An Energy Efficient Technique for Improved Network Lifetime in Wireless Sensor Network (WSN) Through Energy, Distance, and Density-Based Clustering
Bibliographic record
Abstract
The intelligent networks utilize smart and AI federated technologies especially in Industrial Internet of Things (IIoT), as it is the need of the hour to gather the data from sensor devices deployed at diverse locations for the drawing inferences from the gathered data.The data transmission operation requires smart technologies to move the data between base stations and mobile devices.Usually, the sensor devices have limited resources for storage as well as for preserving energy.The design of the network should be done in such a way that it can reduce the energy consumption and the data transmission time.This can help improve the lifetime of the network.With the advent of AI based technologies, it is possible now to integrate the underlying technologies such as datamining, IoT and AI federated technologies to create the clusters of sensing nodes to minimize energy usage.Thus, this study discusses three different cluster-based models for data collection and transmission.The first model is used to create a cluster and then select its head based on the energy parameters.The second model, on the other hand, uses a clustering method to create the cluster and then select its head.The third model in this paper presents the main contribution of the cluster creation process by considering the various parameters that affect the density and energy of the cluster.The simulation experiments are performed for all three models using JUNG simulator.The experimental results show that the third approach with considering energy, distance, and density for selecting the clustering head achieves the optimal results for enhancing the lifetime of the network.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".