Paper Info
Title
Learning-Based Resource Allocation Strategy for Industrial IoT in UAV-Enabled MEC Systems
Abstract
Forest fire monitoring plays an important role in forest resource protection. Although satellite remote sensing is an effective way for forest fire monitoring, satellite-based methods can only monitor large-scale forest areas, and they are weak in predicting the specific areas of forest fires. In this article, we first propose an unmanned aerial vehicle (UAV)-enabled system architecture consisting of multiple industrial Internet of Things (IIoTs), in which the data collected by sensors in IIoTs can be delivered to UAVs for processing directly. As the sensors of IIoTs are deployed to monitor different indexes of forest fires, fully considering the priority constraints among sensors can guarantee a quick response of forest fire monitoring. Thus, the priority constraints among the sensors are taken into consideration in this system architecture, and the objective is to minimize the maximum response time of forest fire monitoring. To search for the optimal UAV resource allocation strategy, a learning-based cooperative particle swarm optimization (LCPSO) algorithm with a Markov random field (MRF)-based decomposition strategy is proposed. The solution space of UAV resource allocation is decomposed into subsolution spaces according to the decomposed decision variables by the MRF network structure, and the optimal resource allocation strategy is searched by LCPSO in multiple subsolution spaces cooperatively. Three simulation experiments on two datasets are designed, and the simulation results compared with the state-of-the-art methods verify the validity of LCPSO, which are reflected by the quickest response time of forest fire monitoring.
Year
DOI
Venue
2021
10.1109/TII.2020.3024170
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Forestry,Monitoring,Resource management,Temperature sensors,Task analysis,Systems architecture
Journal
17
Issue
ISSN
Citations 
7
1551-3203
4
PageRank 
References 
Authors
0.37
0
3
Name
Order
Citations
PageRank
Lu Sun192.14
Liangtian Wan2449.89
Xianpeng Wang312624.25