International Journal of Computer Networks and Applications

90 - 107

Authors: S. Sumithra, R. Vadivel

Vehicular Ad-Hoc Network is an emerging technology, mainly developed for road safety applications, entertainment applications, and effective traffic conditions. VANET applications work based on the accurate mobility information shared among the vehicles. Sometimes attackers manipulate the mobility information shared by the adjacent vehicle or neighboring vehicle, which results in terrible consequences. To deal with the illusion-based type of attacks, researchers have proposed enormous solutions. Unfortunately, those solutions could not deal with the dynamic vehicle conditions and variable cyber malfunctions, which reduces the misbehavior detection accurateness and increases the false-positive rate. In this paper, the dynamic vehicle context is taken into account to propose a two solutions such as Miscellaneous VANET Classifiers based Misbehavior Detection Model (MVC-MDM) and Ensemble Miscellaneous VANET Classifiers based Misbehavior Detection Model (EMVC-MDM). This model is constructed based on the Mobility Data Gathering phase, Mobility Context Feature Extraction phase, Mobility Context Feature Level Fixing phase, Hampel Filter based Context Reference Building phase, Constructing Miscellaneous VANET Classifiers based Misbehavior Detection model and Ensemble Miscellaneous VANET Classifiers based Misbehavior Detection phase. Vehicle context is prepared using the data-centric features and the behavior-based features of the vehicles. The Nonparametric Hampel filter and Kalman filter are used to building the context reference model. These filters discover the temporal and spatial correlation of the uniformity in the current mobility information. Vehicle features are extracted locally according to the stability, likelihood, and performance of the vehicles' mobility information. A random forest based learning algorithm is used to train and test the classifiers. The proposed MVC-MDM and EMVC-MDM has been simulated in various context scenarios and the presence of misbehaving vehicles. NGSIM dataset has been used for extensive simulation. The results prove that the effectiveness and the reliability of the proposed MVC-MDM and EMVC-MDM are higher than the existing misbehavior detection systems.

119 - 129

Authors: Fouziah Hamza, S. Maria Celestin Vigila

The Mobile Ad-hoc Network (MANET) is a decentralized system that consists of mobile nodes. Wireless connections are used to connect these nodes. The primary issues of concern of MANETs are mobility and limited battery lifetime. Advanced techniques for improving MANET energy efficiency and extending network lifespan are critical. Clustering is one of the tried-and-true methods for increasing network lifetime by lowering and balancing energy consumption. Choosing a suitable cluster head from the cluster improves the network’s energy efficiency even further. Because of the additional workloads, the cluster heads (CHs) utilize more energy than non-cluster heads. A novel algorithm for CH selection with a Hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA) is proposed to improve the MANET network’s energy efficiency and lifetime. The proposed method is implemented using the NS-2 platform for the analysis. The proposed model outperforms the existing OSCA, EP-MBO, GBTC, SM-WCA, CM-BCA, and FCO methods in terms of network performance. The model’s performance has achieved a low Bit Error Rate (BER) of 7% for 100 nodes with 99.38% Packet Delivery Ratio (PDR) with minimized delay in the range 2.01sec with the energy efficiency of 99.03%. The validation indicates that the Hybrid PSO-GA approach is more efficient than the other methods.