Using Cloud and Fog Computing for Large Scale IoT-based Urban Sound Classification

TL;DR

This paper explores the use of cloud and fog computing architectures to develop a real-time urban sound classification system, highlighting the limitations of each approach and proposing combined solutions for scalable IoT applications.

Contribution

It evaluates three configurations of cloud, fog, and edge computing for sound classification, emphasizing the importance of hybrid models over single architectures.

Findings

Cloud and edge alone are insufficient for large-scale IoT sound classification.

Hybrid configurations optimize device power, runtime, and server latency.

Combining architectures leverages their strengths for scalable IoT solutions.

Abstract

The Internet of Things (IoT) has become the forefront of bridging different technologies together. It brings rise to online computational services that make mundane tasks convenient. However, the volume of devices connecting to the network started to increase. In turn, services that thrived on centralized storage are being strained and overloaded. As applications and software advances, processing and computational power become a concern to technology companies. With data risks and large numbers of connected devices, cloud computing has become outdated. Devices are forced to commit unnecessary expenses to stay relevant in the market due to the increase in software complexity. This need for change resulted in the introduction of edge computing. Edge computing distributes the computational strain between the server and the devices. This contribution allows the cloud to accommodate more users and devices are no longer in need to make significant changes to their design every so often. Many real-time applications have evolved to require high amounts of processing power to execute. For example, sound classification comes with massive computational needs due to its affiliation with neural networks and deep learning. This paper aims to create a feasible and deployable real-time sound classification system. There were three configurations tested in this paper. The results of our experiments show that cloud computing and edge computing alone cannot cater to a technological market that is exponentially growing in size and complexity. However, the same results show promise in finding optimal configurations in terms of a combination of end device power consumption, application runtime and server latency to systems instead of focusing on a single model. Overall, it is better to take into consideration the strengths and weaknesses of each computing architecture.