Vehicular ad-hoc network (VANET) is capable of offering a diverse set of services, and thus gains lot of attention from both academic and industrial communities. In VANET, the communicating links are prone to break easily; therefore, more attention is required to find reliable routes at a faster rate. In this context, information about connectivity and delay are valuable asset to establish and maintain a robust communication. In the proposed work, an Adaptive Intersection Selection Mechanism using Ant Colony Optimization (AISM), the problem of discovering a promising route subject to multiple Quality-of-service (QoS) constraint is emphasized. In previously reported literature, the best searched route cannot guarantee successful data packet transmission, even when it fulfills the QoS constrains at the time of discovery. To overcome this gap, AISM follows two simple strategies: Firstly, it exploits prediction-based mechanism for real time road evaluation; secondly, the route is formed between two consecutive intersections, instead of a long route between the network nodes. The connectivity and delay information obtained from small control packets called forward and backward ants are used to prioritize the candidate routes. Furthermore, by means of extensive simulation, outcomes in urban settings show that AISM outperforms the existing protocols in terms of packet delivery ratio, average delay and hop count.

中文翻译：

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利用蚁群优化的自适应路口选择机制在城市VANET中高效传播数据

车载自组织网络（VANET）能够提供多种服务，因此引起了学术界和工业界的广泛关注。在VANET中，通讯链接容易断开；因此，需要更多的注意力以更快的速度找到可靠的路线。在这种情况下，有关连接性和延迟的信息对于建立和维护可靠的通信非常有用。在提出的工作中，采用蚁群优化的自适应交叉口选择机制（AISM），强调了发现受多个服务质量（QoS）约束的有希望的路由的问题。在先前报道的文献中，即使在发现时满足QoS约束的情况下，搜索最佳的路由也无法保证成功进行数据包传输。为了克服这一差距，AISM遵循两个简单的策略：首先，它利用基于预测的机制进行实时道路评估；其次，路由是在两个连续的交叉点之间形成的，而不是网络节点之间的长路由。从称为前向蚂蚁和后向蚂蚁的小型控制数据包中获得的连通性和延迟信息用于确定候选路由的优先级。此外，通过广泛的模拟，