Research and exploration of simulation routing algorithm based on computer deep learning fuzzy logic

Abstract

With the concept of "smart city", intelligent transportation system has become particularly important in the city. Bicycle sharing system, as a part of intelligent transportation system, is developing extremely fast nowadays. However, the topology of the current bike-sharing network is relatively simple and the transmitted data is single, and there is no unified scheme for sensing multi-source big data. In this paper, a network structure applicable to bicycle sharing is proposed and studied based on it. By studying the bike-sharing system, we analyze it in different application scenarios and explore its potential value. Therefore, this paper presents the future application scenarios of shared bikes under different situations. In this paper, a multi-hop routing algorithm based on genetic algorithm for split-cluster is proposed. For the characteristics of shared bike mobility, the algorithm adds a random speed factor in cluster head election and uses genetic algorithm for optimal path selection during data forwarding. Comparing the genetic algorithm-based cluster routing algorithm with the classical LEACH algorithm and CECA algorithm with under the same conditions, it is found that the algorithm proposed in this paper can slow down the node death process and thus improve the network performance.