Abstract
In view of the multimetal barrier environment of nuclear power plant, by considering the factors such as transmission power, transmission position, and multipath interference, based on the simulation of metal pipes and equipment, this paper carries out the barrier area channel modeling in logarithmic fading mode and makes quantitative analysis on the channel transmission, path loss, channel power characteristics, and so on under the metal barrier environment. Based on the channel modeling, this paper optimizes the coverage of the network in the obstacle area by using the improved teaching and learning group intelligent algorithm. The simulation results show that the improved teaching and learning algorithm can optimize the network coverage of the obstacle area well, and under the four obstacle modules, 14 nodes can cover the whole area by more than 99%. This provides a solution to the problem of network coverage in the practical application of wireless sensor networks.
Highlights
As a new network emerging in recent years, wireless sensor networks can eliminate signal transmission cables, and some even cancel power supply cables
(3) based on the simulation results of (2), the actual test was verified in a multimetal barrier bench environment, and it is known that the simulation results of (2) can bring guidance to the actual wireless sensor optimization arrangement erefore, this paper studies the channel strength of wireless communication in obstacle areas by modeling to ensure that nodes can obtain enough received power and accuracy, and the network coverage of obstacle areas is optimized
How to improve the network coverage is one of the key points in the study of wireless communication of nuclear power plants. e research shows that it is an NP problem to improve the coverage of wireless sensor networks, and the swarm intelligence algorithm can be used to optimize the coverage of nodes and get the approximate optimal solution
Summary
As a new network emerging in recent years, wireless sensor networks can eliminate signal transmission cables, and some even cancel power supply cables. With the development of network communication technology, some communication requirements in extreme environments that cannot be covered by traditional networks are emerging day by day, such as underground network communication, field sensor network communication, and deep space network communication Data communication in this kind of environment usually has the characteristics of large signal delay, high channel bit error rate, low carrier-to-noise ratio/signal-to-noise ratio, frequent interruption of communication links, etc., and the data transmission performance is facing a huge challenge. (1) A modeling study of channel strength for wireless communication in obstructive areas was conducted to ensure that nodes can obtain sufficient received power as well as communication correctness (2) An optimal analysis of network coverage in obstructive areas was carried out by improving the swarm intelligence algorithm for teaching and learning (3) based on the simulation results of (2), the actual test was verified in a multimetal barrier bench environment, and it is known that the simulation results of (2) can bring guidance to the actual wireless sensor optimization arrangement erefore, this paper studies the channel strength of wireless communication in obstacle areas by modeling to ensure that nodes can obtain enough received power and accuracy, and the network coverage of obstacle areas is optimized
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