Secure Embedded IoT Frameworks for Intelligent Automation of Renewable Energy-Based Electrical Microgrids

Abstract

ADSTRACT The rapid transition toward decentralized clean energy infrastructure has positioned renewable energy-based electrical microgrids at the forefront of modern smart grid initiatives. Integrating distributed energy resources (DERs)—such as solar photovoltaic systems, wind turbines, fuel cells, and battery energy storage systems (BESS)—demands real-time monitoring, autonomous control, and responsive energy orchestration. Embedded Internet of Things (IoT) technologies play a pivotal role in delivering this intelligent automation by deploying distributed microcontrollers, edge gateways, and wireless sensor/actuator networks across grid assets. However, the deep convergence of operational technology (OT) and information technology (IT) significantly expands the cyber-physical attack surface. Edge nodes, often deployed in physically unmonitored environments with severe computation and memory constraints, are highly vulnerable to unauthorized physical access, firmware tampering, eavesdropping, dynamic denial-of-service (DoS) assaults, and false data injection (FDI) attacks. Such security breaches can compromise state estimation algorithms, destabilize system frequency and voltage, trigger cascading outages, and cause permanent hardware degradation. This comprehensive review paper critically evaluates state-of the-art secure embedded IoT frameworks tailored for intelligent microgrid automation. We dissect hardware-root-of-trust mechanisms, lightweight cryptographic protocols, edge-level AI/ML anomaly detection architectures, and secure communication protocols (e.g., MQTT/TLS 1.3, CoAP/DTLS). Furthermore, we analyze cyber-physical resilience strategies, compare existing hardware architectures (ARM Cortex-M, ESP32, RISC-V, FPGA), and present an empirical simulation evaluating microgrid dynamic stability under targeted cyber-attacks. Finally, key technical research gaps, hardware limitations, and future research trajectories are systematically articulated to guide next-generation secure, autonomous microgrid deployments.

KEYWORDS: Embedded IoT, Microgrid Automation, Distributed Energy Resources (DERs), Cyber-Physical Security, Hardware Root of Trust, Lightweight Cryptography, False Data Injection (FDI) Attack, Edge AI Anomaly Detection.