Internet of Things and Cyber-Physical Systems最新文献

Advancement of 5G new radios has enabled more robust communication for the Machine-to-Machine (M2M) communication node, using filter bank multicarrier (FBMC). This paper focuses on robust transmission over random fluctuations of the channel and also enhances the battery life for the massive machine type communication (mMTC) node. Filter bank multicarrier and Adaptive Modulation Coding (AMC) have been utilized together to enhance the performance of the 5G (NR) PHY layer. A frame-to-frame implementation is used to diminish the impact of fading using AMC, while efficient utilization of spectrum is achieved using FBMC. The selection of the AMC profile is obtained through the analysis of uplink packets using the Distance Statistics (DS). The FBMC is incorporated with 5G PHY in place of OFDM to achieve the optimum utilization of spectrum and also obtain a significant reduction in peak to average power ratio (PAPR) for robust transmission, which saves 10% of the battery requirement. On the basis of channel state information, distance statistics were employed to optimize the AMC. The optimum selection of AMC with FBMC will reduce the bit error rate (BER) against multipath fading and ensure the better utilization of available spectrum to attain the optimum utilization of the power amplifier.

With the improvement of people's awareness of environmental protection, electric vehicles (EVs) are becoming more and more popular, and the issue of vehicle to grid (V2G) energy trading is also put on the agenda. To protect the security and privacy of EVs when they trade energy with the grid, many scholars have introduced the emerging blockchain technology. However, there are few studies on the blockchain consensus algorithm for the EVs charging scenario, while the consensus is exactly the core technology in blockchain for reaching agreement in distributed systems, which to some extent determines the efficiency of V2G. Therefore, aiming at the above scenario, this paper proposes two low-complexity consensus algorithms, namely (PBFT-enabled RAFT) PRAFT and (RAFT-enable PBFT) RPBFT, which are combined the typical blockchain consensus PBFT and RAFT, and can be respectively applied to two EVs charging scenarios. In our V2G model, charging piles (CPs) and charging stations (CSs) will participate in the blockchain consensus as nodes. Through theoretical analysis and simulation, and compared with other methods, these two consensus algorithms have high scalability, low communication complexity, low storage overhead, high throughput, and low latency. Meanwhile they can also avoid the risk of Byzantine leader in RAFT. Finally, we demonstrate the two consensus algorithms in a real charging scenario, which show their transaction latency and energy consumption can well adapt to the EVs charging scenario in V2G networks.

While most Internet of Things (IoT) solutions involve sensing, some of them also introduce actuation mechanisms. Specifically, devices interact with assets in the environment and transmit sensor readouts to applications that perform analytics. These applications typically reside on the network core and, in turn, process the readouts that trigger the transmission of actuation commands to the device. One important issue in these schemes is the nature of the communication channels. Most devices are wireless and therefore they are affected by the effects of signal multipath fading that results in application loss. More importantly, these impairments may cause actuation commands to be lost or to be critically delayed. In this context, several standard mechanisms have been proposed for the transmission of traffic from the application to the devices. They fall under two main architectural categories: (1) Representational State Transfer (REST) and (2) Event Driven Architecture (EDA). In this paper, we analyze two protocols associated with each of these two architectures by comparing them in order to assess their efficiency in IoT actuation solutions. This analysis leads to the development of a mathematical model that drives an algorithm that enables the dynamic selection of the right technology based on network impairments.

The Internet of Things (IoT) is playing a significant role in the transformation of traditional factories into smart factories in Industry 4.0 by using network of interconnected devices, sensors, and software to monitor and optimize the production process. Predictive maintenance using the IoT in smart factories can also be used to prevent machine failures, reduce downtime, and extend the lifespan of equipment. To monitor and optimize energy usage during part manufacturing, manufacturers can obtain real-time insights into energy consumption patterns by deploying IoT sensors in smart factories. Also, IoT can provide a more comprehensive view of the factory environment to enhance workplace safety by identifying potential hazards and alerting workers to potential dangers. Suppliers can use IoT-enabled tracking devices to monitor shipments and provide real-time updates on delivery times and locations in order to analyze and optimize the supply chain in smart factories. Moreover, IoT is a powerful technology which can optimize inventory management in smart factories to reduce costs, improve efficiency, and provide real-time visibility into inventory levels and movements. To analyze and enhance the impact of internet of thing in smart factories of industry 4.0, a review is presented. Applications of internet of things in smart factories such as predictive maintenance, asset tracking, inventory management, quality control, production process monitoring, energy efficiency and supply chain optimization are reviewed. Thus, by analyzing the application of IoT in smart factories of Industry 4.0, new ideas and advanced methodologies can be provided to improve quality control and optimize part production processes.

The majority of smartphones on the market run on the Android operating system. Security has been a core concern with this platform since it allows users to install apps from unknown sources. With thousands of apps being produced and launched daily, malware detection using Machine Learning (ML) has attracted significant attention compared to traditional detection techniques. Despite academic and commercial efforts, developing an efficient and reliable method for classifying malware remains challenging. As a result, several datasets for malware analysis have been generated and made available during the past ten years. These datasets may contain static features, such as API calls, intents, and permissions, or dynamic features, like logcat errors, shared memory, and system calls. Dynamic analysis is more resilient when it comes to code obfuscation. Though binary classification and multi-classification have been carried out in recent studies, the latter provides valuable insight into the nature of malware. Because each malware variant operates differently, identifying its category might help prevent it. Using the well-known ensemble ML approach called weighted voting, this study performed dynamic feature analysis for multi-classification. Random Forest, K-nearest Neighbors, Multi-Level Perceptrons, Decision Trees, Support Vector Machines, and Logistic Regression are all studied in this ensemble model. We used a recent dataset named CCCS-CIC-AndMal-2020, which contains an extensive collection of Android applications and malware samples. A well-researched data preparation phase followed by weighted voting based on R² scores of the ML classifiers presents an accuracy of 95.0% even after excluding 60.2% features, outperforming all recent studies.