QoS and Energy-Aware Routing for Wireless Sensor Networks
Bibliographic record
Abstract
Quality of service (QoS) and energy awareness are key requirements for wireless sensor networks (WSNs), which entail considerable challenges due to constraints in network resources, such as energy, memory capacity, computation capability, and maximum data rate. Guaranteeing QoS becomes more and more challenging as the complexity of WSNs increases. This chapter firstly discusses challenges and existing solutions for providing QoS and energy awareness in WSNs. Then, a novel bio-inspired QoS and energy-aware routing algorithm is presented. Based on an ant colony optimization idea, it meets QoS requirements in an energy-aware fashion and, at the same time, balances the node energy utilization to maximize the network lifetime. Extensive simulation results under a variety of scenarios demonstrate the superior performance of the presented algorithm in terms of packet delivery rate, overhead, load balance, and delay, in comparison to a conventional directed diffusion routing algorithm.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".