A Systematic Review of Latency Reduction Techniques in Smart Home IoT Networks

Abstract

The Internet of Things (IoT) is improving automation, security, and energy management by converting traditional living spaces into smart settings. However, latency issues pose significant challenges, affecting the seamless operation of smart home IoT networks. This systematic review explores latency reduction techniques in smart home IoT systems, focusing on their impact on network performance, security, and energy efficiency. The study evaluates research from conference proceedings and peer-reviewed publications between 2010 and 2024. It considers three primary categories of solutions: Fog and edge computing frameworks, which enable localized data processing to reduce transmission delays, Machine learning-based optimization methods, which dynamically adapt to network conditions in real-time and Blockchain-enabled hybrid systems, which enhance security while mitigating latency. The review discusses the trade-offs associated with these methods, including computational overhead, resource consumption, and security benefits. Additionally, it examines the types of IoT devices commonly deployed in smart homes, such as smart sensors, connected appliances, and security systems, providing insights into their unique latency challenges. Findings indicate that no single approach provides a universally optimal solution, emphasizing the need for context-aware, hybrid strategies. The review concludes that integrating fog computing for decentralized data processing with machine learning for adaptive task scheduling offers a more effective approach to mitigating latency. Future research should focus on refining hybrid frameworks to enhance scalability, adaptability, and overall efficiency in evolving smart home IoT ecosystems.