# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 6 Conclusions and Future Work  
### Conclusions  
This study explored the feasibility of Edge AI-based anomaly detection for smart home environments, leveraging Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) to identify unusual patterns in sensor data. The primary objective was to develop an efficient, real-time anomaly detection framework optimized for resource-constrained edge devices, reducing reliance on cloud-based processing. The experimental results demonstrated that both models effectively detected anomalies, with LSTM Autoencoder achieving higher accuracy and fewer false positives, while \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Cloud-Based Detection** & **Edge AI Detection (IF and LSTM-AE)** \\ \hline Bandwidth Usage & High bandwidth required for continuous data uploads & Minimal bandwidth use; only necessary metadata are transmitted (if needed) \\ Scalability & Scalable but dependent on network availability and cloud infrastructure & Scalable for local devices but limited by computational capacity \\ Power Consumption & Relies on remote computation (low device power usage but high cloud energy demand) & Consumes local processing power, but optimizations (e.g., quantization, event-triggered execution) can reduce energy usage \\ \hline \hline \end{tabular}
\end{table} 
Table 13: _Cont._Isolation Forest provided faster inference with lower power consumption. The quantization of the LSTM model led to significant performance improvements, reducing inference time by 76% and power consumption by 35%, making it more suitable for real-time deployment. Notably, these optimization strategies exceeded initial expectations, demonstrating that Edge AI can achieve near-cloud-level performance while maintaining efficiency and privacy. The findings highlight the trade-off between computational efficiency and detection accuracy. While LSTM Autoencoder excels in sequential anomaly detection, making it valuable in applications requiring time-dependent anomaly tracking (e.g., energy consumption monitoring, predictive maintenance), its higher computational cost limits its use in resource-constrained environments. Conversely, Isolation Forest remains a viable option for low-power, real-time flagging, particularly in scenarios where rapid anomaly detection is prioritized over complex pattern recognition. The hybrid IF + LSTM-AE approach demonstrated potential as a balanced solution, efficiently filtering anomalies while reducing false positives and maintaining reasonable computational demands. These results underscore the practical benefits of Edge AI-based anomaly detection in IoT environments, enabling privacy-preserving, low-latency anomaly detection without needing continuous cloud connectivity. The framework offers a scalable, adaptable solution tailored to various smart home security, energy management, and device health monitoring applications. The successful integration of Edge AI into anomaly detection frameworks paves the way for broader adoption of intelligent, autonomous IoT monitoring solutions, reinforcing the potential of real-time AI-driven decision-making in smart home environments. This study set out to (1) develop a lightweight anomaly detection framework optimized for edge deployment, (2) enable low-latency, privacy-preserving operation within smart IoT environments, and (3) validate its feasibility on constrained embedded hardware. These objectives were achieved, with the proposed system reaching up to 93.6% detection accuracy and sub-50 ms inference latency on platforms such as the Raspberry Pi and NVIDIA Jetson Nano. Although initially validated in smart home settings, the system's modular design, model quantization, and computational efficiency make it highly adaptable to broader applications, including smart buildings, industrial IoT, and energy-aware infrastructure. In these contexts, on-device anomaly detection enables scalable, responsive monitoring while reducing reliance on cloud connectivity.