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.
Author: Zheng Ruijuan, Zhang
Mingchuan, Wu Qingtao, Li Ying, Wei Wangyang, Bai Xiuling
Journal Code: jptkomputergg160219
