Optimizing Residual Energy and Delay in WSN Routing using Particle Swarm Optimization
Bibliographic record
Abstract
For reliable use of wireless sensor networks, energy, and delay optimization are equally crucial.Packets must therefore be routed via the path with the least amount of delay and energy consumption possible.For both clustering and non-clustering WSN scenarios, this problem remains an exploratory challenge.The optimization problem is presented here as a multi-objective problem in the clustering and non-clustering WSN contexts.A new energy model is presented for Wireless Sensor Networks (WSN) that has two more components: switching between transmission and reception modes and using the CSMA/CA protocol for packet transfer.This optimization problem is solved in two working environments: clustering and non-clustering, using a stochastic optimization technique particle swarm optimization (PSO) that uses particles to explore the search space.The proposed PSO-based approach increases WSN lifetime by 45% over ACO and twice as much as GA when compared to Genetic Algorithm (GA) and Ant Colony Optimization (ACO).The result additionally demonstrates the WSN's delay-tolerant routing in two operational scenarios.The proposed routing framework offers the potential for prolonging the lifetime of WSNs in many real-time applications, including area monitoring, healthcare monitoring, habitat monitoring, and industrial monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".