scholarly journals VNF Chain Placement for Large Scale IoT of Intelligent Transportation

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3819
Author(s):  
Xing Wu ◽  
Jing Duan ◽  
Mingyu Zhong ◽  
Peng Li ◽  
Jianjia Wang
Keyword(s):  
Network Topology ◽  
Traffic Safety ◽  
Large Scale ◽  
Intelligent Transportation ◽  
Full Potential ◽  
Service Chain ◽  
Network Function ◽  
Large Scale Network ◽  
Scale Network ◽  
Over Time

With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation.

Application of Connected and Automated Vehicles in a Large-Scale Network by Considering Vehicle-to-Vehicle and Vehicle-to-Infrastructure Technology

2020 ◽  
pp. 036119812096310
Author(s):  
Md Hasibur Rahman ◽  
Mohamed Abdel-Aty
Keyword(s):  
Traffic Safety ◽  
Control Algorithm ◽  
Large Scale ◽  
Signal Control ◽  
Market Penetration ◽  
Automated Vehicles ◽  
Large Scale Network ◽  
Vehicle To Vehicle ◽  
Scale Network ◽  
Vehicle To Infrastructure

Application of connected and automated vehicles (CAVs) is expected to have a significant impact on traffic safety and mobility. Although several studies evaluated the effectiveness of CAVs in a small roadway segment, there is a lack of studies analyzing the impact of CAVs in a large-scale network by considering both freeways and arterials. Therefore, the objective of this study is to analyze the effectiveness of CAVs at the network level by utilizing both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies. Also, the study proposed a new signal control algorithm through V2I technology to elevate the performance of CAVs at intersections. A car-following model named cooperative adaptive cruise control was utilized to approximate the driving behavior of CAVs in the Aimsun Next microsimulation environment. For the testbed, the research team selected Orlando central business district area in Florida, U.S. To this end, the impacts of CAVs were evaluated based on traffic efficiency (e.g., travel time rate [TTR], speed, and average approach delay, etc.) and safety surrogates (e.g., standard deviation of speed, real-time crash-risk models for freeways and arterials, time exposed time-to-collision). The results showed that the application of CAVs reduced TTR significantly compared with the base condition even with the low market penetration level. Also, the proposed signal control algorithm reduced the approach delay for 94% of the total intersections present in the network. Moreover, safety evaluation results showed a significant improvement of traffic safety in the freeways and arterials under CAV conditions with different market penetration rates.

scholarly journals A Topology Visualization Early Warning Distribution Algorithm for Large-Scale Network Security Incidents

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Hui He ◽  
Guotao Fan ◽  
Jianwei Ye ◽  
Weizhe Zhang
Keyword(s):  
Network Security ◽  
Network Topology ◽  
Early Warning ◽  
Early Warning System ◽  
Large Scale ◽  
Warning System ◽  
Small Scale ◽  
Large Scale Network ◽  
Scale Network ◽  
Security Incidents

It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

Network Topology Discovery Algorithm Based on OSPF Link State Advertisement

2014 ◽  
Vol 644-650 ◽  
pp. 3203-3207
Author(s):  
Zhe Jian Shen ◽  
Yun Sheng Ge
Keyword(s):  
Network Topology ◽  
Large Scale ◽  
Utilization Rate ◽  
Link State ◽  
Network Discovery ◽  
Large Scale Network ◽  
Scale Network ◽  
Environment Experiment ◽  
Routing Tables ◽  
Discovery Algorithms

In large scale network, the problem that network in an area topology accurately be discovered should be solved. The traditional network discovery algorithms mainly use ICMP and SNMP. But these two algorithms allow routers or other devices to send request packets to other devices. It may cause the low utilization rate of network. However, OSPF routing protocol, when it exchange routing tables, it will send LSUs to other routers and receive them from the remote routers. According to analyzing the LSAs, which are encapsulated in LSUs, we can obtain the network topology. We use GNS3 to simulate OSPF environment. Experiment shows that this algorithm can obtain the network topology rapidly and accurately.

scholarly journals Large-Scale Network Topology Reveals Heterogeneity in Individuals With at Risk Mental State for Psychosis: Findings From the Longitudinal Youth-at-Risk Study

2017 ◽  
Vol 28 (12) ◽  
pp. 4234-4243 ◽  
Author(s):  
Chenhao Wang ◽  
Jimmy Lee ◽  
New Fei Ho ◽  
Joseph K W Lim ◽  
Joann S Poh ◽  
...  
Keyword(s):  
At Risk ◽  
Network Topology ◽  
Mental State ◽  
Large Scale ◽  
Large Scale Network ◽  
Scale Network ◽  
Youth At Risk ◽  
At Risk Mental State
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