scholarly journals Context-Aware Data Dissemination for ICN-Based Vehicular Ad Hoc Networks

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 263 ◽  
Author(s):  
Yuhong Li ◽  
Xinyue Shi ◽  
Anders Lindgren ◽  
Zhuo Hu ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Data Dissemination ◽  
Delivery Ratio ◽  
Relative Distribution ◽  
Context Aware ◽  
Packet Forwarding ◽  
Information Centric Networking ◽  
Hoc Networks

Information-centric networking (ICN) technology matches many major requirements of vehicular ad hoc networks (VANETs) in terms of its connectionless networking paradigm accordant with the dynamic environments of VANETs and is increasingly being applied to VANETs. However, wireless transmissions of packets in VANETs using ICN mechanisms can lead to broadcast storms and channel contention, severely affecting the performance of data dissemination. At the same time, frequent changes of topology due to driving at high speeds and environmental obstacles can also lead to link interruptions when too few vehicles are involved in data forwarding. Hence, balancing the number of forwarding vehicular nodes and the number of copies of packets that are forwarded is essential for improving the performance of data dissemination in information-centric networking for vehicular ad-hoc networks. In this paper, we propose a context-aware packet-forwarding mechanism for ICN-based VANETs. The relative geographical position of vehicles, the density and relative distribution of vehicles, and the priority of content are considered during the packet forwarding. Simulation results show that the proposed mechanism can improve the performance of data dissemination in ICN-based VANET in terms of a successful data delivery ratio, packet loss rate, bandwidth usage, data response time, and traversed hops.

Artificial Intelligence Based Intrusion Detection System to Detect Flooding Attack in VANETs

2018 ◽  
pp. 87-100 ◽  
Author(s):  
Mannat Jot Singh Aneja ◽  
Tarunpreet Bhatia ◽  
Gaurav Sharma ◽  
Gulshan Shrivastava
Keyword(s):  
Intrusion Detection ◽  
Ad Hoc Networks ◽  
Intrusion Detection System ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Detection System ◽  
Delivery Ratio ◽  
Flooding Attack ◽  
Hoc Networks

This chapter describes how Vehicular Ad hoc Networks (VANETs) are classes of ad hoc networks that provides communication among various vehicles and roadside units. VANETs being decentralized are susceptible to many security attacks. A flooding attack is one of the major security threats to the VANET environment. This chapter proposes a hybrid Intrusion Detection System which improves accuracy and other performance metrics using Artificial Neural Networks as a classification engine and a genetic algorithm as an optimization engine for feature subset selection. These performance metrics have been calculated in two scenarios, namely misuse and anomaly. Various performance metrics are calculated and compared with other researchers' work. The results obtained indicate a high accuracy and precision and negligible false alarm rate. These performance metrics are used to evaluate the intrusion system and compare with other existing algorithms. The classifier works well for multiple malicious nodes. Apart from machine learning techniques, the effect of the network parameters like throughput and packet delivery ratio is observed.

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