A Multilayered Framework for Adaptive and Optimized Real-Time Data Processing in Edge–Fog IoT Environments

Real-time Internet-of-Things (IoT) analytics in smart cities demand millisecond-scale responsiveness and energy efficiency requirements that challenge cloud-only deployments, which struggle with latency and bandwidth constraints. In brief, this paper presents a unified three-layer edge-fog decision stack comprising (i) queueing-theoretic performance modeling, (ii) NSGA-II Pareto-based offline trade-off mapping, (iii) a training-free Genetic-Algorithm Offloader for online adaptation, (iv) formal tail-latency guarantees, and (v) comprehensive validation through both simulation and a Raspberry Pi/Jetson prototype. We introduce a novel three-layer decision framework that couples a queueing-theoretic edge-fog pipeline with offline Pareto optimization and online adaptation. Our key contributions are: (1) an analytical model, validated through simulation, demonstrating edge latency of 10 ± 2 ms and fog latency of 40 ± 10 ms (processing time only, excluding network RTT); (2) NSGA-II-based offline optimization yielding Pareto-optimal configurations that reduce energy and cost by 32.6% while adding only 0.7 ms of latency; (3) A novel training-free Genetic-Algorithm Offloader (GAO) for real-time adaptation, eliminating the costly training phase required by DRL; (4) Formal tail-latency guarantees via Kingman’s bounds that ensure provable SLA compliance; and (5) a comprehensive evaluation against 15 baselines, including state-of-the-art DRL and hybrid approaches. In simulation, GAO achieves 2.03 ms 99th-percentile latency (4.7× better than the strongest DRL baseline, GNN-SAC, and 8× better than vanilla DQN), 100% SLA compliance, and 25% energy reduction. On a Raspberry Pi 5 and NVIDIA Jetson Orin Nano hardware prototype, GAO achieves 18.2 ms p99 latency and outperforms Azure IoT Edge, KubeEdge, and Eclipse ioFog by over 30% on latency and 22% on energy.