Bibliographic record
Abstract
This thesis presents two main achievements. The first is a novel link-layer encryption protocol for wireless sensor networks. The protocol design aims to reduce energy consumption by reducing security-related communication overhead. This is done by merging security-related data of consecutive packets. The merging is based on simple mathematical operations. It helps to reduce energy consumption by eliminating the requirement to transmit security-related fields in the packet. The protocol is named the Compact Security Protocol and is referred to as C-Sec. In addition to energy savings, the C-Sec protocol also includes a unique security feature of hiding the packet header information. This feature makes it more difficult to trace the flow of wireless communication, and helps to minimize the effect of replay attacks. The C-Sec protocol is rigorously tested and compared with well-known related protocols. Performance evaluations demonstrate that C-Sec protocol outperforms other protocols in terms of energy savings. The protocol is evaluated with respect to other performance metrics including queuing delay and error probability.\n \n \n The C-Sec operation requires fast encryption, which leads to a second major contribution: The SN-Sec, a 32-bit RISC secure wireless sensor platform with hardware cryptographic primitives. The security vulnerabilities in current WSNs platforms are scrutinized and the main approaches to implementing their cryptographic primitives are compared in terms of security, time, and energy efficiency. The SN-Sec secures these vulnerabilities and provides more time and energy efficiency. The choice of cryptographic primitives for SN-Sec is based on their compatibility with the constrained nature of WSNs and their security. The AES implementation has the best data-path and S-Box design in the literature. All SHA family members are implemented and compared to choose the most compatible with WSN constraints. An efficient elliptic-curve processor design is proposed. It has the least mathematical operations compared to elliptic-curve processors proposed for WSNs in the literature. It also exploits parallelism among mathematical operations to compute elliptic-curve point multiplication with minimal amount of clock cycles. SN-Sec is implemented using VHDL. Experimental results using synthesis for Spartan-6 low-power FPGA shows that the proposed design has very reasonable computational time and energy consumption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".