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An Efficient Indexing Model for the Fog Layer of Industrial Internet of Things

机译：工业物联网雾层的有效索引模型

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摘要

Given the recent proliferation in the number of smart devices connected to the Internet, the era of Internet of Things (IoT) is challenged with massive amounts of data generation. Fog Computing is gaining popularity and is being increasingly deployed in various latency-sensitive application domains including industrial IoT. However, efficient discovery of services is one of the prevailing issues in the fog nodes of industrial IoT, which restrain their efficiencies in availing appropriate services to the clients. To address this issue, this paper proposes a novel efficient multilevel index model based on equivalence relation, named the distributed multilevel (DM)-index model, for service maintenance and retrieval in the fog layer of industrial IoT to eliminate redundancy, narrow the search space, reduce both the number of traversed services and retrieval time, ultimately to improve the service discovery efficiency. The efficiency of the proposed index model has been verified theoretically and evaluated experimentally, which demonstrates that the proposed model is effective in achieving much better service discovery and retrieval performance than the sequential and inverted index models.
机译：鉴于最近连接到Internet的智能设备数量激增，物联网（IoT）时代面临着海量数据生成的挑战。雾计算正在变得越来越流行，并且正越来越多地部署在各种对延迟敏感的应用领域中，包括工业物联网。但是，服务的有效发现是工业物联网雾节点中的主要问题之一，这限制了它们向客户提供适当服务的效率。为了解决这个问题，本文提出了一种基于等价关系的新型高效多级索引模型，称为分布式多级（DM）索引模型，用于工业物联网雾层中的服务维护和检索，以消除冗余，缩小搜索空间，减少了遍历的服务数量和检索时间，最终提高了服务发现效率。理论上验证了所提出的索引模型的效率，并通过实验进行了评估，这表明所提出的模型比序贯和倒排索引模型在实现更好的服务发现和检索性能方面更为有效。

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