Multi-Stage Control Strategy of IoT-Enabled Unmanned Vehicle Detection Systems
Hongyan Dui, Huanqi Zhang, Xinghui Dong, Shaomin Wu, Yu Wang
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.