Bibliographic record
Abstract
Current trend of increasing use of mobile devices such as PDA, Laptop, and intelligent cell phones etc., has given increased interest in wireless and mobile networks. The cost and complexity associated with infrastructure based wireless networking is leading researchers to investigate ad hoc networks. Managing ad hoc network is significantly different than managing infrastructred network. One of the major challenges in managing ad hoc network is discovery of the topology of the network on the fly. We have developed a mobile agent based system that allows for real time topology discovery of the network. As opposed to many such mobile agent based systems that utilize "ant" based algorithms, our system makes efficient use of the limited network resources of the ad hoc network by restricting the mobility of the agents. The mobile agent uses its mobility only to move to a new node after which it turns into a stationary agent. This paper provides an insight into designing a mobile agent based network topology discovery system using IBMcopy Aglets as well as presents our experience with an experimental approach rather than a more common simulation based approach. The paper also provides a comparison of IBM Aglets and JADE in the light of implementing a MAS for mobile ad hoc network
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".