An Autonomic Optimization Model of Multi-Layered Dependability for Intelligent Internet of things

Abstract

Accompanying with the speeding up of Internet of things (IOT) construction, the dependability problems become the important factors constraining its all-round development. Based on the multi-level and multidimensional properties of IOT dependability elements, with the overall improving of the dependability index of IOT as the ultimate goal, the dependability elements of the local fine-tuning in each layer, this paper researches the change rule of internal dependability elements in perception layer, network layer and business layer, and adopts perception layer as the example, using the method of linear programming to seek the best proportion of all kinds of dependability elements and the optimal values of the elements, trying to construct a feasible autonomic optimization model for dependability elements of IOT system. Firstly, according to the function features and dependability properties of each layer, and change rules between the dependability index and dependability elements in each layer are analyzed. Secondly, based on the dynamic changes (up or down) of dependability elements in internal environment (that is, three layers in IOT), the ratio relations of dependability elements in each layer are dynamically controlled and adjusted to implement the local optimization, improving the overall autonomic configuration and autonomic adjusting ability of IOT system. At last, example analysis results show that the optimization model proposed in this paper can realize the substantial optimization in each layer of IOT.