Intelligent Systems for Energy Management in Wireless Sensor-Based Smart Environments
Bibliographic record
Abstract
The purpose of this chapter is to explore and address the issues that are applicable to Smart Environments by encouraging and providing new insights towards the “Sustainable Green Computing” initiatives for “energy aware” applicable solutions. The topics covered in this chapter provide an introduction to future wireless sensor-based smart environments for energy management systems. Introduction to the topic, motivation, and objective are covered in Section 1. A review of the state-of-the-art technological achievements, theory, and applications, related to the energy management systems (wireless sensors and intelligent systems) are covered in Section 2. Whilst, Section 3 covers in detail the authors’ proposed methodological approach and main ideas leading towards the “Intelligent Systems for Energy Management in Wireless Sensor-Based Smart Environments.” Case studies of real-world applications, following the principles of “Green Computing” in intelligent systems are introduced. The authors present the simulation results of an “energy conservation perspective” in smart homes, demonstrating the potential improvements with respect to energy conservation. Moreover, they present examples of large Wireless Sensor Networks (WSN) simulations (impact of topology control in network survivability) and hybrid intelligent techniques for energy efficient solutions, i.e. finding optimal solution in a predefined interval. The conclusions and future research directions are provided in Sections 4 and 5, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".