A reliable and energy efficient IoT data transmission scheme for smart cities based on redundant residue based error correction coding
Bibliographic record
Abstract
Cities today face multifarious challenges, including environmental sustainability, and low carbon solutions. Given these trends, it is critical to understand how information and communication technology (ICT) can benefit the future of city-planning process. Internet of Things (IoT) is a futuristic communication concept integrating plethora of interconnected heterogeneous objects forming a large scale smart city system, that is, it can communicate and control other objects over a global network. Hence, devising a fault tolerant mechanism is very important because of construction and deployment characteristics of these smart low powered sensing devices. In this paper, we propose a fault detection and error correction scheme based on redundant residue arithmetic, which provides a low complexity, delay tolerant and energy-efficient data transmission solution. By comparing the proposed method with existing solution in perceived packet loss rate and expected delivery latency, we show advantages of the proposed solution in improving data transmission quality.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".