Bibliographic record
Abstract
Abstract Radio Frequency Identification (RFID) is a promising technology to provide automated contactless identification of objects, people and animals. The identification process is performed as the reader receives simultaneous responses from various tags over a shared wireless channel and without no requirement of line-of-sight in the interrogation zone. The communication between the reader and tags is separated into two processes: identification and acknowledgment processes. Both processes suffer from serious drawbacks that limit the proliferation of RFID. Such drawbacks are security and privacy and collision problems. This thesis has two main parts. The first part examines the security and privacy of the existing RFID authentication protocols. We introduced a novel cryptographic scheme, Hacker Proof Authentication Protocol (HPAP) that allows mutual authentication and achieves full security by deploying tag static identifier, updated timestamp, a one-way hash function and encryption keys with randomized update using Linear Feedback Shift Register (LFSR). Cryptanalysis and simulation show that the protocol is secure against various attacks. In comparison with the various existing RFID authentication protocols, our protocol has less computation load, requires less storage, and costs less. The second part focuses on solving RFID collision arbitration imposed by the shared wireless link between a reader and the many tags distributed in the interrogation zone. In most proposed anticollision algorithms, tags reply randomly to the time slots chosen by the reader. Since more than two tags may choose the same time slot in a frame, this Random Access (RA) causes garbled data at the reader side resulting the identification process fails. Towards this challenge, two ALOHA based anti-collision algorithms that adopt a new way for tags to choose their replied time slots to enhance system efficiency are presented. In MBA and LTMBA, tags use modulo function to choose their owned time slot. The difference between the two algorithms relies on the method by which the reader estimates the next frame size. The performance evaluation of the two algorithms shows better performance than previously proposed algorithms in terms of fewer communication rounds and fewer collided/empty slots considering the limitation of the EPCglobal Class-1 Gen-2 standard.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".