HPEQ A Hierarchical Periodic, Event-driven and Query-based Wireless Sensor Network Protocol
Bibliographic record
Abstract
Applications that require fine-grain monitoring of physical environments subjected to critical conditions, such as fire, leaking of toxic gases and explosions, pose a great challenge to sensor network protocols. These networks have to provide a fast, reliable, fault tolerant and energy aware channel for events diffusion, which meets the requirements of query-based, event-driven and periodic sensor networks application scenarios. These requirements have to be met even in the presence of emergency conditions that can lead to node failures and path disruption to the sink. This paper presents HPEQ (hierarchical periodic, event-driven and query-based), a cluster-based routing protocol that groups sensor nodes to efficiently relay the sensed data to the sink. In HPEQ protocol nodes with more residual energy are selected as aggregator nodes that relay data to the sink by uniformly distributing energy dissipation among the nodes, and reducing latency and network data traffic. HPEQ is based on a previous protocol, PEQ, that meets sensor networks requirements for critical conditions surveillance applications. HPEQ uses the publish/subscribe paradigm to disseminate requests across the network. The algorithm was implemented using NS-2 simulator and compared to PEQ and to the directed diffusion paradigm. Important metrics were evaluated showing that the proposed algorithm can be a potential solution to meet constraints and requirements of events delivery in critical conditions monitoring applications
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".