Journal of High Speed Networks

In this new round of power network development, the concept of coordinated and optimized operation of power network interconnection and smart grid “source network load” has gradually attracted attention. Based on the analysis of the impact of the flexible DC distribution network on the complex energy system with multiple data sources and large data volume under the big data platform, the coordinated dispatching of the source and load of the big data flexible DC distribution network is studied. Therefore, a source-load matching index that can evaluate the impact of different types of loads on the stability of the flexible DC grid is constructed and incorporated into the load recovery optimization model. First, use the scene reduction method to process all generated scenes; next, take the reduction technology and the scene generation method into account of the uncertainty caused by the prediction error and incorporate them into the multi-objective function optimization model; finally, the membership function of fuzzy numbers is used to model uncertainty. So as to construct a load recovery model that can coordinate the load recovery amount, importance and system dynamic response. The actual meaning of the matching index is explained through model solving and actual case analysis.

Drivers are held responsible for the vast majority of traffic accidents. While most of the errors that cause these accidents are involuntary, a significant number of them are caused by irresponsible driving behaviors, which must be utterly preventable. Irresponsible driving, in addition, is often associated with the stress drivers experience while driving. We have previously implemented an intelligent system based on fuzzy logic for determining driver’s stress in Vehicular Ad hoc Networks (VANETs), called Fuzzy-based System for Determining the Stress Feeling Level (FSDSFL), considering the driver’s impatience, the behavior of other drivers, and the traffic condition as input parameters. In this work, we present an Improved FSDSFL (IFSDSFL) system, which considers the driving experience and history as an additional input. We show through simulations the effect that driving experience and history and the other parameters have on the determination of the stress feeling level and demonstrate some actions that can be performed when the stress exceeds certain levels.

Intrusion Detection is very important in computer networks because the widespread of internet makes the computers more prone to several cyber-attacks. With this inspiration, a new paradigm called Intrusion Detection System (IDS) has emerged and attained a huge research interest. However, the major challenge in IDS is the presence of redundant and duplicate information that causes a serious computational problem in network traffic classifications. To solve this problem, in this paper, we propose a novel IDS model based on statistical processing techniques and machine learning algorithms. The machine learning algorithms incudes Fuzzy C-means and Support Vector Machine while the statistical processing techniques includes correlation and Joint Entropy. The main purpose of FCM is to cluster the train data and SVM is to classify the traffic connections. Next, the main purpose of correlation is to discover and remove the duplicate connections from every cluster while the Joint entropy is applied for the discovery and removal of duplicate features from every connection. For experimental validation, totally three standard datasets namely KDD Cup 99, NSL-KDD and Kyoto2006+ are considered and the performance is measured through Detection Rate, Precision, F-Score, and accuracy. A five-fold cross validation is done on every dataset by changing the traffic and the obtained average performance is compared with existing methods.

With the rapid development of social networks, studying and analyzing their structures and behaviors has become one of the most important requirements of businesses. Social network analysis can be used for many different purposes such as product ads, market orientation detection, influential members detection, predicting user behaviors, recommender systems improvements, etc. One of the newest research topics in social network analysis is the enhancement of the information propagation performance in different aspects based on application. In this paper, a new method is proposed to improve few metrics such as distribution time and precision on social networks. In this method, the local attributes of nodes and also the structural information of the network is used to forward data across the network and reduce the propagation time. First of all, the centrality and Assortativity are calculated for all nodes separately to select two sets of nodes with the highest values for both criteria. Then, the initial active nodes of the network are selected by calculating the intersection of the two sets. Next, the distribution paths are detected based on the initial active nodes to calculate the propagation time. The performance analysis results show that the proposed method has better outcomes in comparison to other state-of-the-art methods in terms of distribution time, precision, recall, and AUPR criteria.

In recent years, with the rapid development of IoT technology, hundreds of millions of IoT devices have been manufactured and applied, and the subsequent IoT attacks have become more and more severe. The complex and diverse architecture of IoT devices, coupled with the lack of security development specifications by IoT device manufacturers, and the widespread misuse and abuse of code, lead to the proliferation of IoT vulnerabilities. The conventional IoT vulnerability detection scheme is expensive to operate, and the implementation technology is complex, which is difficult to be fully promoted. This paper proposes a lightweight IoT firmware vulnerability detection scheme based on homology detection. The processing is converted into a feature vector, which effectively reduces the platform dependence. Combined with the database technology, the storage and retrieval efficiency is increased, and the same-origin vulnerability detection is realized by calculating the cosine similarity of the vector. The experimental results show that this scheme can effectively identify the vulnerabilities in firmware.

Today, Internet of Vehicles (IoV) applications communicate with smart devices and deploy Intelligent Transportation Systems (ITS) to recognize traffic congestion problems. One of the main challenges of smart cities and IoV platforms is data routing methods for smart traffic congestion problems to navigate the individual information of data transmission as a critical issue in the ITS. Providing a way to extend the Quality of Service (QoS) variables and energy efficiency methods for directing data transmission in routing-based traffic management systems within the IoV environment is an important issue since the energy consumption of IoV devices is a critical issue in low-power saving storage. This paper presents a hybrid Genetic Algorithm and Social Spider Optimization (GA-SSO) algorithm for an energy-aware routing schema for optimizing traffic congestion and smart devices in the IoV environment. After conducting some studies and comparisons, the exactness and prevalence of the proposed schema were received. Experimental results confirmed that the GA-SSO algorithm calculation decides optimal and ideal arrangement for energy-aware routing schema demonstrate by essentially making strides the execution of QoS variables can oversee information blockage comes from data transmission between IoV nodes.