A Reliable IEEE 802.15.4 Model for Cyber Physical Power Grid Monitoring Systems
Bibliographic record
Abstract
Cyber physical systems (CPSs) can significantly improve the resiliency of the smart grid. In CPSs, real time and reliable monitoring require an accurate and stable model of the wireless sensor network (WSN)-based monitoring system. Furthermore, WSNs require strict quality of service (QoS) provisioning as the data generated by the monitored equipment is generally delay and reliability-sensitive. QoS provisioning in WSNs has been widely studied in the literature where most of the work addresses the issue by QoS-aware protocol design. However, analytical models that consider delay, throughput, and power consumption have not matured for CPSs. In this paper, we propose a Markov-based model for cluster-tree WSN topologies that enhances the stability of the WSNs. Cluster-tree deployments are particularly of interest to cyber-physical power grid monitoring systems since they are suitable for large-scale deployments. We perform an exhaustive performance evaluation using different traffic and network conditions in star and cluster-tree WSN topologies. Furthermore, we test the accuracy of our model by performing simulations in environments that are consistent with the analytical model.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".