Abstract

Internet of Things (IoT) devices face increasing security threats while operating under severe energy constraints. This research addresses the critical challenge of implementing effective cybersecurity measures within sustainable energy frameworks for resource-constrained IoT environments. The research presents Sustainable Micro-Neural Energy-Efficient Security Intelligence (SMEESI), a novel TinyML-enabled intrusion detection system (IDS) that significantly reduces energy consumption while maintaining robust security capabilities. The research innovation combines lightweight neural network architectures with energy-aware anomaly detection algorithms specifically optimized for microcontroller deployment. The SMEESI framework includes an adaptive power management module that dynamically adjusts computational intensity based on threat levels, achieving energy efficiency without compromising security posture. Performance evaluation demonstrates a 78% reduction in power consumption compared to traditional IDS implementations while maintaining 94.3% detection accuracy across multiple attack vectors. Memory footprint requirements decreased by 65%, enabling deployment on severely resource-limited IoT sensors and actuators. The system has been successfully tested in smart buildings, industrial monitoring, and healthcare IoT applications, proving its versatility across critical infrastructure domains. This research contributes to green cybersecurity by enabling sustainable security monitoring in IoT ecosystems, extending device battery life, reducing electronic waste through prolonged hardware lifecycles, and minimizing the carbon footprint of security operations while maintaining essential protection against evolving cyber threats in the digital age.