Ul Hassan Mahmood, Al-Awady Amin A, Ali Abid, Akram Muhammad, Iqbal Muhammad Munwar, Khan Jahangir, Abdelrahman Ali Yahya Ali
Department of Computer Skills, Deanship of Preparatory Year, Najran University, Najran 66241, Saudi Arabia.
Department of Computer Science, University of Engineering and Technology, Taxila 48080, Pakistan.
Sensors (Basel). 2024 Jan 26;24(3):0. doi: 10.3390/s24030818.
A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.
车载自组织网络（VANET）是一种复杂的无线通信基础设施，它融合了集中式和分散式控制机制，协调车辆之间的无缝数据交换。这个复杂的通信系统依赖于5G连接的先进能力，采用专门的拓扑结构来增强数据包传输。这些车辆相互通信并与路边单元（RSU）建立连接。在车辆通信的动态环境中，干扰，尤其是在涉及高速车辆的场景中，带来了挑战。一个值得关注的问题是黑洞攻击的出现，即一辆车辆恶意行事，阻碍数据包转发给后续车辆，从而损害了VANET内内容的安全传播。我们提出一种基于智能集群的路由协议，以缓解VANET路由中的这些挑战。该系统通过两个关键阶段运行：首先，利用人工神经网络（ANN）模型检测恶意节点；其次，通过增强的聚类算法为每个集群指定簇头（CH）来建立集群。随后，预测数据传输的最佳路径，旨在最小化数据包传输延迟。我们的方法集成了一种改进的按需距离向量（AODV）协议用于按需路由发现和最佳路径选择，增强了请求和应答（RREQ和RREP）协议。路由性能评估涉及BHT数据集，利用ANN分类器计算准确率、精确率、召回率、F1分数和损失。NS-2.33模拟器有助于在路径预测阶段评估端到端延迟、网络吞吐量和跳数。值得注意的是，我们的方法通过ANN分类模型在检测黑洞攻击方面达到了98.97% 的准确率，在各种网络路由参数方面优于现有技术。
