Optimization in wireless sensor and machine-type communication networks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) are systems used for detecting events and gathering information from an area of interest in many different application domains, from home and industry automation, to healthcare and transportation, to environmental monitoring. With regard to the communication task involved in WSNs, they can also be seen as an instance of the new paradigm, known as machine-type communication (MTC). Similar to traditional wireless sensors, MTC-enabled devices can communicate together without direct human interference. Energy efficiency for the sake of longevity is perhaps the most challenging requirement for many WSNs and MTC networks. In this thesis, we consider ultra-wideband (UWB) transmission technology for energy-efficient communication in WSNs. UWB achieves frugal use of energy by transmitting with low spectral efficiency when compared to legacy wireless technologies. This also allows it to operate license-exempt in many jurisdictions around the world. More recently, however, wireless service operators consider the use of cellular technology also for low data-rate applications originally only served by WSN-type technology. In particular, long-term evolution (LTE) technology has moved into the focus for joint personal-communication and MTC networks. Recent releases of the LTE standard and ongoing work items in LTE standardization specifically accommodate low-cost and low-power MTC. This thesis presents contributions that improve the performance of UWB WSN and LTE MTC networks in several aspects, namely lifetime, localization accuracy, and coverage. A common theme of these different contributions are the use of optimization methods for obtaining scalable, robust, and/or low-complexity solutions. We first address the lifetime maximization problem in a UWB-based WSN designed for multiple event detection. The key contribution is the joint optimization of transmission and routing parameters of sensor nodes so that the energy consumption is distributed as evenly as possible among the entire WSN. We then investigate the challenges of localization in WSNs and provide a convex solution which is robust to measurement uncertainties. In the last part of this thesis we focus on providing coverage for low-cost LTE MTC networks, where the challenge is to develop efficient transmission strategies that maximize the coverage of MTC devices in an LTE cell.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".