On-Demanding Information Acquisition in Multi-UAV-Assisted Sensor Network: A Satisfaction-Driven Perspective

When multiple heterogeneous unmanned aerial vehicles (UAVs) provide service for multiple users in sensor networks, users’ diverse priorities and corresponding priority-related satisfaction are rarely concerned in traditional task assignment algorithms. A priority-driven user satisfaction model is pr...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021-10, Vol.2021, p.1-14
Main Authors: Yang, Hua, Yang, Jungang, Zhao, Wendong, Liu, Cuntao
Format: Article
Language:English
Subjects:
Assignment problem
Combinatorial analysis
Data collection
Data processing
Decision making
Energy consumption
Engineering
Game theory
Genetic algorithms
Linear programming
Operations research
Optimization
Polynomials
Problem solving
Traveling salesman problem
Unmanned aerial vehicles
User satisfaction
Windows (intervals)
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Summary:When multiple heterogeneous unmanned aerial vehicles (UAVs) provide service for multiple users in sensor networks, users’ diverse priorities and corresponding priority-related satisfaction are rarely concerned in traditional task assignment algorithms. A priority-driven user satisfaction model is proposed, in which a piecewise function considering soft time window and users’ different priority levels is designed to describe the relationship between user priority and user satisfaction. On this basis, the multi-UAV task assignment problem is formulated as a combinatorial optimization problem with multiple constraints, where the objective is maximizing the priority-weighted satisfaction of users while minimizing the total energy consumption of UAVs. A multipopulation-based cooperation genetic algorithm (MPCGA) by adapting the idea of “exploration-exploitation” into traditional genetic algorithms (GAs) is proposed, which can solve the task assignment problem in polynomial time. Simulation results show that compared with the algorithm without considering users’ priority-based satisfaction, users’ weighted satisfaction can be improved by about 47% based on our algorithm in situations where users’ information acquisition is tight time-window constraints. In comparison, UAVs’ energy consumption only increased by about 6%. Besides, compared with traditional GA, our proposed algorithm can also improve users’ weighted satisfaction by about 5% with almost the same energy consumption of UAVs.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/2717733