# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 3 Proposed System Architecture
### Anomaly Detection Approach
#### 3.2.1 Advantages of Edge AI over Cloud-Based Detection  
Deploying anomaly detection models directly on edge devices offers several key advantages over traditional cloud-based approaches, particularly in terms of latency, privacy, and bandwidth usage:  
* **Lower latency**: On-device processing eliminates the delay associated with cloud-based computation, enabling on-device anomaly monitoring and immediate response [20]. Edge AI-based detection can reduce latency from several hundred ms (typical for cloud processing) to under 50 ms, making it suitable for time-sensitive applications.
* **Enhanced privacy**: Keeping data local reduces the risk of exposing sensitive user information by limiting transmission to external servers. This approach ensures compliance with data protection regulations and enhances user trust.
* **Reduced bandwidth usage**: Processing sensor data at the edge minimizes network congestion and reliance on cloud infrastructure, reducing overall bandwidth requirements by up to 80% compared to continuous cloud-based processing.  
To further highlight the advantages of Edge AI-based anomaly detection over cloud-based methods, Table 2 compares key factors such as latency, privacy, bandwidth usage, scalability, and power consumption.  
Additionally, Table 3 compares the effectiveness of different Edge AI approaches for anomaly detection, evaluating Isolation Forest, LSTM Autoencoder, and a hybrid approach.  
By processing data locally while optimizing for performance and accuracy, Edge AI offers a compelling alternative to traditional cloud-based anomaly detection, making it particularly well-suited for real-time smart home IoT applications.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Feature** & **Cloud-Based Detection** & **Edge AI-Based Detection** \\ \hline Latency & High (data transmission delays) & Low (on-device real-time processing) \\ Privacy & Data stored in the cloud (risk of exposure) & Processed locally (minimized risk) \\ Bandwidth Usage & Requires frequent data uploads & Minimal data transmission \\ Scalability & Scalable but network-dependent & Scalable but hardware-limited \\ Power Consumption & Low (processing offloaded to cloud) & Moderate (depends on device efficiency) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of cloud-based and Edge AI-based anomaly detection approaches, evaluating their impact on latency, privacy, bandwidth usage, scalability, and power consumption for smart home IoT applications.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Aspect** & **Isolation Forest** & **LSTM Autoencoder** & **Hybrid Approach** \\ \hline Accuracy & Medium & High & High \\ False Positive Rate & High & Low & Low \\ Energy Consumption & Low & High & Medium \\ Retraining Frequency & Rarely Needed & Weekly & Adaptive \\ Best Use Case & Real-Time Flagging & Sequential Pattern Analysis & Balanced Approach \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of cloud-based and Edge AI-based anomaly detection approaches, highlighting trade-offs in latency, privacy, bandwidth usage, scalability, and power consumption. This comparison provides insights into their optimal deployment scenarios for smart home IoT applications, balancing real-time performance with computational efficiency.