MPTS: A Multi-Queue Priority-Based Task Scheduling Algorithm to Reduce Delay in Fog Computing for Lightweight IoT Devices

Abstract

The rapid growth of lightweight Internet of Things (IoT) applications has intensified the need for effi- cient task scheduling mechanisms in fog computing environments, where delay sensitivity and resource constraints are critical concerns. To address these challenges, this paper proposes MPTS, a Multi-Queue Priority-Based Task Scheduling algorithm designed to minimize service delay while ensuring fair resource allocation for heterogeneous and delay-sensitive IoT workloads. The proposed algorithm classifies incom- ing tasks into short and long jobs based on burst time and schedules them using multiple priority queues with a dynamic time-frame mechanism, effectively mitigating starvation and improving response time. The performance of MPTS is evaluated using a Cooja-based simulation environment implemented on Con- tiki OS, considering realistic fog–IoT network settings. The proposed approach is compared against two benchmark scheduling schemes: Greedy Knapsack-based Scheduling (GKS) and Delay and Performance Optimization in Fog Computing (DPOFC). Simulation results demonstrate that MPTS achieves approx- imately 18–24% reduction in average end-to-end service delay and 47–50% lower network usage, while maintaining comparable energy consumption across varying numbers of IoT devices. These results confirm that MPTS significantly enhances Quality of Service (QoS) by jointly optimizing delay, network utilization, and energy consumption, making it well suited for delay-sensitive and resource-constrained fog-enabled IoT applications.