Energy Efficient Distributed Filtering With Wireless Sensor Networks
Bibliographic record
Abstract
We consider a wireless sensor network (WSN) that monitors a physical field and communicates pertinent data to a distant fusion center (FC). We study the case of a binary valued hidden natural field observed in a significant amount of Gaussian clutter, which is relevant to applications like detection of plumes or oil slicks. The considerable spatio-temporal dependencies found in natural fields can be exploited to improve the reliability of the detection/estimation of hidden phenomena. While this problem has been previously treated using kernel-regression techniques, we formulate it as a task of delay-free filtering on a process observed by the sensors. We propose a distributed scalable implementation of the filter within the network. This is achieved by i) exploiting the localized spatio-temporal dependencies to define a hidden Markov model (HMM) in terms of an exponential family with O(N) parameters, where N is the size of the WSN, ii) using a reduced- state approximation of the propagated probability mass function, and iii) making a tractable approximation of model marginals by using iterated decoding algorithms like the Gibbs sampler (GS), mean field decoding (MFD), iterated conditional modes (ICM), and broadcast belief propagation (BBP). We compare the marginalization algorithms in terms of their information geometry, performance, complexity and communication load. Finally, we analyze the energy efficiency of the proposed distributed filter relative to brute force data fusion. It is demonstrated that when the FC is sufficiently far away from the sensor array, distributed filtering is significantly more energy efficient and can increase the lifetime of the WSN by one to two orders of magnitude.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".