Abstract
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.
Highlights
Nowadays, with the rapid increase of vehicles, the phenomenon of traffic congestion and pollution is getting worse, which leads to frequent traffic violations and accidents
The main contributions are as follows: (1) Firstly, this paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the optimization problem of task allocation, designs the task allocation model of intelligent transportation wireless sensor networks (ITWSNs), and designs a new fitness function to evaluate the performance of the algorithm
Simulation experiments are carried out in the actual scene of road traffic information collection; the results show that the genetic simulated annealing can effectively optimize the alliance structure of task allocation
Summary
With the rapid increase of vehicles, the phenomenon of traffic congestion and pollution is getting worse, which leads to frequent traffic violations and accidents. These have become bottlenecks for the further development of cities [1]. An intelligent transportation system mainly includes the collection, transmission, control, and guidance of traffic information. A wireless sensor network can provide an effective means for information collection and transmission of the intelligent transportation system. The effective operation of the intelligent transportation system depends on obtaining comprehensive, accurate, and real-time dynamic traffic information. People process the information collected by sensor nodes to obtain comprehensive traffic condition, which facilitates identification, decision-making, positioning, detection, and tracking of vehicles in the traffic management.
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