Consensus in fractional-order multi-agent systems (MASs) with time delays has attracted considerable research interest in recent years. Numerous existing findings rely heavily on the stability theory of fractional-order delay systems. In these findings, however, certain stability rates remain inestimable, while others lack rigorous estimation. This paper develops enhanced Mittag-Leffler stability criterion for fractional-order systems incorporating infinite distributed and discrete delays, which provides precise characterizations of convergence rates and establish a pioneering framework for fractional-order stability analysis. Moreover, this work proposes a novel dual-mode adaptive event-triggered strategy (DMAETS) to optimize limited communication resources and enhance network utilization efficiency. The DMAETS framework features time-varying event-triggering thresholds and adaptive control gains that dynamically adjust based on real-time system states. Through innovative DMAETS-based controller designs (internal/boundary configurations) and the proposed stability criterion, the fractional-order coupled PDE-ODE MASs (FCPOMASs) achieve consensus, with corresponding consensus conditions derived as linear matrix inequalities (LMIs). Finally, two simulation examples are presented to illustrate the validity of the theoretical analysis and the feasibility of the control schemes.

The exponential growth of Internet of Things (IoT) devices has led to the proposal of edge computing for data processing. The decentralized nature of edge computing servers and IoT devices makes blockchain ideal for connecting IoT users and servers. The consensus protocol, a core technology in blockchain, ensures node agreement and operational efficiency. However, as node numbers increase and spread geographically, traditional consensus protocols face deployment challenges, significantly reducing efficiency. To address this, we propose a hierarchical blockchain consensus protocol based on geographic location and a trust model called location and trust delegated-BFT (LT-DBFT). This protocol reduces global communication delays caused by wide geographic distribution by assigning consensus nodes to different clusters based on their locations. A trust model is also designed to elect active nodes for consensus voting, thereby reducing the consensus overhead within clusters. Subsequently, the primary nodes of each cluster form a global shared layer to achieve the final ordering and execution of transactions. Through theoretical and experimental analysis, our scheme demonstrates lower latency and higher throughput performance than traditional practical Byzantine fault tolerance and GeoBFT, making it more suitable for efficient deployment in large-scale and geographically widespread IoT environments.

The continuous evolution of network threats has made Cyber Threat Intelligence (CTI) sharing an essential strategy to counter these challenges. However, existing CTI data-sharing models face challenges such as establishing and maintaining trust, lacking flexible sharing strategies, and lacking mechanisms for dispute resolution. To address these issues, this paper proposes a reliable and transparent CTI data-sharing framework based on blockchain (RTB-RM). RTB-RM utilizes a Multidimensional Reputation Model (MRM) to assess user trustworthiness, thus enhancing the credibility of CTI data. It adopts a selective data-sharing approach based on trust levels and sensitivity, balancing the demands of data-sharing and security considerations. Additionally, RTB-RM employs real-time arbitration for dispute resolution. Our experiments validate the cost-effectiveness of RTB-RM and the effectiveness and rationality of MRM in evaluating user reputations and parameter configurations. RTB-RM demonstrates the immense potential and advantages of blockchain technology and multidimensional reputation models in enhancing the reliability and transparency of CTI data sharing.

With the development of Vehicle Edge Computing (VEC), vehicles can offload tasks to Road Side Units (RSU) for processing. However, optimizing latency is a challenging problem in high-speed environments. This paper investigates the latency optimization problem for task offloading in high-speed environments, where multiple vehicles on the user end can choose to offload their tasks to either RSUs or Base Station (BS) for computation. Meanwhile, based on the vehicle’s location and speed, we predict the time a vehicle spends within the communication range of an RSU to select a more suitable edge node. Then, an improved Group Priority Sampling-Double Dueling Deep Q Network (GPS-D3QN) algorithm is used to optimize the proposed task offloading, resulting in an optimal offloading strategy that reduces latency. Through simulation, by varying the number of vehicles and RSUs, the proposed optimization scheme is verified to have an advantage in terms of delay compared to other schemes.

Unknown vulnerabilities, also known as zero-day vulnerabilities, are vulnerabilities in software, systems, or networks that have not yet been publicly disclosed or fixed. If these vulnerabilities are ever discovered by hackers, intentionally or unintentionally, they pose a major threat to network security. This is particularly true in the blockchain field, as smart contracts hold a lot of money, and if they are discovered and exploited by hackers, the financial losses to users will be even greater. However, the current research on smart contract vulnerabilities mainly focuses on known vulnerabilities, and the research on unknown vulnerabilities has been limited. Based on this, we introduce a machine learning-based method for detecting unknown vulnerabilities in smart contracts. First, the method obtains the opcode sequences executed by smart contract transactions in the EVM by instrumenting Geth and replaying the Ethereum transactions. Next, we employ an n-gram model and a vector weight penalty mechanism to extract the opcode sequence features. We then use machine learning algorithms to detect unknown vulnerabilities based on the similarity principle. Finally, we test the effectiveness of our method with four machine learning models: the K-Nearest Neighbor algorithm (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The SVM model performs best at detecting unknown vulnerabilities, with an accuracy of 96%, a precision of 91%, a recall of 100%, and an F1-score of 95%. We also discuss the benefits of the method: timely detection of attacks due to unknown vulnerabilities, thus reducing user losses.

Artificial intelligence (AI) technology is booming in information society, and its role has become one of the hot research topics in the field of image identification. Flowers play essential role in daily life, but the identification and know well of some flower species is not clear. Therefore, the system is of great significance to help people obtain the detailed information of plant flowers. The proposed method is to use machine learning algorithm to process and identify flower images, mainly using transfer learning model, based on the trained model parameters, and taking 25 reptiles as the training base. After the optimal training and parameters adjustment, the extracted features were transferred to the flower model. The experimental results show that the effective recognition accuracy of different flowers is up to 98.20%. The proposed algorithm can be adopted to the small-scale training task, and can reduces the amount of machine operation. In addition, the algorithm also can obtain satisfactory recognition accuracy within a short time. The flower information is presented in the form of dynamic web pages, which improved readability of idenfication results.

Ethereum is a blockchain platform that allows developers to create smart contracts. Smart contracts are programs that can automatically execute and handle cryptocurrency funds. However, over a hundred thousand new smart contracts are deployed every day and inevitably contain vulnerabilities due to programming errors. Once deployed, smart contracts cannot be fixed or changed, leaving funds at risk. To mitigate it, we use deep learning to detect vulnerabilities in smart contracts. First, we create our own dataset of labeled smart contracts based on opcode sequences, since few smart contract codes and labeled datasets are publicly available. We collect opcode sequences by replaying real-world transactions from the Ethereum Mainnet in our fully synchronized node while we leverage a plugin called "SODA" to label opcode sequences with vulnerability classes. Second, after data collection, we preprocess the data by removing duplicate opcode sequences, normalizing the sequences to the same length, and converting them into vectors. Finally, to detect vulnerabilities in smart contracts, we train a deep classification model using LSTM neural networks. Our model achieved an average accuracy of 82.63% and an F1-score of 79.74% across seven types of vulnerabilities, which is important for securing funds and logic in smart contracts.

When faced with massive data volumes, many current companies and institutions usually choose centralized databases or distributed databases to meet their data storage needs. But untrusted centralized third-party auditor can pose serious security problems. Malicious database service providers may tamper with or delete users data to achieve certain benefits, while returning false data integrity verification results to users. The traditional solution is to introduce a third-party auditor to ensure the reliability of the data verification results, but this third-party auditor may also be untrustworthy and partner with the database service provider to forge false data verification results. The centralization of the database system makes the verification of data integrity a difficult but necessary task. Therefore, we propose a data integrity verification scheme using smart contract (DIV-SC) to ensure the reliability of data integrity verification results in a centralized database environment. We introduce the blockchain as a decentralized third-party auditor. The immutability of the blockchain can ensure that the information stored on the blockchain will not be maliciously tampered with. Meanwhile, the smart contract deployed on the blockchain can ensure that the procedure of storing verification information and the verification procedure are correct and will not be affected by any malicious parties.

Most machine learning-based malware detection systems use various supervised learning methods to classify different instances of software as benign or malicious. This approach provides no information regarding the behavioral characteristics of malware. It also requires a large amount of training data and is prone to labeling difficulties and can reduce accuracy due to redundant training data. Therefore, we propose a malware detection method based on deep learning, which uses malware images and a set of autoencoders to detect malware. The method is to design an autoencoder to learn the functional characteristics of malware, and then to observe the reconstruction error of autoencoder to realize the classification and detection of malware and benign software. The proposed approach achieves 93% accuracy and comparatively better F1-score values while detecting malware and needs little training data when compared with traditional malware detection systems.

The global internet of vehicles market is growing rapidly, it is estimated to increase significantly its value by the next few years. Vehicular Ad hoc Networks (VANETs) has a central role in the development of Intelligent Transportation System, since vehicles can communicate with each other. This paper proposes a model integrating both 5G and Blockchain for vehicular ad-hoc network management. This choice is motivated by the need of guaranteeing secure and reliable information exchange between vehicles. 5G provides low latency communication improving both V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) connections increasing considerably their trustworthiness. On the other side, BlockChain offers a distributed ledger, enhancing security and data reliability. These technologies together with VANETs mechanism can provide multiple new opportunities and uses, such as automating braking system. In this work, not only we provide a complete overview of these technologies, but also we suggest a new research topic, based on the integration of such technologies with VANETs environment, to obtain a very robust network, and hence a safer traffic management.

While the industrial Internet of Things (IIoT) brings convenience to the industry, it also brings security problems. Due to the massive amount of data generated by the surge of IIoT devices, it is impossible to ensure whether these data contain an attack or untrustworthy data, therefore, how to ensure the security and trustworthiness of IIoT devices has become an urgent problem to solve. In this article, we design a new hinge classification algorithm based on mini-batch gradient descent with an adaptive learning rate and momentum (HCA-MBGDALRM) to minimize the effects of security attacks. The algorithm significantly improves the performance of deep network training compared with traditional neural networks, decision trees and logistic regression in terms of scale and speed. In addition, we have solved the data skew problem in the shuffle phase, and we implement a parallel framework for HCA-MBGDALRM to accelerate the processing speed of very large traffic data sets.

Wireless sensor networks (WSNs) are being deployed for structural health monitoring (SHM) applications at an increasing rate. A WSN is often organized into groups or clusters for distributed monitoring purposes. However, we discover that the dependability (in terms of the monitoring ability/quality and low false alarm rate) is greatly affected by such grouping schemes, as they do not satisfy application-specific monitoring aspects. We present an SHM application oriented network architecture (SHMnet) and analyze health event monitoring performance with it. We propose a substructure-oriented sensor organization (SOSO), considering the formation of engineering structures and finding that a large physical structure consists of a number of substructures. We enable deployed sensors to be organized into groups (unlike dynamic clusters/trees) in such a way that each group of sensors can monitor a substructure independently. We evaluate SHMnet via simulations using real data traces. The evaluation results, compared to existing work, show that SHMnet achieves at least five times the energy saving (including the energy for communication) in WSNs and dependability in terms of high ability of monitoring and low false alarm rate.

Sensing coverage is one of fundamental problems in wireless sensor networks. In this paper, we investigate the polytype target coverage problem in heterogeneous wireless sensor networks where each sensor is equipped with multiple sensing units and each type of sensing unit can sense an attribute of multiple targets. How to schedule multiple sensing units of a sensor to cover multiple targets becomes a new challenging problem. This problem is formulated as an integer linear programming problem for maximizing the network lifetime. We propose a novel energy-efficient target coverage algorithm to solve this problem based on clustering architecture. Being aware of the coverage capability and residual energy of sensor nodes, the clusterhead node in each cluster schedules the appropriate sensing units of sensor nodes that are in the active status to cover multiple targets in an optimal way. Extensive simulations have been carried out to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.

Mass data are usually collected and processed in large and ultra large-scale wireless sensor networks, and this will greatly affect the life of intelligent sensors and the performance of network. In this paper, we propose an approach to reduce the collected data from wireless sensor networks by using compressed sensing method. Compressed sensing is a new sampling method that the data sampling and compressing can be done simultaneously. Compressed sensing can significantly reduce the collected data size by lowering the sampling rates of sensors, but it is non-adaptive and its algorithm has high computational complexity as well. We put forward and achieved the parallel processing of compressed sensing algorithm for improving algorithms execution speed. Experiment results shows that the proposed scheme significantly outperforms existing solutions in terms of reconstruction accuracy.

This paper addresses the polytype target coverage problem for heterogeneous wireless sensor networks (HWSNs) with clustered configurations. This problem is formulated as an integer programming (IP) problem for maximizing the whole lifetime of HWSNs. We present an energy-efficient target coverage algorithm (ETCA) to solve this problem and balance the energy consumption of sensor nodes. Each sensor node first calculates its sensing capability with neighbors' and sends a message with its current status information to a clusterhead. Then, the clusterhead decides which sensing units should be turned on to cover the targets in the most optimized way based on the information received from all its member nodes. Simulation results show that the performance of ETCA is close to the IP-solution that is an optimal coverage scheme regarding the energy efficiency. Moreover, ETCA can prolong 16% network lifetime compared with the energy first (EF) algorithm.

Sensing coverage and network connectivity are two fundamental issues in wireless sensor networks (WSNs). Due to resource constraints of sensor nodes, it may not be possible, or necessary, to provide full coverage and/or connectivity in WSNs. Under a certain coverage and connectivity requirement, the