Joint source coding, routing and resource allocation for wireless sensor networks
Bibliographic record
Abstract
This paper presents an optimization framework for a wireless sensor network in which each sensor plays a dual role of sensing the environment and relaying the sensor information. The design of such a network involves two distinct aspects. First, as the observations of the underlying environment are often correlated, distributive source coding methods have the potential to greatly improve the efficiency of the sensor operation. Thus, information theoretical source coding methods are useful in the application layer. Second, as each sensor must send information individually to a central processor, routing and power allocation in the network and physical layers are also important issues. The main focus of this paper is an optimization framework that jointly solves the source coding, routing and power allocation problems in such a network. The main insight is the following: the joint optimization problem for a sensor network, when solved in the dual domain, provides a natural separation between the application layer, the network layer and the physical layer. The interface between the layers is precisely the dual optimization variables. The crucial observation that makes this possible is that the underlying source coding problem in the application layer and the channel coding problem in the physical layer can always be made convex via time-division or frequency-division multiplexing. Convexification in time or frequency enables dual algorithms to reach the global optimum of the overall network optimization problem efficiently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| 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".