International Conference on Computer and Knowledge Engineering

Home / 14th International Conference on Computer and Knowledge Engineering

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.

Papers List

List of archived papers

Density Estimation Helps Adversarial Robustness

Afsaneh Hasanebrahimi - Bahareh Kaviani Baghbaderani - Reshad Hosseini - Ahmad Kalhor

Bipartite link prediction improvement using the effective utilization of edge betweenness centrality

Sadegh Sulaimany Sulaimany - Yasin Amini

Efficient T-Count Fault-tolerant Quantum Clifford+T Multiplexer

Negin Mashayekhi - Shekoofeh Moghimi - Mohammad Reza Reshadinezhad

MIPS-Core Application Specific Instruction-Set Processor for IDEA Cryptography − Comparison between Single-Cycle and Multi-Cycle Architectures

Ahmad Ahmadi - Reza Faghih Mirzaee

Novel Insights in Deep Learning for Predicting Climate Phenomena

Mohammad Naisipour - Saghar Ganji - Iraj Saeedpanah - Behnam Mehrakizadeh - Ahmad Reza Labibzadeh

Adaptive-A-GCRNN: Enhancing Real-time Multi-band Spectrum Prediction through Attention-based Spatial-Temporal Modeling

Seyed majid Hosseini - Seyedeh Mozhgan Rahmatinia - Seyed Amin Hosseini Seno - Hadi Sadoghi yazdi

Adaptive Hybrid TRCA–CORRCA algorithm for enhanced accuracy in SSVEP-based brain-computer interfaces

Sepehr Tayebeh Khabbaz - Sina Tayebeh Khabbaz - Arshia Barani - Arsalan Ganjeh - Sasan Harifi - Seyed Mohsen Mirhosseini

PowerLinear Activation Functions with application to the first layer of CNNs

Kamyar Nasiri - Kamaledin Ghiasi-Shirazi

Classification of COVID-19 and Nodule in CT Images using Deep Convolutional Neural Network

Amirhossein Ghaemi - Seyyed Amir Mousavi mobarakeh - Habibollah Danyali - Kamran Kazemi

A Facial Deepfake Detection Approach using CNN-based Models, Swin Transformer and Classifier Fusion

Alireza Honardoost - Mahdie Rahmati - Babak Nasersharif