An intelligent agent for fault reconnaissance in sensor networks
Bibliographic record
Abstract
An embedded sensor network is a system of nodes, each of which is equipped with a certain amount of sensing, actuating, computation, communication and storage components. Two major components of sensor nodes are sensing unit and wireless transceiver. They directly interact with nodes in wireless sensor networks (WSNs) that are easily prone to failure due to hardware failure, communication link errors, energy depletion, malicious attacks, etc. Even if the sensor node hardware is in excellent condition, still the communication between sensor nodes depends on many factors such as signal strength, obstacles and interference. Degradation in these factors results in low reliability of sensor nodes. One of the key prerequisite for an effective, efficient embedded sensor network is utilization of low-cost, low-overhead and high-resilient fault-inference techniques. Our attempt is to address fault-inference issues in sensor networks using an agent-based approach. Our proposed approach involves an intelligent agent that outfitted with a fault-inference engine. This engine profits from Expectation Maximization (EM) algorithm to evaluate fault probabilities of sensor nodes. Our experiment in a wireless sensor testbed is conducted to demonstrate the correctness and effectiveness of our approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".