Resilient Edge Computing Framework for Autonomous, Secure, and Energy-Aware Systems

Edge computing has become a key enabling paradigm for next-generation intelligent systems by allowing data processing to occur closer to data sources, thereby reducing latency and network dependency. As edge infrastructures increasingly support autonomous and distributed applications, they face growing challenges related to system resilience, cybersecurity, and energy efficiency. Conventional cloud-centric architectures often fail to satisfy the strict real-time responsiveness, reliability, and sustainability requirements of applications such as autonomous vehicles, smart infrastructure, healthcare monitoring, and industrial automation. To address these limitations, this paper proposes a resilient edge computing framework designed to support autonomous operation, secure data handling, and energy-aware resource management in dynamic and uncertain environments. The proposed framework integrates fault tolerance mechanisms, adaptive security controls, and intelligent energy optimization strategies within a unified layered architecture. Local intelligence at the edge enables continuous system monitoring, proactive anomaly detection, and autonomous recovery from failures and cyber threats. Energy-awareness is achieved through adaptive workload scheduling and resource allocation that balance performance demands with power constraints. The framework is evaluated using scenario-based simulations reflecting realistic edge computing conditions. Experimental results demonstrate notable improvements in system availability, reduced response latency, enhanced security robustness, and lower energy consumption compared to traditional edge architectures. These findings confirm that a holistic integration of resilience, security, and energy management is essential for dependable edge-enabled systems. The proposed framework provides a scalable and sustainable foundation for future autonomous and mission-critical edge computing applications.