QoS-aware energy efficient time-slotted channel schedule for heterogeneous IoT sensor networks

Abstract

The emergence of the Internet of Things (IoT) has attracted significant attention in industrial environments, where applications must meet specified Quality of Service (QoS) requirements for latency, throughput, and packet loss. To address this, the IEEE 802.15.4e standard introduced the Time Slotted Channel Hopping (TSCH) Medium Access Control (MAC) protocol. However, the protocol does not specify any particular MAC schedule. Designing a centralized scheduling system that simultaneously achieves the required QoS is challenging due to the multi-objective optimization nature of the problem. Additionally, managing the energy consumption of IoT devices is also crucial while achieving the QoS requirements of the sensing applications. This thesis presents a novel QoS-aware Energy Efficient optimized TSCH scheduling algorithm (QoE-TSCH), designed to meet QoS requirements such as delay and packet loss for multiple services within a heterogeneous sensor network, while also achieving the expected throughput. The QoE-TSCH algorithm incorporates a padding strategy to increase the duty cycle, resulting in reduced energy consumption. The QoE-TSCH algorithm was implemented in MATLAB and evaluated within a co-simulation environment that integrates both MATLAB and TSCH, focusing on a range of sensor network topologies and industrial QoS scenarios as defined by the ISA SP100 standard. The evaluation results indicate that the optimum schedules produced by the QoE-TSCH algorithm effectively support both “open-loop” and “monitoring” industrial services specified in the ISA SP100 standard for sensor networks comprising 16 to 36 nodes. While the delay requirements are met in scenarios involving 64 nodes, the packet loss rates in these cases exceed the maximum acceptable threshold by an average of 0.5%. Additionally, the algorithm’s energy-saving strategy significantly improves the scheduling duty cycle. The reduction in the duty cycle enhances energy efficiency across sensor network configurations ranging from 16 to 64 nodes. Specifically, for scenarios with 16 to 36 nodes, the duty cycle was reduced by approximately 80%, while for scenarios with 64 nodes, the reduction was around 15%.