Roadside Unit Power Saving using Vehicle Detection System in Vehicular Ad-hoc Network

Author(s):  
Ronald Adrian ◽  
Selo Sulistyo ◽  
I Wayan Mustika ◽  
Sahirul Alam

The number of deaths resulting from road accidents and mishaps has increased at an alarming rate over the years. Road transportation is the most popularly used means of transportation in developing countries like Nigeria and most of these road accidents are associated with reckless driving habits. Context-aware systems provide intelligent recommendations allowing digital devices to make correct and timely recommendations when required. Furthermore, in a Vehicular Ad-hoc Network (VANET), communication links between vehicles and roadside units are improved thus enabling vehicle and road safety. Hence, a non-intrusive driver behaviour detection system that incorporates context-aware monitoring features in VANET is proposed in this study. By making use of a one-dimensional highway (1D) road with one-way traffic movement and incorporating GSM technology, irregular actions (high speed, alcohol while driving, and pressure) exhibited by drivers are monitored and alerts are sent to other nearby vehicles and roadside units to avoid accidents. The proposed system adopted a real-time VANET prototype with three entities involved in the context-aware driver’s behaviour monitoring system namely, the driver, vehicle, and environment. The analytical tests with actual data set indicate that, when detected, the model measures the pace of the vehicle, the level of alcohol in the breath, and the driver's heart rate in-breath per minute (BPM). Therefore, it can be used as an appropriate model for the Context-aware driver’s monitoring system in VANET.


2018 ◽  
Vol 7 (3) ◽  
pp. 41 ◽  
Author(s):  
Ayoob Ayoob ◽  
Gang Su ◽  
Gaith Al

In this research, new modeling strategy based hierarchical growing neural gas network (HGNG)-semicooperative for feature classifier of intrusion detection system (IDS) in a vehicular ad hoc network (VANET). The novel IDS mainly presents a new design feature for an extraction mechanism and a HGNG-based classifier. Firstly, the traffic flow features and vehicle location features were extracted in the VANET model. In order to effectively extract location features, a semicooperative feature extraction is used for collecting the current location information for the neighboring vehicles through a cooperative manner and the location features of the historical location information. Secondly, the HGNG-based classifier was designed for evaluating the IDS by using a hierarchy learning process without the limitation of the fix lattice topology. Finally, an additional two-step confirmation mechanism is used to accurately determine the abnormal vehicle messages. In the experiment, the proposed IDS system was evaluated, observed, and compared with the existing IDS. The proposed system performed a remarkable detection accuracy, stability, processing efficiency, and message load.


2013 ◽  
Vol 380-384 ◽  
pp. 2427-2430
Author(s):  
Tan Cheng ◽  
Hao Sun ◽  
Ning Cao ◽  
Cheng Li

A rapid growth in the number of vehicles contributes to more and more attention on vehicular ad hoc network (VANET) recently, and ensuring security plays a significant role in maintaining its stable operation. Because of some variable environmental factors, its impossible to evaluate a precise value of packets transmission success rate. To reduce the influence of errors on detection, we develop a novel two-person zero-sum intrusion detection game model for formulating confrontation behavior between intrusion detection system (IDS) and malicious node.


Sign in / Sign up

Export Citation Format

Share Document