Mining Internet of Things for intelligent objects using genetic algorithm. (February 2018)

• Record Type: Journal Article
• Authors: Mardini, Wail; Khamayseh, Yaser; Yassein, Muneer Bani; Khatatbeh, Montaha Hani
• Is Part Of: Computers & electrical engineering. Volume 66(2018)
• Page Start: 423
• Page End: 434
• Publication Date: 2018-02
• Subjects: IoT -- Social IoT (SIoT) -- Friendship selection -- Link selection -- Searching IoT -- Genetic algorithm; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.10.010
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: In SIoT concept, every object in the IoT can use its friends' or friends-of-friends' relationships to search for a specific service. Service search is a slow process because each node (object) is required to manage a large number of friends. A link selection strategy using the Genetic Algorithm (GA) to find the near optimal solution is proposed. The results show an improvement over the examined strategies in terms of several parameters including average degree and average cluster coefficient. Abstract: The Internet of Things (IoT) is overpopulated by a large number of objects and millions of services and interactions. Therefore, the ability to search for the right object to provide a specific service is important. The merger of the IoT and social networking, the Social Internet of Things (SIoT), has made this possible. The main idea in the SIoT is that every object in the IoT can use its friends' or friends-of-friends' relationships to search for a specific service. However, this is usually a slow process because each node (object) is required to manage a large number of friends. This paper addresses the issue of link selection of friends and analyzes five strategies in the literature. Then it proposes a link selection strategy using the Genetic Algorithm (GA) to find the near optimal solution. The results show an improvement over the examined strategies in terms of several parameters. Graphical abstract: