Constrained Multiobjective Optimization for UAV-assisted Mobile Edge Computing in Smart Agriculture: Minimizing Delay and Energy Consumption
IEEE Transactions on Sustainable Computing(2024)
Abstract
With the development of technology, unmanned aerial vehicles (UAVs) and Internet of Things devices are widely used in smart agriculture, resulting in significant energy consumption. In this paper, the optimization problem for UAV-assisted mobile computing in smart agriculture is modeled as a constrained multi-objective optimization problem. By jointly optimizing the deployment position of UAVs, the offloading location of the tasks, the transmit power of the devices, and the resource allocation of the UAVs, two optimization objectives (total delay and energy consumption) are minimized simultaneously. In view of the complex constraints, a constrained multiobjective algorithm named JO-DPTS is proposed. The algorithm adopts dual-population and two-stage approach to improve population convergence and diversity. The simulation results substantiate that JO-DPTS exhibits superior performance compared to the other three state-of-the-art constrained multi-objective evolutionary algorithms.
MoreTranslated text
Key words
Mobile edge computing,multiobjective optimization,unmanned aerial vehicles,evolutionary algorithm
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined