Identity Management in IoT Networks Using Blockchain and Smart Contracts
Bibliographic record
Abstract
Internet of Things (IoT) devices proliferation is on the rise, where the number of connected devices has surpassed 7 billion devices in 2017, whereas insights foresee a number of 20-50 billion connections by year 2020. Most of those devices are deployed in heterogeneous and complex networks which imposes many challenges on the devices management functionality. Among those challenges is the identity management which pertains to how devices' identities are authenticated and verified in addition to how devices establish the means for authorizing and controlling access to data and services. Blockchain as a distributed ledger technology positions itself as a suitable candidate to address this challenge. That is mainly attributed to Blockchain's use of cryptographic identifiers, records immutability, and provenance. These features, together, provide a platform to implement the functions of IoT devices identity management that can ensure a global and unique identity for the devices, and also provide the mechanism to maintain it throughout the device life cycle. This paper presents a semi-decentralized Blockchain-based IoT identity management framework that provides features of identity creation and transfer of ownership, along with the capability of identity portability among networks visited by the devices. For validation, we describe a set of smart contracts that provide the functions of the registrar and management contracts.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".