AODV adaptation for semi-static smart grid monitoring systems
Bibliographic record
Abstract
One of the main implementations of wireless sensor networks (WSNs) is monitoring equipment. These types of equipment can range from transmission line systems to hydro usage data collection in residential areas. The functionality of WSNs can also be extended to sending commands or instructions to external systems based on varying conditions or emergencies. Mesh networks have proved valuable in smart grid applications based on the self-configuring, self-healing nature of the mesh networking protocols. Ad Hoc On-Demand Distance Vector (AODV) routing protocol has been chosen as a base for redesigning a routing protocol to monitor transmission lines and collect data. Lowering overhead in network traffic is crucial to decreasing latency times, which is the motivation for our proposed design. Our design exploits the static nature of the data collection and Smart Grid monitoring networks. AODV-Uppsala University (AODV-UU) is a reactive, self-healing, and self-configuring routing protocol designed for Mobile Ad Hoc Networks (MANETs). Due to the fact that high frequency periodic rediscovery of neighbors is not necessary with a more static network topology, neighbor discovery in AODV-UU has been modified to lower the control overhead traffic. Additionally, through extending the lifetime of valid routes our design can throttle down control messages used for route maintenance. The modification was implemented in a real-time prototype and our results show increased throughput and a lower end-to-end delay of data transmission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".