Research On Path Planning And Functional Semantic Clustering In Hybrid Wireless Sensor Networks

Wireless Sensor Network(WSN) is one of the key techniques of Internet of Things(Io T) and also an important technique in real‐time Geographic Information System(GIS). According to related researches, there will be billions of sensors and smart devices connected to the internet. Then, the Io T‐services will be provided based on wireless sensor function. By using mobile sensors, the Hybrid WSN solved the problem of the sensors near the sink consume more energy and optimized efficiency in energy utilization and improved communication performance. In Hybrid WSN, in order to prolong network working life, it is important to schedule mobile sensor traversal route to make sure each mobile sensor has similar energy consumption. At the same time, in order to build Io T service layer and improve interoperability between top application layer and the bottom sensor layer, it is also necessary to study how to manage static sensor in function aspect. Then, it is necessary to research the topic of how to schedule mobile sensors and how to cluster static sensor functions.In this dissertation, in order to prolong hybrid WSN working life, an energy balanced heuristics algorithm for mobile sinks scheduling in Hybrid WSN has been proposed to balance the energy consumption between mobile sensors. The method has three main steps: Region Division to Grid Cells, Grid Division to Clusters and Clusters Adjustment. The simulation experiment result shows that the proposed method is effective to balance energy consumption between mobile sensors. And the comparative experiment result shows that the method in this dissertation is better than m TSP scheduling algorithm. The proposed energy balanced heuristics algorithm for mobile sinks scheduling in Hybrid WSN in this dissertation makes the mobile sensors have similar energy consumption. Then, the network working life has been prolonged.A method based on tuple has been proposed in this dissertation to challenge static sensor function sematic clustering problem. At the first place, the functions are represented as a tuple, and then the sematic distance between two functions are calculated according to the sematic distance between corresponding parameters, and then the functions are divided into different clusters by Spectral Clustering. At last, the function index has been built based on clustering result. Also, the method to query function and update index are introduced. The proposed method in static sensor function sematic clustering problem has a better function representation accuracy and calculation efficiency. The function index and query method improved sensor function in publishing, discovery, selection and combination, and also support the application development based on Io T service.