Abstract

The fog layer provides substantial benefits in cloud-based IoT applications because it can serve as an aggregation layer and it moves the computation resources nearer to the IoT devices; however, it is important to ensure adequate performance is achieved in such applications, as the devices usually communicate frequently and authenticate with the cloud. This can cause performance and availability issues, which can be dangerous in critical applications such as in the healthcare sector. In this paper, we analyze the efficacy of the fog layer in different architectures in a real-world environment by examining performance metrics for the cloud and fog layers using different numbers of IoT devices. We also implement the fog layer using two methods to determine whether different fog implementation frameworks can affect the performance. The results show that including a fog layer with semi-heavyweight computation capability results in higher capital costs, although the in the long run resources, time, and money are saved. This study can serve as a reference for fundamental fog computing concepts. It can also be used to walk practitioners through different implementation frameworks of fog-aided IoT and to show tradeoffs in order to inform when to use each implementation framework based on one’s objectives.

Highlights

  • We extend the previous studies by implementing real-life experiments and analyzing performance metrics from a popular cloud provider (AWS) and Internet of Things (IoT) protocol (MQTT)

  • Connect Success: This metric is used to collect the number of successful connections from our IoT nodes or fog nodes to the Amazon Web Services (AWS) message broker

  • Fog-computing-aided IoT environments are known for their higher scalability, since every group of IoT devices is connected to a fog node

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Summary

Introduction

Different kinds of sensors can be found in our homes, cars, workplaces, and other areas, which are sold independently (e.g., smoke sensors, light sensors, temperature sensors, motion sensors, proximity sensors, touch sensors, ultrasonic sensors, humidity sensors, IR sensors, pressure sensors, gyroscope sensors) or as integral parts of sophisticated devices, such as smartphones, which may contain dozens of sensors. These sensors are developed by major manufacturers and are deployed in many sectors, including healthcare, education, communication, transportation, and manufacturing. IoT platforms enable ment and automation of connected devices devices within the

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