An MQTT Brokers Distribution Based on Mist Computing for Real-Time IoT Communications

Abstract

As we consider the number of IoT time-sensitive applications , the transfer of data to a remote data center and server such as Cloud, Fog, and Edge becomes inefficient since the deadline constraint is not satisfied. Thus, ensuring that the IoT time-sensitive applications meet their timing constraints is a challenge. Mist Computing is closer to IoT devices, presenting the lowest communication delay but less computational resource than the Cloud, Fog, and Edge. Seeing several IoT devices use MQTT protocol to access the data due to its lightness and flexibility, we propose an architecture for IoT time-sensitive applications based on MQTT protocol and integrating Mist Computing. We focus on distributing the MQTT brokers over Mist nodes to satisfy the deadline constraints with the consideration of the limited resource of Mist nodes. Hence, we propose an approach for the selection of the appropriate MQTT Mist broker. We have also proposed MQTT communication model that provides the M/M/1 based analysis for delay computing and energy conception. The experiment results show that our proposal is very effective for time-sensitive applications and also maximize the lifetime of IoT systems since it minimizes the cumulative energy of the system. Compared to MQTT Edge broker distribution, our solution provides the lesser delay of communication between IoT devices.

Improvement of MQTT semantic to minimize data flow in IoT platforms based on distributed brokers

“…As Table 1 shows there is no solution that enables data fusion, filtering, and aggregation in distributed MQTT. On the one hand, there are several approaches [20][21][22][23] that proposed distributed architecture for MQTT without considering data fusion, filtering, and aggregation. On the other hand, there are numerous works 29,32,33 regarding data fusion, filtering, and aggregation From the previous works, two contributions caught our attention the most.…”

“…Besides MQTT broker distribution on edge nodes, EMMA 21 offers continuous network QOS monitoring, proximity detection, MQTT broker orchestration, and network reconfiguration for optimal QoS during runtime by transparently migrating MQTT clients to nearby brokers. Along the same lines, our approach in Reference 22 focuses on optimizing delay and load of communications by distributing brokers across mist level.…”

“…The transmission of data over the cloud can potentially increase the delay and the load of communications, raising significant challenges for real‐time applications. To address the limitations of the centralized broker architecture, several researchers focused on using distributed MQTT broker architectures, such as References 20–23. Distributed MQTT broker architectures consist of distributing several MQTT brokers much closer to the devices by deploying them in fog, edge, and mist nodes.…”

PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

“…These properties include ease of maintenance, resilience, and sustainability, all of which make it possible to schedule resources and maintain performance control. To improve service delivery for various geospatial data, cloud, fog, mist, and edge computing systems, they have been adopted by many authors [10,11]. However, adding an extra layer to the conventional mist computing system is inefficient for many geospatial IoT devices.…”

“…This ensures that the mist, the fog devices, and the cloud can coordinate effectively and adequately. As a result, we use queuing theory for the performance study to examine this kind of resource allocation approach [10,11,14].…”