Dynamic Weight Fusion with Entropy Optimization for Enhanced Distributed Object Detection and Resource Allocation
Bibliographic record
Abstract
The Dynamic Weight Fusion with Entropy Optimization algorithm introduces an innovative approach to enhancing distributed object detection and resource allocation in environments with limited resources, such as IoT networks and edge computing systems. Conventional object detection methods struggle to balance accuracy and resource efficiency when deployed on various devices with different computational strengths. The Entropy Weight Fusion algorithm tackles these challenges by integrating measures of uncertainty to dynamically modify the weight of each device’s contribution, prioritizing outputs with higher reliability while reducing the impact of uncertain detections. This strategy enables more effective aggregation of detection outputs, resulting in enhanced detection precision, minimized packet loss, and steady latency performance. Comprehensive evaluations highlight the algorithm’s effectiveness in achieving high utility scores and managing resources efficiently, making it a promising solution for real-time applications such as surveillance, autonomous systems, and other use cases requiring dependable distributed object detection. This work addresses the challenge of maintaining detection accuracy under limited resources in edge environments. By combining utility-based scoring with entropy-driven uncertainty handling, the proposed method enables adaptive, efficient object detection across heterogeneous devices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".