School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
PLoS One. 2024 Jan 11;19(1):e0296331. doi: 10.1371/journal.pone.0296331. eCollection 2024.
The Internet of Vehicles (IoV) is one of the developing paradigms that integrates the automotive industry with the Internet of Things (IoT). The evolution of traditional Vehicular Ad-hoc Networks (VANETs), which are a layered framework for Intelligent Transportation Systems (ITS), is employed to provide Quality of Service (QoS) to end users in hazardous situations. VANETs can set up ad-hoc networks and share information among themselves using Peer-to-Peer (P2P) communication. Dynamic properties in VANETs such as dynamic topology, node mobility, sparse vehicle distribution, and bandwidth constraints can have an impact on scalability, routing, and security. This can result in frequent link failures, instability, reliability, and QOS concerns, as well as the inherent complexity of NP-hard problems. Researchers have proposed several techniques to achieve stability; the most prominent one is clustering, which relies on mobility metrics. However, existing clustering techniques generate overwhelming clusters, resulting in greater resource consumption, communication overhead, and hop count, which may lead to increased latency. Therefore, the primary objective is to achieve stability by increasing cluster lifetime, which is accomplished by generating optimal clusters. A nature-inspired meta-heuristic algorithm titled African Vulture Optimization Based Clustering Algorithm (AVOCA) is implemented to achieve it. The proposed algorithm can achieve load optimization with efficient resource utilization by mitigating hidden node challenges and ensuring communication proficiency. By maintaining an equilibrium state between the exploration and exploitation phases, AVOCA avoids local optima. The paper explores a taxonomy of the techniques used in Cluster Head (CH) selection, coordination, and maintenance to achieve stability with lower communication costs. We evaluated the effectiveness of AVOCA using various network grid sizes, transmission ranges, and network nodes. The results show that AVOCA generates 40% less clusters when compared to the Clustering Algorithm Based on Moth-Flame Optimization for VANETs (CAMONET). AVOCA generates 45% less clusters when compared to Self-Adaptive Multi-Kernel Clustering for Urban VANETs (SAMNET), AVOCA generates 43% less clusters when compared to Intelligent Whale Optimization Algorithm (i-WOA) and AVOCA generates 38% less clusters when compared to Harris Hawks Optimization (HHO). The results show that AVOCA outperforms state-of-the-art algorithms in generating optimal clusters.
车联网（IoV）是将汽车行业与物联网（IoT）融合的发展范例之一。传统车对车自组织网络（VANETs）的演进被用于为智能交通系统（ITS）中的终端用户提供服务质量（QoS），它是一个分层框架。VANETs 可以使用点对点（P2P）通信来建立自组织网络并在它们之间共享信息。VANETs 中的动态特性，如动态拓扑、节点移动性、稀疏车辆分布和带宽约束，会影响可扩展性、路由和安全性。这可能导致频繁的链路故障、不稳定、可靠性和 QoS 问题，以及 NP 难问题的固有复杂性。研究人员提出了几种技术来实现稳定性；最突出的是基于集群的方法，它依赖于移动性指标。然而，现有的聚类技术会产生压倒性的集群，从而导致更大的资源消耗、通信开销和跳数，这可能会导致延迟增加。因此，主要目标是通过生成最佳集群来提高集群的寿命，从而实现稳定性。为此，实现了一种名为非洲秃鹫优化聚类算法（AVOCA）的基于自然启发式的元启发式算法。所提出的算法可以通过缓解隐藏节点挑战和确保通信效率来实现负载优化和高效资源利用。AVOCA 通过在探索和开发阶段之间保持平衡，避免了局部最优。本文探讨了用于簇头（CH）选择、协调和维护以实现稳定性的技术分类，以降低通信成本。我们使用各种网络网格大小、传输范围和网络节点来评估 AVOCA 的有效性。结果表明，与基于 moth-flame 优化的 VANET 聚类算法（CAMONET）相比，AVOCA 生成的集群数量减少了 40%。与城市 VANET 的自适应多核聚类算法（SAMNET）相比，AVOCA 生成的集群数量减少了 45%。与智能鲸鱼优化算法（i-WOA）相比，AVOCA 生成的集群数量减少了 43%。与哈里斯鹰优化算法（HHO）相比，AVOCA 生成的集群数量减少了 38%。结果表明，AVOCA 在生成最佳集群方面优于最新算法。
