Dynamic Generation of Internet of Things Organizational Structures Through Evolutionary Computing

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@Article{Shen:2018:ieeeIOT,

• author = "Zhiqi Shen and Han Yu and Ling Yu and Chunyan Miao and Yiqiang Chen and Victor R. Lesser",
• journal = "IEEE Internet of Things Journal",
• title = "Dynamic Generation of Internet of Things Organizational Structures Through Evolutionary Computing",
• year = "2018",
• volume = "5",
• number = "2",
• pages = "943--954",
• 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 recognised 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. Compared with the traditional genetic operators, the newly introduced operators produced organizations of higher utility more consistently under a variety of test cases. The proposed approach is computationally efficient in large search spaces and provides a novel method for future generations of IoT systems to autonomously improve performance.",
• keywords = "genetic algorithms, genetic programming, Bioinformatics, Genomics, Information retrieval, Internet of Things, Organizations, Standards organizations, Evolutionary computing, Internet of Things (IoT), organization",
• DOI = "doi:10.1109/JIOT.2018.2795548",
• month = apr,
• notes = "Also known as \cite{8264715}",

}

Genetic Programming entries for Zhiqi Shen Han Yu Ling Yu Chunyan Miao Yiqiang Chen Victor R Lesser

Citations