IoT and Blockchain for Smart Locks
Bibliographic record
Abstract
Community-based online platforms for hospitality services have connected hosts and guests globally. With the increasing popularity of those platforms - e.g., Airbnb - some management issues have attracted the attention of researchers - for instance, granting access to properties and rooms remotely, without requiring hosts and guests to meet in person. Solutions have been proposed - e.g., locks with pin pad and vendor centralized smart locks - but they usually have shortcomings that compromise either security, privacy, or convenience. This research designs and proposes a blockchain-based system for smart door locks to provide the convenience of remote access control management, while security and privacy for both hosts and guests are not compromised. Moreover, to surpass current locks' functionalities, this research proposes a feature that enables the guests to cease the hosts' access to the lock, during their stay. This feature also guarantees to the guest that no one will be able to change that access rule without their explicit approval. The proposed solution integrates Ethereum blockchain as the foundation of the system and uses Infura API as the bridge to connect the IoT infrastructure to the blockchain network. Such architecture alleviates hardware demand from the equipment, which can favor the use of resource-constrained IoT devices. Once Ethereum is used to build the solution, and users are charged fees to perform some actions in the blockchain, the system is evaluated concerning operation costs. Three Ethereum test networks - Ganache, Ropsten, and Rinkeby - were used to run the smart contract and assess the charge to complete significant actions in the system. The results show that the cost to use the smart lock is low, especially if the benefits of security, privacy, and convenience are taken into consideration. Most of the actions yielded fees about cents of a US dollar, where the exception is a one-time payment of USD3.83 - worst-case scenario - to deploy the smart contract - i.e., setup the system.
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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".