify these risks. This critical security gap therefore necessitates the deployment of lightweight IDS(Intrusion Detection Systems) to achieve real-time anomaly detection and monitoring However, the current IDS for RPL-based IoT has certain limitations such as routing attacks dataset lack coverage of a sufficient variety of routing attacks and detection methods involve significant computational overhead. To address these problems, we have conducted the following research work. First, a novel routing attacks dataset that covers four RPL routing attack types is constructed through simulation on the Cooja IoT platform. And we develop feature extraction algorithms for dataset to extract 24 features implementing in IDS model training. Second, we propose an efficient routing detection method that accelerates model training speed by using Hybrid Pearson Correlation and GS-PSO(Grid Search-Particle Swarm Optimization) Optimized Random Forest Technique. The Pearson correlation can effectively extract key data features for different routing attacks. And the hybrid GS-PSO algorithm can optimize the hyperparameters of IDS model and enhance the accuracy of the detection mechanism while significantly reducing computational overhead. Finally, we demonstrate our IDS model has higher detection accuracy and lower computation time compared to other existing models through simulation experiments.

Sequence alignment is a critical factor in the variant analysis of genomic research. Since the FM (Ferrainas–Manzini) index was developed, it has proven to be a model in a compact format with efficient pattern matching and high-speed query searching, which has attracted much research interest in the field of sequence alignment. Such characteristics make it a convenient tool for handling large-scale sequence alignment projects executed with a small memory. In bioinformatics, the massive success of next-generation sequencing technology has led to an exponential growth in genomic data, presenting a computational challenge for sequence alignment. In addition, the use of a heterogeneous computing system, composed of various types of nodes, is prevalent in the field of HPC (high-performance computing), which presents a promising solution for sequence alignment. However, conventional methodologies in short-read alignment are limited in performance on current heterogeneous computing infrastructures. Therefore, we developed a parallel sequence alignment to investigate the applicability of this approach in NUMA-based (Non-Uniform Memory Access) heterogeneous architectures against traditional alignment algorithms. This proposed work combines the LF (longest-first) distribution policy with the EP (enhanced partitioning) strategy for effective load balancing and efficient parallelization among heterogeneous architectures. The newly proposed LF-EP-based FM aligner shows excellent efficiency and a significant improvement over NUMA-based heterogeneous computing platforms. We provide significantly improved performance over several popular FM aligners in many dimensions such as read length, sequence number, sequence distance, alignment speedup, and result quality. These resultant evaluation metrics cover the quality assessment, complexity analysis, and speedup evaluation of our approach. Utilizing the capabilities of NUMA-based heterogeneous computing architectures, our approach effectively provides a convenient solution for large-scale short-read alignment in the heterogeneous system.

In recent years NUMA architecture based on multicore system leads a mainstream solution to tackle the rapidly expanding computation in bioinformatics analysis. Sequence alignment is the most computing cost in the workflow of variant analysis in bioinformatics. Although parallel coding in sequence alignment could be few difficulties, approaching a good performance in such a system is complicated. This research explores the architecture bottlenecks we usually neglect in the implementation of bioinformatics computing, NUMA effect for instance. We exploit the scalability of threads in the sequence aligners to illustrate the problem and the significance of NUMA architecture in the multicore system. The preliminary results in the experiments display that sequence aligners cannot take advantage of NUMA-based multi-core architecture. The scalability of threads is deficient, even negative in time-cost at the case of large-scale genome data larger NUMA nodes.