A Mobile Ad hoc Network (MANET) is a network composed of numerous autonomous mobile nodes. In recent times, the opportunistic network, a type of MANET is gaining a lot of significance among the researchers, as it is capable of communicating with the sink node through an efficient selection of relay nodes. In the opportunistic networks, the node does not seek any knowledge about the network topology as it selects the efficacious relay node for transmission of packets. However, MANET requires nodal information about network topology. In the opportunistic network, the data stockpiled in the packets are transmitted from a source node to a sink node by utilizing relay node opportunistically for every hop. However, this type of communication leads to delayed data delivery with increased hops as a consequence of the unsystematic selection of relay nodes. To overcome these constraints, this article focuses on the selection of optimal relay nodes for attaining faster data delivery, by unveiling the location and by predicting the mobility pattern of the neighbor nodes. Hence, this research paper proposes Particle Swarm Optimization algorithm for the selection of optimal relay nodes by locating the neighbor nodes within an established Inter-Communication Range employing Cartesian based localization technique and by analyzing their mobility pattern using recurrent neural network-long short-term memory prediction model. The results of the proposed methodology are compared with four other existing methods, namely, MaxProp, Spray and Wait, and Epidemic. The comparative results infer that the proposed method is efficient in terms of performance, reduced hops, reduced delay with enhanced packet delivery ratio, and improved overhead ratio.

移动自组织网络（MANET）是由众多自治移动节点组成的网络。近年来，机会网络（一种MANET）在研究人员中变得越来越重要，因为它能够通过有效选择中继节点与宿节点进行通信。在机会网络中，该节点不选择任何有关网络拓扑的知识，因为它选择了有效的中继节点来传输数据包。但是，MANET需要有关网络拓扑的节点信息。在机会网络中，通过针对每个跳机会利用中继节点，将存储在分组中的数据从源节点传输到宿节点。然而，由于中继节点的非系统性选择，这种类型的通信导致跳数增加的数据传送延迟。为了克服这些限制，本文重点介绍了通过揭示位置并预测相邻节点的移动性模式来获得更快的数据传递的最佳中继节点。因此，本研究提出了一种粒子群优化算法，用于选择最佳中继节点，方法是使用基于笛卡尔的定位技术将邻居节点定位在已建立的内部通信范围内，并使用递归神经网络-长短期记忆分析其迁移模式预测模型。将该方法的结果与其他四个现有方法进行了比较，即MaxProp，Spray and Wait和Epidemic。比较结果表明，该方法在性能，减少的跳数，