Key Technology Research Of Monitoring Network For Airport Bridge Load Equipment

Airport ground bridge load equipments which belong to the newly introduced system for various airport departments, the security and stability of bridge load equipment have a great concern by people. It has also appeared unstable operation causing the aircraft ground equipment related to airborne electronic equipment damage and safety issues, this has brought huge economic losses for airlines and airports. In order to better promote the use of the bridge load equipment to the airport and achieve the purpose of energy conservation, we need to carry out a real-time monitoring for the work state of bridge load equipment, which also provide a lot of raw data reference for the use of bridge load equipment performance evaluation and failure prediction and exclusion in the future. Also because the bridge load equipments distribute relatively broad in the apron scope, they are not suitable for the bridge load equipments data collection by way of cable transmission, which lead to bridge load equipments condition monitoring network of things architecture.Exposing small network data throughput, high energy consumption of wireless nodes and too short network life cycle issues during the bridge load equipments condition monitoring of network applications, which relate to routing and mac layer protocols of bridge load equipments condition monitoring network. In order to further improve the performance of the network, this topic will be covered to optimize network routing and mac protocols.In this paper, first, for the improvement of mac layer protocol, it is mainly to optimize for the Binary Exponential Backoff(BEB) algorithm, the new improved algorithm- Fibonacci backoff algorithm reduces the conflict and competing caused by the node access to channel in the same backoff slot. It can achieve the real-time data transmission of bridge load equipment and reduce energy consumption of network. Second,The network routing layer protocols optimize mainly for the LEACH protocol, and improved algorithm focuses on wo aspects of cluster head election mechanism and cluster optimization mechanisms. Cluster head election threshold formula concern on the current energy of nodes and the distance from nodes to the base station. The clustering mechanism focus on the path weight. At last, the results show that the improved algorithm can further improve network performance and meet the requirements of the network by simulation analysis.