物联网海量多维信息的按需采集决策问题研究

Research on On-demand Acquisition Decision Problem for Mass Multi-dimensional Information in Internet of Things

对物联网海量多维信息按需采集的决策问题进行研究,将该问题转换为网络子网构造问题并建立整数非线性多目标优化模型。针对特定事件,根据物联网布局和节点传感器配置,确定采集的节点、每个节点上传数据的传感器类型以及上传数据的时间间隔,利用遗传算法对其进行多目标最优化求解,以均衡采集后所需传输的数据流量和节点信息量。通过仿真实例验证了该模型及其求解方法的正确性与有效性。

This paper studies on-demand acquisition decision problem for mass multi-dimensional information in Intemet of Things（IOTs）. It converts this problem to the network subnet construct issues and establishes an integer nonlinear multi-objective optimization model. For specific event, according to the deployment and configuration of sensors, determine collection of nodes, each node in the data upload sensor types as well as the data upload time interval. It uses genetic algorithms to solve multi-objective optimization, and balances data flow and the amount of information transmission requirements after collection. The results of simulation case prove the correctness and validity of the model and its solution.