Efficient and Reliable In-Network Query Processing in Wireless Sensor Networks
Bibliographic record
Abstract
The Wireless Sensor Networks (WSNs) have emerged as a new paradigm for collecting and processing data from physical environments, such as wild life sanctuaries, large warehouses, and battlefields. Users can access sensor data by issuing queries over the network, e.g., to find what are the 10 highest temperature values in the network. Typically, a WSN operates by constructing a logical topology, such as a spanning tree, built on top of the physical topology of the network. The constructed logical topology is then used to disseminate queries in the network, and also to process and return the results of such queries back to the user. A major challenge in this context is prolonging the network's lifetime that mainly depends on the energy cost of data communication via wireless radios, which is known to be very expensive as compared to the cost of data processing within the network. In this research, we investigate some of the core problems that deal with the different aspects of in-network query processing in WSNs. In that context, we propose an efficient filtering based algorithm for the top-k query processing in WSNs. Through a systematic study of the top-k query processing in WSNs we propose several solutions in this thesis, which are applicable not only to the top-k queries, but also to in-network query processing problems in general. Specifically, we consider broadcasting and convergecasting, which are two basic operations that are required by many in-network query processing solutions. Scheduling broadcasting and convergecasting is another problem that is important for energy efficiency in WSNs. Failure of communication links, which are common in WSNs, is yet another important issue that needs to be addressed. In this research, we take a holistic approach to deal with the above problems while processing the top-k queries in WSNs. To this end, the thesis makes several contributions. In particular, our proposed solutions include new logical topologies, scheduling algorithms, and an overall sophisticated communication framework, which allows to process the top-k queries efficiently and with increased reliability. Extensive simulation studies reveal that our solutions are not only energy efficient, saving up to 50% of the energy cost as compared to the current state-of-the-art solutions, but they are also robust to link failures.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".