Internet of Things-based On-demand Rental Asset Tracking and Monitoring System
Bibliographic record
Abstract
The Internet of things (IoT) technology can track and monitor an asset in the outdoor and indoor environment. This empowers businesses and end-users with the information and opportunity to run their operations efficiently and make educated decisions, respectively. In this paper, we examine the IoT-based rental asset tracking and monitoring system to develop innovative and flexible IoT devices for better management of asset infrastructure. We study different wireless technologies for monitoring the physical location of rental items. These technologies include WiFi, Bluetooth, GSM cellular, and LoRa. We propose an architecture of on-demand asset tracking and monitoring. We then present a case study to support the idea presented in the architecture, including the development of IoT devices, integration with the existing infrastructure of potential business, and field deployment for rental management assets. First, we develop IoT devices tailored for tracking rental items, so business owners can on-demand track their rental items' physical location. The goal is to maximize connectivity distance and minimize energy consumption while considering constraints on the tracking system's cost and size. Finally, we study feasibility by deploying the proposed solution to address rental asset management challenges and study feasibility while ensuring the scalability and quality-of-service (QoS) in terms of delay and reliability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".