Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3102 2019 2019 Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamilnadu, India V. V. SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India R. Lathamanju Department of Computer Science and Business Systems, Rajalakshmi Institute of Technology, Chennai, Tamilnadu, India T. Nithya Department of Computer Science and Engineering (Cyber Security), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, India T. Rajendran Task scheduling (TS) in fog computing (FC) involves efficiently allocating computing tasks to fog nodes, considering factors such as minimizing execution time, energy consumption, and latency to meet the quality-of-service (QoS) requirements of the Internet of Things (IoT) and edge devices. Efficient TS in FC is crucial for optimizing resource usage, minimizing latency, and ensuring that IoT and edge devices receive timely and high-quality services. The growing complexity of FC environments, along with the dynamic nature of IoT applications, necessitates innovative TS models using metaheuristic algorithms to allocate tasks and meet diverse quality-of-service requirements efficiently. This research introduces the GTO-SSSA (Gorilla Troops Optimization with Skip Salp Swarm Algorithm), a novel model for intelligent TS in FC environments. This model capitalizes on the collaborative nature of the GTO algorithm while incorporating enhanced exploration and exploitation capabilities via the SSSA algorithm's skipping mechanism. The primary objective of GTO-SSSA is to tackle the intricate challenges of TS in FC effectively. This includes the efficient allocation of tasks to fog nodes, considering multiple objectives such as minimizing makespan, execution time, and throughput. The GTO-SSSA model in FC demonstrates improved efficiency, consistently surpassing compared models across various task quantities with significantly reduced makespan values. Performance improvement rates for GTO-SSSA over other models show substantial gains in TS efficiency, ranging from 0.87% to 17.83%. The model exhibits scalability as it maintains its efficiency even with an increased number of tasks, aligning with the dynamic nature of IoT applications. 2025 2025 114 128 10.54216/JISIoT.140109 https://www.americaspg.com/articleinfo/18/show/3102