IoT-Based Resource Control for In-Vehicle Infotainment Services: Design and Experimentation
1
School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
2
Silla System Corporation, Daegu 41065, Korea
3
Department of Computer Science, Bahria University, Islamabad 44000, Pakistan
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 620; https://doi.org/10.3390/s19030620
Received: 29 November 2018 / Revised: 19 December 2018 / Accepted: 29 January 2019 / Published: 1 February 2019
(This article belongs to the Special Issue Topology Control and Protocols in Sensor Network and IoT Applications)
Abstract
A variety of in-vehicle infotainment (IVI) devices and services have been developed by many vehicle vendors and software companies, which include navigation systems, cameras, speakers, headrest displays, and heating seat. However, there has not been enough research on how to effectively control and manage numerous IVI resources (devices and contents), so as to provide users with more enhanced services. This paper proposes a framework of resource control for IVI services so as to efficiently manage the IVI resources within an automobile. Differently from conventional IVI systems, in the proposed scheme, the IVI-Master is newly introduced for overall control of IVI resources, and IVI users are divided into owner and users. In addition, the IVI resources are classified as personal resources and shared resources, which are managed by the IVI-Master using the Lightweight Machine-to-Machine (LWM2M) standard. The proposed IoT-based IVI resource control scheme was implemented and tested. The experimental results showed that the proposed scheme can be used to effectively manage IVI resources for users. Additionally, the proposed resource control scheme shows lower bandwidth usage than the existing scheme. View Full-Text
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MDPI and ACS Style
Choi, D.-K.; Jung, J.-H.; Kim, J.-I.; Gohar, M.; Koh, S.-J. IoT-Based Resource Control for In-Vehicle Infotainment Services: Design and Experimentation. Sensors 2019, 19, 620.
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