Search

Network traffic classification is a fundamental and intricate component of network management in the modern, high-tech era of 5G architectural design, planning of resources, and other areas. Investigation of traffic classification is a key responsibility of traffic engineering in SDN. SDN is a network programmability technology used in 5G networks that divides the control plane from the data plane. It also points the way for autonomous and dynamic network control. SDN needs data from the classification system's flow statistics to apply the appropriate network flow policies. To control the volume of heterogeneous network traffic data in 5G network service, the network administrator must implement a carefully supervised traffic investigation system. This study uses machine learning techniques to examine alternative ways of handling heterogeneous network traffic. The suggested approach is Ensemble Learning for Automated Network Traffic Categorization. i.e., CatBoosting for Automated network traffic classification for multiclass (Cat-ANTC) predicts traffic categorization and offers a higher prediction accuracy than individual models and a more regularized model formalization to decrease over-fitting and boost efficiency. The Cat-ANTC is evaluated using benchmark network traffic datasets that are openly accessible and contrasted with current classifiers and optimization methods. It is clear that when compared to the currently used ensemble techniques, the suggested ensemble methodology produces promising outcomes. Additionally, the proposed method is tested and shown to perform better than the classification of traffic flow using the current model. [ABSTRACT FROM AUTHOR]

The rapid advancements in computer systems and wireless sensor networks (WSNs) motivate many intelligent transportation systems (ITSs) applications in smart cities, such as vehicular ad hoc networks (VANETs). The WSN supports data exchange between mobile vehicles and servers to provide effective traffic control and smooth service for VANET users. However, the vehicle environment still faces many challenges. One of the VANET concerns is the privacy and security of data flows from heterogeneous systems. Therefore, we suggest a Provably Online/Offline Signcryption (POOSC) protocol for vehicular communication in VANET to preserve data confidentiality and ciphertext unforgeability. In this architecture, the transmitter registered in the Identity-Based Cryptosystems (IBC) environment can safely and efficiently communicate with the receiver in the Public-Key Infrastructure (PKI) environment despite the limitations and challenges of heterogeneous systems. In a Random Oracle Model (ROM), we show that our protocol is safe against Indistinguishability-Adaptive Chosen-Ciphertext Attacks (IND-CCA2) under the Computational Diffie–Hellman (CDH) assumption and Existential Unforgeability-Adaptive Chosen Message Attacks (EUF-CMA) under the Discret-Logarithm (DL) assumption. Moreover, the POOSC protocol overcomes the certificate authority burden. Besides, we designed the POOSC protocol with a free pairing that achieves low computation cost and better scalability for VANET environments. The POOSC effectively meets non-repudiation, integrity, and authentication in a logically single step. [ABSTRACT FROM AUTHOR]

Traffic congestion is becoming a serious problem with the large number of vehicle on the roads. In the traditional traffic control system, the timing of the green light is adjusted regardless of the average traffic rate at the junction. Many strategies have been introduced to solve and improve vehicle management. However, in order to handle road traffic issues, an intelligent traffic management solution is required. This article represents a self adaptive real-time traffic light control algorithm based on the traffic flow. We present a machine learning approach coupled with image processing to manage the traffic clearance at the signal junction. The proposed system utilizes single image processing via neural network and You Only Look Once (YOLOv3) framework to establish traffic clearance at the signal. We employed YOLO architectures because it is accurate in terms of mean average precision (mAP), interaction over union (IOU) values and fast in object detection tasks as well. It runs significantly faster than other detection methods with comparable performance. The average processing time of single image was estimated to be 1.3 s. Further based on the input from YOLO we estimated the 'on' time period green light for effective traffic clearance. Several real time parameters like number of vehicles (two wheelers, four wheelers), road width and junction crossing time are considered to estimate the 'on'time of green light. Moreover, we used the real traffic images to test the performance and trained the system with different dataset. Our experiments investigation reveals that the predicted vehicle counts were well matched with the actual vehicle count and proposed method apprehended an average accuracy of 81.1%. The reported strategy is self adaptive, highly accurate, fast and has the potential to be implemented in the traffic clearance at the junctions. [ABSTRACT FROM AUTHOR]

The autonomous vehicles emergence is a significant step forward in the development of safe and dependable intelligent transportation systems. In vehicular automation, digital connectivity has been one of the most critical specifications. In this paper, we focus on the autonomous train, which is one of the most rapidly growing applications of autonomous vehicles. In this context, we address the issue of safety by the proposition of Enhanced Train-centric Communication-Based Train Control (ETcCBTC), which provides efficient control of rail traffic. To ensure the operation's safety, we've implemented a new safety-checking approach based on process algebra, which aims to track and correct in real time the train behavior. The proposed ETcCBTC system's performance is tested using extensive simulations and compared to CBTC and TcCBTC. In terms of checking success, transmission load, and response time, the obtained results demonstrate that ETcCBTC outperforms the concurrent systems. [ABSTRACT FROM AUTHOR]

Erasure coding has become an integral part of the storage infrastructure in data-centers and cloud backends-since it provides significantly higher fault tolerance for substantially lower storage overhead compared to a naive approach like n-way replication. Fault tolerance refers to the ability to achieve very high availability despite (temporary) failures, but for long term data durability, the redundancy provided by erasure coding needs to be replenished as storage nodes fail or are retired. Traditional erasure codes are not easily amenable to repairs, and their repair process is usually both expensive and slow. Consequently, in recent years, numerous novel codes tailor-made for distributed storage have been proposed to optimize the repair process. Broadly, most of these codes belong to either of the two following families: network coding inspired regenerating codes that aim at minimizing the per repair traffic, and locally repairable codes (LRC) which minimize the number of nodes contacted per repair (which in turn leads to the reduction of repair traffic and latency). Existing studies of these codes however restrict themselves to the repair of individual data objects in isolation. They ignore many practical issues that a real system storing multiple objects needs to take into account. Our goal is to explore a subset of such issues, particularly pertaining to the scenario where multiple objects are stored in the system. We use a simulation based approach, which models the network bottlenecks at the edges of a distributed storage system, and the nodes' load and (un)availability. Specifically, we abstract the key features of both regenerating and LRC, and examine the effect of data placement and the corresponding de/correlation of failures, and the competition for limited network resources when multiple objects need to be repaired simultaneously by exploring the interplay of code parameters and trade-offs of bandwidth usage and speed of repairs. [ABSTRACT FROM AUTHOR]