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DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications

The Department of Computer Science and Engineering, Korea University of Technology and Education, Cheonan-si 31253, Korea
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 3071;
Received: 15 August 2018 / Revised: 10 September 2018 / Accepted: 11 September 2018 / Published: 12 September 2018
PDF [3676 KB, uploaded 13 September 2018]


Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT. View Full-Text
Keywords: MQTT; low delay; multicast; SDN; edge computing MQTT; low delay; multicast; SDN; edge computing

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Park, J.-H.; Kim, H.-S.; Kim, W.-T. DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications. Sensors 2018, 18, 3071.

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