Secure Wireless Power Transfer in Internet-of-Things Networks
Abstract
Title: Secure Wireless Power Transfer in Internet-of-Things NetworksProject Abstract(Approved for Public Release)The exponential growth of Internet of Things (IoT) devices brings significant advancements in connectivity and efficiency across domains such as smarthomes, cities, industrial automation, and healthcare. However, these pervasive wireless networks introduce substantial security challenges, especially in the transmission and protection of sensitive data. Traditional cybersecurity approaches, predominantly software-focused, often neglect threats emerging from the RF and electromagnetic domains, a critical oversight for IoT devices operating at network edges in remote environments. This project addresses these challenges through a multifaceted approach to enhance the security of wireless networks.The project leverages on-chip electromagnetic sensors for comprehensive monitoring and analysis of circuit behaviors within IoT devices, facilitating immediate detection of threats and vulnerabilities. It explores self-supervised deep learning models to equip IoT devices with autonomous sensing capabilities, providing effective protection against both known and emerging threats through adaptive learning techniques. The intelligent sensing module will be validated on a beamforming-based wireless power transfer platform for non-invasive threat detection and response, enhancing device resilience and ensuring continuous operation and security through efficient power management. Through the integration of intelligent sensor technologies, this framework empowers IoT devices to safeguard sensitive data integrity and confidentiality in real-time across diverse deployment scenarios. This proactive and comprehensive approach to threat detection and mitigation aims to significantly enhance the resilience of IoT networks against malicious attacks, ensuring the reliability and trustworthiness of IoT deployments in various environments.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 08, 2024
- Source ID
- N000142412507
Entities
People
- Vanessa Chen
Organizations
- Carnegie Mellon University
- Office of Naval Research
- United States Navy