Edge Intelligence: Transforming Real-Time Data Processing in Smart Systems

Abstract

Edge intelligence — the deployment of artificial intelligence models directly at network edge nodes proximal to data sources — has emerged as a
critical paradigm for real-time data processing in smart systems, circumventing the latency, bandwidth, and privacy constraints imposed by
centralised cloud architectures. Despite growing industrial adoption, systematic frameworks for co-optimising model accuracy, inference latency,
energy consumption, and communication overhead across heterogeneous edge deployments remain underdeveloped. This study presents
EdgeMind, a unified edge intelligence framework integrating neural architecture search (NAS) for edge-optimised model design, knowledge
distillation (KD) for lightweight model deployment, and federated edge learning (FEL) for privacy-preserving distributed training across
heterogeneous IoT nodes. EdgeMind was evaluated across three real-world smart system deployments: smart factory anomaly detection (48 edge
nodes, Siemens S7 PLC network, Amsterdam), smart city traffic monitoring (64 edge cameras, The Hague), and smart grid fault detection (32
substation edge controllers, Dutch national grid). EdgeMind achieved inference latency reductions of 67.3%, energy consumption reductions of
54.8%, and accuracy improvements of 8.4% over cloud-offloaded baselines. Federated training preserved differential privacy (epsilon = 1.2) while
converging within 23.4% fewer communication rounds than FedAvg. These results establish EdgeMind as a scalable, privacy-compliant framework
for real-world edge intelligence deployment in smart systems.