Impacts of Internet of Things (IoT) Leveraging Radio Frequency Identification (RFID) and Internet Protocol (IP) on Business Continuity (BC) and Disaster Recovery (DR)
Bibliographic record
Abstract
The pervasive presence of Internet of Things (IoT) technologies, in conjunction with Radio Frequency Identification (RFID) and Internet Protocol (IP), has significantly revolutionized the business operations panorama. This paper aims to explore the impact of these technologies on enhancing Business Continuity (BC) and Disaster Recovery (DR) strategies for organizations operating in dynamic and unpredictable environments. In today's highly interconnected and digitalized business environment, organizations are increasingly vulnerable to disruptions and disasters. Traditional BC and DR approaches often struggle to keep pace with the scale and complexity of modern business operations. This paper focuses on leveraging IoT, RFID, and IP technologies to enhance BC and DR strategies by showcasing their ability to enable real-time data collection, predictive analytics, and adaptive decision-making. It draws upon relevant industry best practices to highlight practical applications and the transformative potential of these technologies for BC and DR. The findings will significantly enhance our current evaluation of various Endpoint Detection and Response (EDR) products by offering deeper insights into how IoT, RFID, and IP technologies can be leveraged to improve these systems. This paper will thus offer valuable insights into optimizing BC and DR strategies and advancing cybersecurity frameworks through the strategic application of modern technologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".