Loop parallelization in source code for internet of things computing using hybrid heuristic algorithm

Abstract

Efficient task scheduling remains a key challenge in High-Performance Computing and Internet of Things (IoT) systems, where the sequential execution of nested loops often limits parallelism. This paper proposes a hybrid approach that dynamically parallelizes nested loops in heterogeneous IoT environments. The suggested method (PSOALS) combines Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and wave-angle scheduling to model nested loops as two-dimensional iteration spaces and minimize communication overhead. By encoding loop iterations as particles and using a dependency-aware fitness function, PSOALS enhances makespan, resource utilization, and scalability. The key contributions of this work include: a dynamic scheduling framework for efficient loop parallelization and dependency management, a wave-angle scheduling mechanism to improve task execution order by balancing load and communication delays, and the integration of mutation and diversity techniques to enhance the quality of the solution. Experimental results across various IoT configurations show that PSOALS outperforms block-based, cyclic, and GA-based scheduling methods in convergence speed, stability, and execution time. The proposed approach offers a scalable and adaptive solution to future IoT challenges, including real-time processing, energy efficiency, and large-scale deployment.