Agent based approach towards energy aware routing in wireless sensor networks
Bibliographic record
Abstract
In an environment where node density is massive, placement is heterogeneous and lots of sensory traffic with redundancy is produced; individual nodes waste limited wireless bandwidth and consume lot of energy. This minimizes their life time on the network. To handle such problem number of routing schemes have been proposed by researchers. These schemes tend to find the minimum energy path to prolong the network life. In this paper, we argue that it is not always wise to use the minimumenergy path. Nodes along the optimal path will be used rapidly, burn out energy aggressively and eventually die hastily creating communication holes in network. This brings rapid change in the topology resulting in increased latency, poor connectivity and production of heterogeneous subnets. Thus, it is better to occasionally use suboptimal paths. We unmitigated our efforts to augment the node life time in sensor network by frequent use of suboptimal paths, and reducing redundant sensory network traffic. Towards this end, we propose an agentbased directed diffusion approach that incorporates static and mobile agents. Static agent is responsible for calculating and maintaining the set of optimal paths.Mobile agent main function is to perform data processing and make data aggregation decisions at nodes in the network rather than bring data back to a central processor (sink). This paper provides an analytical model for data dissemination for both approaches. To demonstrate the performance evaluation, a prototype of a simulator is implemented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".