Permissioned Blockchain Reinforced API Platform for Data Management in IoT-based Sensor Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the rise of IoT-based sensor networks, there is an increasing need for proper security mechanisms and efficient management of crucial resources, such as energy. At the same time, as more services are integrated, more sensors are introduced into the network. As a consequence, the architecture that manages these sensors need to be improved. Blockchains can be a solution in terms of data security. To cater to the inexpensive devices used as sensors in most IoT-based sensor networks, usually, permissioned blockchains are used as its base data management structure. However, due to the diverse collection of sensors that can be incorporated into a network, a static database is not enough. A more sustainable and automative system is needed as its service administrator. Therefore, we chose to integrate smart contracts to enable adaptive and dynamic automation. Lastly, to manage the reception of all the incoming data, a proper interface is required. Therefore, we chose to encapsulate the blockchain within a REST API to enable a systematic allocation of network resources. With these technologies, we propose a low-cost platform to address the management issues of current IoT-based sensor networks. We tested our design against a commercial REST API service and standard socket communication to test its feasibility. The testing metrics were latency and throughput. Based on the results, our platform proved to be the most stable and proficient among the configurations. Therefore, our proposed design shows promise as a low-cost, secure, and systematic means of managing IoT-based sensor networks.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.008 | 0.003 |
| 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 it