International Conference on Computer and Knowledge Engineering

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Adaptive Active Queue Management for Time Slot Channel Hopping in Industrial Internet of Things

Authors :

Mehdi Zirak¹ Yasser Sedaghat² Mohammad Hossein Yaghmaee Moghaddam³

1- Computer Engineering Department, Ferdowsi University of Mashhad - Mashhad, Iran 2- Computer Engineering Department, Ferdowsi University of Mashhad - Mashhad, Iran 3- Computer Engineering Department, Ferdowsi University of Mashhad - Mashhad, Iran

Keywords :

industrial internet of things،time slot channel hopping،queue management،active queue management

Abstract :

The Industrial Internet of Things (IIoT) enhances productivity by enabling industrial environments to collect discrete data, leverage intelligence, monitor operations continuously, and perform predictive maintenance. Consequently, the IIoT has emerged as a core component of the Industry 4.0 (I4.0) revolution. High Quality of Service (QoS) is a fundamental requirement that differentiates the IIoT from Internet of Things (IoT). Time Slot Channel Hopping (TSCH), a standard scheduling protocol introduced in IEEE 802.15.4e, aims to increase reliability and provide controlled latency for IIoT networks. TSCH operates through two main components: Slotframe and queue. The slotframe is a matrix that dictates communication scheduling between network nodes, while the queue is a buffer that temporarily holds packets received from higher layers before transmission. Scheduling and queue management are complementary policies, yet while various scheduling approaches have been proposed, queue management has often been overlooked. Our studies indicate that effective queue management significantly mitigates queue overflow and waiting times and improves QoS parameters such as reliability, average delay, and maximum delay. This paper introduces an Adaptive Active Queue Management (A2QM) mechanism for TSCH queue management to improve QoS parameters through effective queue length control in IIoT networks. The A2QM adjusts the queue length using a distributed and dynamic approach to reduce queue waiting and overflow based on node location, available bandwidth, and queue occupancy. Simulation results demonstrate that A2QM enhances QoS, load balancing, and queue efficiency. Consequently, improvements of 13%, 41%, 66%, 38%, and 41% were achieved in the reliability, average delay, maximum delay, average, and standard deviation of queue length criteria, respectively.

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