The field of Internet of Things (IoT) research is moving towards enhancing security and monitoring capabilities in various environments, including smart homes and industrial settings. Recent developments focus on leveraging techniques such as fingerprinting, deep learning, and wireless sensor networks to improve device identification, authentication, and intrusion detection. Furthermore, there is a growing interest in exploring the potential of neuromorphic sensing and communications for online anomaly detection. Noteworthy papers include:

• A comprehensive survey on smart home IoT fingerprinting, which highlights the challenges and opportunities in this area.
• A proposal for a lightweight CNN-based Wi-Fi intrusion detection system, which achieves competitive detection performance with low inference time.
• A system for real-time monitoring and control of industrial environments using wireless sensor networks, which enables remote oversight and control of key parameters.
• A study on inferring privacy-invasive information from encrypted wireless traffic, which demonstrates the potential privacy risks in smart homes.
• A low-power online anomaly detection framework based on neuromorphic wireless sensor networks, which reliably detects anomalies under stringent false discovery rate requirements.