Leveraging Edge Computing for Enhanced Threat Detection in Smart Home Environments

With the rapid expansion of Internet of Things (IoT) devices in smart homes, ensuring robust cybersecurity has become a primary concern. Traditional cloud-based threat detection methods struggle with latency, scalability, and privacy issues. This research explores how edge computing, when integrated with artificial intelligence (AI), can significantly enhance real-time threat detection in smart home ecosystems. The study provides an extensive review of existing literature, introduces an experimental setup that compares cloud-based and edge-based models, and analyzes implementation case studies. Key performance metrics such as latency, accuracy, privacy leakage, and bandwidth efficiency are discussed in detail.