Enhancing task scheduling in fog computing using fine-tuned barnacles mating optimization

Smart cities, industrial equipment, supply chains, and logistics increasingly depend on emerging technologies such as the Internet of Things (IoT) to enhance operational efficiency. IoT enables real-time monitoring of conditions by capturing environmental data through distributed sensor networks, which are then processed and transmitted to fog computing (FC) nodes where critical decisions are made. The efficiency of FC relies heavily on reducing latency and effectively scheduling fog node tasks. Scheduling mechanisms are therefore essential to ensure system responsiveness and improve the quality of service (QoS) for IoT applications and end-users. This paper proposes an Enhanced Barnacles Mating Optimization (EBMO) algorithm to address the FC task scheduling problem by optimizing resource utilization and task allocation. EBMO aims to distribute workloads efficiently across fog nodes, thereby lowering execution latency and increasing overall throughput. Unlike conventional methods, the proposed approach leverages adaptive task swapping to further boost scheduling performance. A performance comparison was conducted between EBMO and other existing scheduling algorithms across several critical parameters, including response time, energy consumption, and system reliability. Experimental results demonstrate that EBMO consistently outperforms traditional optimization techniques, delivering superior outcomes across all performance measures. By intelligently managing computational resource utilization, the model reduces latency, enhances throughput, and ensures more efficient task execution within IoT-based FC architectures. Consequently, the system delivers faster response times and improved QoS for end-users. These findings highlight the potential of bio-inspired optimization algorithms like EBMO to significantly improve task scheduling strategies, offering scalable and efficient solutions for diverse industrial sectors relying on IoT and FC. The offered method outlines an extremely promising field for future development with adaptive smart scheduling processes and opens up gates for increased efficiency in IoT-environmental systems.