Security-aware Resource Allocation for Fog Computing Using MDP

Fog computing extends cloud capabilities to the network edge, enabling low-latency support for smart-city and IoT applications. However, the heterogeneity and limited security capacity of fog and edge nodes make security-aware resource allocation a critical challenge. This paper proposes a Markov Decision Process (MDP)-based framework for allocating application modules across cloud–fog–edge infrastructures while considering security constraints. The allocation problem is modeled as a static decision process in which each module is assigned to a device using a composite reward function capturing security compliance, probability-weighted breach risk, and resource utilization constraints. The MDP is solved offline using value iteration to derive a reward-optimal static allocation policy. The approach is evaluated on a smart-city fog scenario comprising 25 application modules and 11 heterogeneous devices and is compared against a genetic algorithm (GA) baseline using an identical reward structure. Experimental results show that the MDP achieves 68