Feasibility Analysis of Data Transmission in Partially Damaged IoT Networks of Vehicles

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS(2023)

引用 4|浏览14
暂无评分
摘要
Nowadays, vehicle-oriented Internet of Things (IoT) is a new generation of IoT networks in which sensors are deployed on electronic hardware modules of vehicles. A secure and feasible IoT-assisted vehicle environment should include a robust data transmission mechanism for transferring and collecting data packets from both onboard and roadside sensors, resulting in the accurate delivery of packages without delay. When designing such Internet of Vehicles (IoV) networks, the vulnerability of the network should be considered to facilitate data transmission in the remaining network under the condition that some nodes (e.g., vehicles) and channels are damaged due to the dynamic environmental factors and unpredicted failures at various nodes. Fractional Critical Deleted Graph (FCDG), which is used in graph theory, can act as Fractional Factor (FF) in the IoV networks to maintain the IoT network stable and provide reliable network connectivity when a part of data transmission network is damaged. Toughness is an important condition to measure the sturdiness of such FF-encoded network. In this work, we study the relationship between toughness and FCDG in IoV networks. Moreover, the graph conditions are considered together with the tight lower bound of the toughness for the existence of path factor. Such feasibility analysis of IoV networks help to find the bound in the effort to recover or realign lost links in networks, which is critical for the next generation of intelligent transportation systems where all vehicles are connected seamlessly.
更多
查看译文
关键词
Data communication, Graph theory, Trajectory, Wireless sensor networks, Roads, Reliability theory, Public transportation, Internet of Vehicles, graph theory, intelligent transportation system, toughness, fractional factor (FF), fractional critical deleted graph (FCDG)
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要