Wireless home automation networks for indoor surveillance: technologies and experiments
Bibliographic record
Abstract
Abstract The use of wireless technologies for critical surveillance and home automation introduces a number of opportunities as well as technological challenges. New emerging technologies give the opportunity to exploit the full potential of the internet of things paradigm by augmenting existing wired installations with smart wireless architectures. This work gives an overview of requirements, characteristics, and challenges of wireless home automation networks with special focus on intrusion detection systems. The proposed wireless network is based on several sensors that are deployed over a monitored area for detecting possible risky situations and triggering appropriate actions in response. The network needs to support critical traffic patterns with different characteristics and quality constraints. Namely, it should provide a periodic low-power monitoring service and, in case of intrusion detection, a real-time alarm propagation mechanism over inherently unreliable wireless links subject to fluctuations of the signal power. Following the guidelines introduced by recent standardization, this paper proposes the design of a wireless network prototype at 868 MHz which is able to satisfy the specifications of typical intrusion detection applications. A proprietary medium access control is developed based on the low-power SimpliciTI radio stack (Texas Instruments Incorporated, San Diego, CA, USA). Network performance is assessed by experimental measurements using a test-bed in an indoor office environment with severe multipath and nonline-of-sight propagation conditions. The measurement campaigns highlight the potential of the sub-GHz technology for cable replacing.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".