PERFORMANCE ENHANCEMENT OF VEHICULAR AD HOC NETWORKS USING INTELLIGENT TECHNIQUES

Abstract

Vehicular Ad Hoc Networks (VANETs) form the communication backbone of Intelligent Transportation Systems (ITS), enabling vehicle-to-vehicle and vehicle-to infrastructure exchange for safety-critical and traffic-management services. However, the high mobility of vehicles, rapidly changing topology, fluctuating density, and intermittent connectivity make stable and reliable communication difficult, producing unstable links, frequent cluster reformation, control overhead, and degraded performance. Clustering mitigates these problems by organising vehicles into manageable groups, yet conventional distance-based, mobility-based, and trust-based techniques adapt poorly to dynamic conditions, rely on subjective or fixed cluster head selection weights, and model the network as a simple pairwise graph that cannot capture the higher-order interactions of dense urban traffic. Reliable communication is further threatened by intrusions such as spoofing, packet dropping, and denial-of service attacks that cryptographic mechanisms alone cannot detect. This thesis addresses these coupled challenges by developing an intelligent, stability-aware, and secure clustering framework built on hypergraph-based spectral clustering, objective multi-criteria cluster head selection, and graph-based deep learning intrusion detection. The research pursues four objectives: to analyse existing VANET clustering algorithms, to develop scalable spectral clustering algorithms, to implement an efficient stability-based model benchmarked against the state of the art, and to analyse the reliability of the selected cluster heads. Guided by a bibliometric study of the field and an investigation of V2V and V2I communication reliability, two spectral clustering frameworks are proposed. The first represents the vehicular network as a weighted hypergraph, fixes the optimal number of clusters through an eigen-gap heuristic, forms clusters using an Improved Tensor Trace Maximization (iTTM) procedure over a modularity-based Laplacian, and elects cluster heads through an objective weighted Criteria Importance Through Intercriteria Correlation (CRITIC) decision over four criteria relative speed, connectivity level, link lifetime, and v consensus trust achieving 84% cluster head stability with only three clusters, together with higher throughput and reduced packet delay. The second framework retains the hypergraph foundation but introduces a modified Tensor Trace Maximization (mTTM) pipeline with Gaussian Mixture Model assignment and an Adaptive Neighbouring Behaviour-Incentivization (ANB-I) mechanism that rewards steady vehicles and penalizes erratic ones, achieving 75% stability with three clusters a 4.17% improvement over the hypergraph baseline and consistently low packet delay across sparse and dense networks. Both frameworks were evaluated on a realistic urban scenario derived from Connaught Place, Delhi, using SUMO generated mobility streamed into MATLAB. To secure this stable clustering, a Knowledge Graph-based Deep Convolutional Neural Network (KG-DeepCNN) Intrusion Detection System (IDS) is deployed at the roadside unit for intrusion-aware access control. The model combines knowledge graph and graph convolutional network embeddings with a weighted ensemble of four heterogeneous CNN tunnels, and was validated on the BoT-IoT, ToN-IoT, UNSW NB15, and NSL-KDD benchmarks, attaining accuracies of approximately 99%, 98%, 98%, and 99% respectively, with a 2.01% accuracy gain on UNSW-NB15 over comparable methods and composite cluster head reliability between 98.43% and 99.38%. Taken together, the results demonstrate that objective, behaviour-aware clustering on a scalable spectral foundation, protected by knowledge-driven intrusion detection, yields VANET communication that is simultaneously stable, reliable, and secure. By sustaining stable cluster heads, minimizing packet delay, and screening malicious access requests, the proposed frameworks support the timely delivery of safety critical messages, reduce signalling overhead, and contribute to safer and more efficient intelligent transportation systems.