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

Jun-Hong Park,Hyeong-Su Kim,Won-Tae Kim

doi:10.3390/s18093071

Abstract

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.

Highlights

Cloud computing shows problems when many IoT (Internet of Things) devices send and receive data like smart cities
The authors in this paper verify the DM-MQTT by measuring the data transmission delay and network usage in the data transmission process between different edge networks
The SDN controller uses the edge information received from the master broker to set the data transmission path between different edge networks

Summary

Introduction

Cloud computing shows problems when many IoT (Internet of Things) devices send and receive data like smart cities. Existing cloud computing collects and analyzes data generated from multiple devices in a single cloud. As the number of IoT devices grows, the burden of processing data in a single cloud increases [1,2,3]. The evolution of IoT devices enables the transfer of large amounts of data, such as photographs and video data, rather than small sensor data, resulting in network congestion in the process of collecting data in the cloud [4,5,6]. Since IoT devices evolve from devices that produce data to devices that produce and consume data, sending data to the cloud for analysis and returning the analyzed results to the IoT device is an unnecessary delay. To improve the problem of cloud computing, recent research suggests edge computing [7,8,9]

Methods

Results

Conclusion