[Paper Review] The Power of Internet of Things (IoT): Connecting the Dots with Cloud, Edge, and Fog Computing

Shams Forruque Ahmed, Shanjana Shuravi+4 more|arXiv (Cornell University)|Sep 7, 2023

IoT and Edge/Fog Computing4 citations

TL;DR

This paper provides a comprehensive review of IoT integration with cloud, edge, and fog computing, analyzing their synergies, comparative advantages, and limitations. It demonstrates that combining these architectures enables scalable, low-latency IoT systems by leveraging cloud resources for storage and processing, edge for real-time response, and fog for reduced latency and bandwidth use, paving the way for next-generation smart applications.

ABSTRACT

The Internet of Things (IoT) is regarded as an improved communication system that has revolutionized traditional lifestyles. To function successfully, IoT requires a combination of cloud, fog, and edge computing architectures. Few studies have addressed cloud, fog, and edge computing simultaneously, comparing them and their issues, although several studies have looked into ways of integrating IoT with either one or two computing systems. Thus, this review provides a thorough understanding of IoT integration with these three computing architectures, as well as their respective applications and limitations. It also highlights the advantages, unresolved issues, future opportunities and directions of IoT integration with the computing systems to advance the IoT. IoT can use the Cloud's almost limitless resources to overcome technology restrictions, such as data processing, storage, and transmission. While edge computing can outperform cloud computing in many circumstances, IoT and edge computing become increasingly integrated as IoT devices increase. Cloud computing also poses a few issues, including managing time-sensitive IoT applications like video gaming, simulation, and streaming, which can be addressed by fog computing integrated with IoT. Due to the proximity of fog computing resources to the edge, data transfers and communication delays to the cloud can be reduced as a result of combining the two. The integration of IoT with cloud, fog, and edge computing will create new business prototypes and opportunities. Since IoT has the potential to greatly enhance connectivity infrastructure as an inevitable component of the future internet, further study is needed before it can be fully integrated.

Motivation & Objective

• To provide a holistic understanding of how IoT integrates with cloud, edge, and fog computing architectures.
• To compare the strengths, limitations, and trade-offs of cloud, edge, and fog computing in IoT environments.
• To identify unresolved issues and future research directions for advancing IoT-enabled systems.
• To highlight the potential of combined cloud-edge-fog architectures in enabling low-latency, scalable, and efficient IoT applications.

Proposed method

• Systematic literature review of existing studies on IoT integration with cloud, edge, and fog computing.
• Categorization of applications, use cases, and deployment models across the three computing layers.
• Analysis of performance metrics such as latency, bandwidth usage, and processing efficiency in hybrid architectures.
• Identification of architectural trade-offs through comparative evaluation of computing paradigms.
• Mapping of unresolved challenges including security, scalability, and interoperability across layers.
• Synthesis of future research opportunities based on current gaps in integration and standardization.

Experimental results

Research questions

• RQ1 How do cloud, edge, and fog computing complement each other in supporting IoT workloads?
• RQ2 What are the key performance differences and trade-offs between cloud, edge, and fog computing in IoT deployments?
• RQ3 What are the unresolved challenges in integrating IoT with multi-tiered computing architectures?
• RQ4 How can fog computing reduce latency and bandwidth consumption in IoT systems compared to pure cloud or edge solutions?
• RQ5 What future research directions are most promising for advancing integrated cloud-edge-fog IoT systems?

Key findings

• Cloud computing provides virtually unlimited storage and processing power, making it ideal for large-scale data analytics and long-term storage in IoT systems.
• Edge computing enables real-time processing and low-latency responses, which is critical for time-sensitive applications like video streaming and industrial automation.
• Fog computing reduces communication delays and bandwidth usage by processing data closer to the source, acting as an intermediary between edge devices and the cloud.
• The integration of fog with edge and cloud computing significantly improves scalability and responsiveness in large-scale IoT deployments.
• Despite their advantages, all three paradigms face unresolved challenges in security, interoperability, and dynamic resource management.
• Future IoT systems will likely depend on hybrid cloud-edge-fog architectures to balance performance, cost, and reliability.