Collaborative building of behavioural models based on internet of things. (February 2017)

• Record Type: Journal Article
• Authors: Colom, José Francisco; Mora, Higinio; Gil, David; Signes-Pont, María Teresa
• Is Part Of: Computers & electrical engineering. Volume 58(2017)
• Page Start: 385
• Page End: 396
• Publication Date: 2017-02
• Subjects: Social internet of things -- Big data -- Embedded systems -- Healthcare -- Distributed system framework; 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.2016.08.019
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A framework for distributing the computation among the objects of IoT is proposed. The proposal enables building collaborative models for achieving a common goal. This approach allows extending the computation capabilities to a closed and controlled environment. Healthcare applications can benefit from this to preserve user privacy. Graphical abstract: Abstract: This paper proposes a new framework that takes advantage of the computing capabilities provided by the Internet of Thing (IoT) paradigm in order to support collaborative applications. It looks at the requirements needed to run a wide range of computing tasks on a set of devices in the user environment with limited computing resources. This approach contributes to building the social dimension of the IoT by enabling the addition of computing resources accessible to the user without harming the other activities for which the IoT devices are intended. The framework mainly includes a model of the computing load, a scheduling mechanism and a handover procedure for transferring tasks between available devices. The experiments show the feasibility of the approach and compare different implementation alternatives.