Energy Efficient Routing Algorithm for WSN-IoT Network
Bibliographic record
Abstract
In various domains, to enable reaction and detection of anomalies IoT (Internet of Things) have infrastructure in which significant sensory data gathering method is important because IoT nodes has limited computational capacity and energy.Range rather than sensory data accurate value which is fascinating to domain applications, range is expressed as sensory data category.Redundancy of data is discouraged by the routing algorithm and current data transfer, which decreases energy, bandwidth and memory usage.Between energy consumption and security level, there is tradeoff.Security level is nothing but it is intensive computational operations.For Secure transmission of data and to minimize consumption of energy in IoT-WSN networks, this paper proposes Spanning Tree Based Flooding Mechanism with Cooperative Game Theory (STBFM-CGT) method for efficient data transmission.To minimize consumption of energy in IoT-WSN networks, this paper proposes Spanning Tree Based Flooding Mechanism with Cooperative Game Theory (STBFM-CGT) method for efficient data transmission.For synchronization purpose, sensory data of IoT is routed and predicted through data prediction model in cloud.When compared with state-of-art method by considering packet delivery ratio (PDR), network lifetime, throughput and energy consumption, this method achieves better results.As a result, proposed method achieves 23.8% energy consumption, 53.2% network lifetime, 75.4% of PDR and 76.6% of throughput.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".