Heterogeneous Cluster-Based Information-Centric Sensor Networks With User Security Satisfaction
Bibliographic record
Abstract
In heterogeneous cluster-based information-centric wireless sensor networks (ICWSNs), sensor nodes acquire different application-specific data. They are clustered based on proximity, where cluster heads (CHs) act as cache nodes. Meanwhile, grouping sensor nodes based on the information type gathered can improve the ICWSN performance. Motivated by the heterogeneous nature of ICWSNs, application-specific communities can be formed within each cluster, where community leaders (CLs) can be selected to cache application-specific data. In this case, CHs gather aggregated data from CLs rather than basic sensing nodes. However, this creates an issue of the energy–latency and security tradeoff and affects user security satisfaction. In this work, we propose solving this issue by studying cluster-based ICWSNs with heterogeneous communities and comparing them to conventional heterogeneous cluster-based ICWSNs. Based on the formulated analytical model, we then propose SLAC-H, a security-level-aware CHs’ and CLs’ selection algorithm for cluster-based ICWSNs with heterogeneous communities. SLAC-H addresses the energy–latency and security issue by optimizing energy and coverage supported by sensor nodes in a cluster-based ICWSN with heterogeneous communities subject to security constraints. Simulation results show that compared to existing works, SLAC-H achieves lower latency and energy consumption while fulfilling higher user security satisfaction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".