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|Title:||Dynamic generation of internet of things organizational structures through evolutionary computing||Authors:||Shen, Zhiqi
Lesser, Victor R.
Internet Of Things
DRNTU::Engineering::Computer science and engineering
|Issue Date:||2018||Source:||Shen, Z., Yu, H., Yu, L., Miao, C., Chen, Y., & Lesser, V. R. (2018). Dynamic generation of internet of things organizational structures through evolutionary computing. IEEE Internet of Things Journal, 5(2), 943-954. doi:10.1109/JIOT.2018.2795548||Series/Report no.:||IEEE Internet of Things Journal||Abstract:||In today's world, intelligent embedded devices and sensors are interconnected into a dynamic and global network infrastructure is referred to as the Internet of Things (IoT). It has been widely recognized that the performance of an IoT is highly affected by how it is organized. A large-scale system may have billions of possible ways of being organized, which makes it impractical to find a high quality choice of organization by manual means. In this paper, we propose a genetic algorithm (GA) aided framework for generating hierarchical IoT organizational structures. We propose a novel unique mapping between organizational structures and genome representations. Since hierarchical (i.e., tree-structured) organizations are one of the most common forms of organizations, we propose a novel method to map the phenotypic hierarchical structure space into a genome-like array representation space. This new representation opens up opportunities for evolutionary computing techniques to help IoT applications automatically generate organizational structures according to desired objective functions. Based on this mapping, we introduce the hierarchical GA which enriches standard genetic programming approaches with the hierarchical crossover operator with a repair strategy and the mutation of small perturbation operator. The proposed approach is evaluated in an IoT-based information retrieval system. The results have shown that competitive baseline structures which lead to IoT organizations with good performance in terms of utility can be found by the proposed approach during the evolutionary search.||URI:||https://hdl.handle.net/10356/90165
|DOI:||10.1109/JIOT.2018.2795548||Rights:||© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JIOT.2018.2795548||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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