Determination of Optimal Power for ZigBee-based Wireless Sensor Networks
Bibliographic record
Abstract
In designing WSNs, both the transmit power, network topology, and routing scheme are considered. Transmitting at lower power affect the connectivity of the network while transmitting at excessive power reduces the lifetime of nodes and increases the network interference. Thus, determining the optimal power of the nodes that will be necessary to guarantee network connectivity. In this work, a practical self-healing and self-configuring real life prototype ZigBee Wireless Mesh Sensor Networks (WMSNs) was design to evaluate the performance of IEEE 802.15.4/ZigBee. We showed that increasing the transmit power of nodes from -6dBm to 0dBm in WMSNs leads to improved packets delivery ratio and throughput improvement and the optimal power was -2dBm for the studied topology. The testbed will aid wireless sensor network designer to make an accurate decision on transmit power and mesh network topology using Ad-hoc on-demand distance vector algorithm (AODV) as the routing scheme.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".