面向社交物联网系统的自动服务发现
Automated Service Discovery for Social Internet-of-Things Systems
摘要：
在本文中，我们建议设计一个自动化的服务发现过程，让移动众包任务请求者选择一个小的一组设备的大规模的物联网（IoT）网络来执行他们的任务。为此，我们将大规模物联网网络划分为几个虚拟社区，其成员共享强大的社交物联网关系。研究了两种社区发现算法，即Louvain算法和顺序统计局部方法（OSLOM）算法，并将其应用于真实世界的物联网数据集，以形成非重叠和重叠的物联网设备组。然后，执行基于自然语言处理（NLP）的方法来处理众包文本请求，并相应地找到能够有效完成任务的IoT设备的列表。这是通过匹配NLP输出来执行的，例如，应用程序的类型、位置、所需的可信度级别以及不同的检测到的社区。所提出的方法有效地帮助自动化和减少服务发现过程和招聘过程的移动众包应用程序。
英文摘要：
In this paper, we propose to design an automated service discovery process to allow mobile crowdsourcing task requesters select a small set of devices out of a large-scale Internet-of-things (IoT) network to execute their tasks. To this end, we proceed by dividing the large-scale IoT network into several virtual communities whose members share strong social IoT relations. Two community detection algorithms, namely Louvain and order statistics local method (OSLOM) algorithms, are investigated and applied to a real-world IoT dataset to form non-overlapping and overlapping IoT devices groups. Afterwards, a natural language process (NLP)-based approach is executed to handle crowdsourcing textual requests and accordingly find the list of IoT devices capable of effectively accomplishing the tasks. This is performed by matching the NLP outputs, e.g., type of application, location, required trustworthiness level, with the different detected communities. The proposed approach effectively helps in automating and reducing the service discovery procedure and recruitment process for mobile crowdsourcing applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
