QoS-Aware Energy-Efficient Time-Slotted Channel Hopping Scheduling Algorithm
Bibliographic record
Abstract
Efficiently balancing Quality of Service (QoS) requirements with energy conservation is essential for the sustainability of Internet of Things (IoT) sensor networks in industrial applications. This paper introduces a novel algorithm designed to optimize QoS and energy efficiency in Time-Slotted Channel Hopping (TSCH) networks for heterogeneous IoT sensor systems. The proposed approach meets application-specific QoS requirements, such as delay and packet loss, while reducing energy consumption through a strategic duty cycle optimization technique. Implemented in MATLAB and tested in a co-simulation environment, the algorithm was evaluated across various sensor network topologies and industrial QoS scenarios aligned with the ISA SP100 standard. The results demonstrate that the algorithm supports the delay and packet loss requirements for “open-loop” and “monitoring” services in networks of 16 to 36 nodes. For larger topologies of up to 64 nodes, it meets delay requirements but slightly exceeds packet loss thresholds by an average of 0.5%. Importantly, the duty cycle reduction strategy significantly enhances energy efficiency, lowering duty cycles by approximately 80% for networks of 16 to 36 nodes and by 15% for networks with 64 nodes. This method thus provides a robust solution for ensuring QoS compliance in industrial IoT systems, balancing high performance with optimized energy usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".