COMPARATIVE ANALYSIS OF FOG SYSTEM ARCHITECTURE OPTIMIZATION METHODS: FROM METAHEURISTICS TO HYBRID SOLUTIONS

Abstract

The rapid proliferation of Internet of Things devices has intensified demands on distributed computing infrastructures, making fog computing — a paradigm that positions computational resources at the network edge — a critical enabler of low-latency real-time applications. Optimally placing services across fog nodes is a proven NP-hard combinatorial problem. Despite a decade of active research, existing studies examine individual algorithm families in isolation, leaving system designers without an evidence-based framework for selecting an optimisation method suited to their deployment context. The aim of this paper is to conduct a systematic comparative analysis of more than ten optimization approaches within a unified analytical framework and to construct a formalised five-dimensional classification model M: X → Y (X = S×W×L×P×T) that maps IoT system characteristics onto the most suitable method class. The study synthesises results from twenty-six primary sources across three method classes — metaheuristic algorithms (GA, PSO, ACO, Firefly), machine learning methods (supervised learning, LSTM, CNN-BiLSTM, GNN, federated learning), and reinforcement learning (single-agent DQN and multi-agent MARL) — evaluated against five metrics: processing latency, energy efficiency, scalability, adaptability to dynamic workloads, and implementation complexity. The analysis yields three principal results. First, PSO and ACO exhibit linear O(n) per-iteration complexity relative to population size, whereas GA and Firefly exhibit quadratic O(n²), making complexity the decisive selection criterion for fog networks exceeding 50 nodes: at that threshold, T_GA ≈ 100·T_PSO. Second, federated learning achieves over 93% classification accuracy in IoT transportation systems without centralising raw data, and is the only method class that satisfies legal data-sovereignty constraints. Third, the fully integrated MARL+GNN+FL approach is theoretically projected to achieve a latency of 65–98 ms and a 22–30% improvement in energy efficiency relative to greedy baselines, based on the principle of architectural orthogonality and on results independently validated for the MARL+GNN and FL sub-architectures; however, none of the 26 reviewed sources validates this combined architecture within a single unified testbed, and empirical co-validation constitutes the primary direction for future work. The formalised model M: X → Y includes explicit numerical thresholds for all five input dimensions, a priority-ordered decision function (Algorithm 1), and six verified output classes; it is falsifiable, reproducible, and extensible, and its application is demonstrated on a smart-city fog deployment scenario with N = 75 nodes.