An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road...

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Vydané v:Journal of sensors Ročník 2021; číslo 1
Hlavní autori: Zha, Zhihua, Li, Chaoqun, Xiao, Jing, Zhang, Yao, Qin, Hu, Liu, Yang, Zhou, Jie, Wu, Jie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Hindawi 2021
John Wiley & Sons, Inc
Predmet:
Adaptive algorithms
Cloning
Collaboration
Control algorithms
Convergence
Coordination compounds
Design
Energy consumption
Energy efficiency
Experiments
Genetic algorithms
Intelligent transportation systems
Linear programming
Mutation
Optimization
Performance evaluation
Remote sensing
Remote sensors
Revenue
Roads & highways
Scheduling
Sensors
Simulated annealing
Simulation
Traffic congestion
Transportation planning
Wireless networks
Wireless sensor networks
ISSN:1687-725X, 1687-7268
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Popis
Shrnutí:Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each node to automatically establish the entire network. In the large-scale self-organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP-hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.
Bibliografia:ObjectType-Article-1
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ISSN:1687-725X
1687-7268
DOI:10.1155/2021/5582646