High Throughput and Thermal Aware Routing Protocol (HTTRP) for Wireless Body Area Networks
Bibliographic record
Abstract
One of the major applications of sensor networks in the near future will be in the area of biomedical research. Wireless Body Area Networks (WBANs) are composed of implanted biosensors for health monitoring and diagnostic purposes. The communication between these sensors is made in a wireless way at the base of the radio waves. The sensors' activity produces heat causing a temperature rise. The high-temperature rise of the sensors for a prolonged period might damage the surrounding tissues. Various routing protocols have been proposed in the literature to remedy this problem. These protocols tried to perform routing based on the SHR algorithm while avoiding hot-spots nodes. However, the energy of sensor nodes located in this shorter path is quickly exhausted and by the way, the whole network lifetime is influenced. In this work, we propose HTTRP, a new routing protocol for WBANs introducing a new route selection mechanism that aims to reduce the overheating of sensors and balance their energy consumption. This mechanism is based on a function that considers the residual energy of sensor nodes and their temperature when choosing the next relay node. The carried out simulation results show that our HTTRP protocol has better performance in terms of network lifetime, charge balancing, temperature rise, and throughput compared to a representative of TARP that is TARA protocol.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".