A Location Routing Protocol based on Smart Antennas for Wireless Sensor Networks
Bibliographic record
Abstract
The task of finding and maintaining routes in a Wireless Sensor Networks is a nontrivial task since energy restrictions and sudden changes in node status (e.g. failure) cause frequent and unpredictable topological changes. This work introduces a novel location routing protocol that uses smart antennas to estimate nodes positions into the network and to deliver information basing routing decisions on neighbor’s status connection and relative position, named LBRA. The main purpose of LBRA is to eliminate network control overhead as much as possible. To achieve this goal, the algorithm employs local position for route decision, implements a novel mechanism to collect the location information and involves only route participants in the synchronization of location information. In addition, the protocol uses node battery information to make power aware routing decisions. In order to asses LBRA a series of simulations were designed with the help of the Network Simulator 2 (ns2). The experiment results showed that LBRA succeed in reducing the control overhead and the routing load, improving the packet delivery rate. Additionally, network power depletion is more balanced, since routing decisions are made depending on nodes’ battery level Keywords: Local Positioning, Routing Protocol, Smart Antennas, Wireless Sensor Networks
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".