A Blockchain-Based Approach for Optimal and Secure Routing in Wireless Sensor Networks and IoT
Bibliographic record
Abstract
The traffic load balance, the interferences reduction, and the security during the routing phase in wireless sensor networks (WSN) and IoT are investigated in this paper. In our work, we suppose that the network's nodes are sensing some events which generate heavy data that must be carried over several packets. We propose a routing protocol that makes use of the Blockchain technology to offer a shared memory between the network's nodes. These nodes are considered as coins in which the ownership transacts between the source nodes and the sink. All the transactions are stored in the Blockchain as a means to share the network's status in real-time. In order to select the optimal path, we introduce a cost function which considers the load density and interferences level at each node. Furthermore, we are taking advantage of the Blockchain security to secure the selected paths in the network. The simulation results have shown that this solution could be applicable and could resolve the issues cited above.
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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".