Year: 2025
Type: article
Abstract: As the environment deteriorates, natural disasters occur more frequently and become more devastating to human beings and the environment. After a disaster, to quickly and optimally restore the damaged things, including physical systems (e.g., transport networks) and the environment, needs decision makers to own sufficient data/information. Unmanned vehicle detection systems (UVDS) are undoubtedly feasible tools in collecting such data in a harsh environment. The most important challenges in UVDS management are on modeling the UVDS data layer and multi-stage recovery strategies, which have received little research. To address such problems, this paper proposes a multi-stage control strategy for UVDS based on Internet of Things (IoT). The optimal decision is decided by utilizing four indicators: performance recovery efficiency, normal detection probability, operation cost, and economic benefit cost, respectively. The simulation results show that the proposed strategy improves the performance recovery efficiency by 12.1% and the normal detection probability by 3.9%, the operation cost declines by 58.4%, and the economic benefit cost by 75.9% compared with the general control strategy.
Authors: Hongyan Dui, Huanqi Zhang, Xinghui Dong, Shaomin Wu, Yu Wang
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Awards: G1503883414, G2595120689
OpenAlex ID: W4407247091