Quality of Service-Aware Multi-Objective Enhanced Differential Evolution Optimization for Time Slotted Channel...

Area of Science:

• Computer Science
• Network Engineering
• Optimization Algorithms

Background:

• Industrial Internet of Things (IoT) applications demand strict latency and reliability.
• The IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) protocol addresses these needs.
• Centralized scheduling for multi-objective QoS optimization in TSCH networks is complex.

Purpose of the Study:

• To introduce a novel optimization algorithm, QMDE, for enhancing QoS in industrial IoT networks.
• To address multi-objective optimization challenges in designing centralized scheduling systems.
• To ensure required service throughput alongside critical QoS metrics like delay and packet loss.

Main Methods:

• Development of the QoS-aware Multi-objective enhanced Differential Evolution optimization (QMDE) algorithm.
• Co-simulation using TSCH-SIM and Matlab, R2023a.
• Extensive simulations across diverse sensor network topologies and industrial QoS scenarios.
• Comparative evaluation against the Traffic-Aware Scheduling Algorithm (TASA).

Main Results:

• QMDE generates optimal schedules that fulfill QoS requirements for industrial services.
• Effective performance demonstrated in sensor networks ranging from 16 to 100 nodes.
• QMDE significantly enhances Packet Delivery Ratio (PDR) and reduces delay compared to TASA.

Conclusions:

• The QMDE algorithm successfully addresses the multi-objective optimization problem in TSCH scheduling.
• QMDE provides a robust solution for meeting stringent QoS demands in industrial IoT.
• The proposed algorithm offers superior performance in terms of PDR and delay metrics.