A Proposed Secure Hybrid, Hierarchical Communication Architecture to Facilitate Fault Prediction in SDPN

Author(s):  
Ally Bitebo ◽  
Godfrey Chugulu ◽  
David Makota ◽  
Fatuma Simba
Sensors ◽  
2011 ◽  
Vol 11 (12) ◽  
pp. 11343-11356 ◽  
Author(s):  
Elsa Macias ◽  
Alvaro Suarez ◽  
Francesco Chiti ◽  
Andrea Sacco ◽  
Romano Fantacci

2020 ◽  
Vol 14 ◽  
Author(s):  
Intyaz Alam ◽  
Sushil Kumar ◽  
Pankaj Kumar Kashyap

Background: Recently, Internet of Things (IoT) has brought various changes in the existing research field by including new areas such as smart transportation, smart home facilities, smart healthcare, etc. In smart transportation systems, vehicles contain different components to access information related to passengers, drivers, vehicle speed, and many more. This information can be accessed by connecting vehicles with Internet of Things leading to new fields of research known as Internet of Vehicles. The setup of Internet of Vehicle (IoV) consists of many sensors to establish a connection with several other sensors belonging to different environments by exploiting different technologies. The communication of the sensors faces a lot of challenging issues. Some of the critical challenges are to maintain security in information exchanges among the vehicles, inequality in sensors, quality of internet connection, and storage capacity. Objective: To overcome the challenging issues, we have designed a new framework consisting of seven-layered architecture, including the security layered, which provides seamless integration by communicating the devices present in the IoV environment. Further, a network model consisting of four components such as Cloud, Fog, Connection, and Clients has been designed. Finally, the protocol stack which describes the protocol used in each layer of the proposed seven-layered IoV architecture has been shown. Methods: In this proposed architecture, the representation and the functionalities of each layer and types of security have been defined. Case studies of this seven-layer IoV architecture have also been performed to illustrate the operation of each layer in real-time. The details of the network model including all the elements inside each component, have also been shown. Results: We have discussed some of the existing communication architecture and listed a few challenges and issues occurring in present scenarios. Considering these issues, which is presently occurring in the existing communication architecture. We have developed the seven-layered IoV architecture and the network model with four essential components known as the cloud, fog, connection, and clients. Conclusion: This proposed architecture provides a secure IoV environment and provides life safety. Hence, safety and security will help to reduce the cybercrimes occurring in the network and provides good coordination and communication of the vehicles in the network.


Author(s):  
Fatemeh Alighardashi ◽  
Mohammad Ali Zare Chahooki

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.


Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.


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