Optimising the power using firework‐based evolutionary algorithms for emerging IoT applications
Bibliographic record
Abstract
Optimising the overall power in a cluster‐assisted internet of things (IoT) network is a challenging problem for emerging IoT applications. In this study, the authors propose a mathematical model for the cluster‐assisted IoT network. The cluster‐assisted IoT network consists of three types of nodes: IoT nodes, core cluster nodes (CCNs) and base stations (BSs). The objective is to minimise transmission, between IoT nodes (IoTs)–CCNs and CCNs–BSs, and computational power (at CCNs), while satisfying the requirements of communicating nodes. The formulated mathematical model is a integer programming problem. They propose three swarm intelligence‐based evolutionary algorithms: (i) a discrete fireworks algorithm (DFWA), (ii) a load‐aware DFWA (L‐DFWA), and (iii) a hybrid of the L‐DFWA and the low‐complexity biogeography‐based optimisation algorithm to solve the optimisation problem. The proposed algorithms are population‐based metaheuristic algorithms. They perform extensive simulations and statistical tests to show the performance of the proposed algorithms when compared with the existing ones.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".