IoT Devices Modular Security Approach Using Positioning Security Engine
Bibliographic record
Abstract
In this paper, we propose a modular security approach using a positioning security engine featuring Global Positioning System (GPS) location features that can uniquely identify the Internet of Things (IoT) user device. Our approach aims to reinforce the security and viability of IoT-centric solutions for various innovative applications, including IoT Mobile payment, Smart city heterogeneous networks, communication services, safety, and location-based services integration. To achieve our goal of securitization and viability, we target consumer IoT devices equipped with built-in location-based GPS chips, which are vulnerable to hackers where the existing cryptographic authentication-based protocols demand power and computation resources required for authentication protocols is not sufficient to carry end to end secure transaction in an IoT environment. Therefore, to compensate this lack of environment capability to carry the end-to-end secure transaction on IoT devices when emitting various radio signals, we implement a modular security approach to compensate the lack of capabilities. This leads to an optimal security facilitated by Simple Public Key Infrastructure following the Pretty Good Privacy Web of Trust approach. Moreover, our implementation on the development board Arduino succeeded in providing and extended secure capable environment for carrying secure transactions. The results show a communication success rate of 70, 80 and 90 percent between Security Engine component called modules, with 70 percent of successful Secure Sockets Layer (SSL) key exchange by every identified user in average 15 seconds simulation running time for every two by third round of simulation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".