The Supportive Smart Home System: Implications and Solutions for Service Providers
Bibliographic record
Abstract
Supportive smart home systems show the potential to enable older adults to age-in-place.However, research has not considered the communication challenge accompanied by widescale use.This thesis provides insight into supportive home systems' network traffic, identifies the impact of network impairments on a mechanism aimed to reduce network traffic, and develops a solution to ensure robustness of the traffic reduction mechanism to network impairments.Network traffic for two smart home systems and bed sensors was analyzed for 57 days.Results indicated a 10-fold difference in traffic between similar systems and the predominance of small packets which consume the network.Dual Machine Learning was implemented to reduce network traffic and, under simulated network impairments, yielded inaccuracies in cloud-recorded data.A solution was developed to mitigate the impact of network impairments, whereby accuracy increased from 71.4% to 94.6% for latency, 64.1% to 90.3% for jitter, and 61.6% to 78.9% for packet loss.I would like to thank my academic supervisors, Dr. Rafik Goubran and Dr. Bruce Wallace, for their tremendous support and continued encouragement throughout my master's journey.Thank you, Dr. Goubran, for providing me with opportunities I once thought were beyond my reach.Thank you, Dr. Wallace, for your admirable leadership and genuine care and for allowing
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".