Department of Computer Science, Faculty of Computing, University Technology Malaysia, Johor Bahru 81310, Malaysia.
School of Business, Asia Pacific University of Technology, and Innovation, Jalan Innovasi 6, Technology Park Malaysia, Kuala Lumpur 57000, Malaysia.
Sensors (Basel). 2023 Jan 28;23(3):1431. doi: 10.3390/s23031431.
Improving models for managing the networks of firefighting unmanned ground vehicles in crowded areas, as a recommendation system (RS), represented a difficult challenge. This challenge comes from the peculiarities of these types of networks. These networks are distinguished by the network coverage area size, frequent network connection failures, and quick network structure changes. The research aims to improve the communication network of self-driving firefighting unmanned ground vehicles by determining the best routing track to the desired fire area. The suggested new model intends to improve the RS regarding the optimum tracking route for firefighting unmanned ground vehicles by employing the ant colony optimization technique. This optimization method represents one of the swarm theories utilized in vehicles ad-hoc networks and social networks. According to the results, the proposed model can enhance the navigation of self-driving firefighting unmanned ground vehicles towards the fire region, allowing firefighting unmanned ground vehicles to take the shortest routes possible, while avoiding closed roads and traffic accidents. This study aids in the control and management of ad-hoc vehicle networks, vehicles of everything, and the internet of things.
改进管理拥挤区域消防无人地面车辆网络的模型，作为推荐系统 (RS)，代表了一项艰巨的挑战。这一挑战源于这类网络的特殊性。这些网络的特点是网络覆盖面积大、网络连接频繁失败和网络结构快速变化。研究旨在通过确定通往目标火灾区域的最佳路由来改善自动驾驶消防无人地面车辆的通信网络。所提出的新模式旨在通过使用蚁群优化技术来改进 RS 以获得最佳的消防无人地面车辆跟踪路线。这种优化方法是车辆自组织网络和社交网络中使用的群体理论之一。根据结果，所提出的模型可以增强自动驾驶消防无人地面车辆向火灾区域的导航，使消防无人地面车辆尽可能选择最短的路线，同时避免封闭道路和交通事故。这项研究有助于控制和管理自组织车辆网络、万物互联和物联网。
