Performance of Mobile Agent Based Network Topology Discovery
Bibliographic record
Abstract
Topology discovery in both wired and wireless networks has been receiving increasing attention in the research community as well as the industry. Topology discovery is quite a time consuming task if it is done manually in a large network. It is more difficult in networks where the topology changes very frequently such as mobile ad hoc networks (MANET). MANET networks are complex and distributed system comprising of wireless mobile nodes that can move around freely, dynamically self-organize into arbitrary and temporary network topologies [14]. Currently there are a number of tools available that allows network administrators to discover the network nodes automatically. However most of these tools either use PING, trace route or SNMP queries. In addition, a lot of these tools also require a range of IP addresses that can be pinged to discover the network. However in such approaches, nodes in mobile ad hoc network can consume considerable amount of power and bandwidth in transmitting and receiving data for topology discovery. We present a mobile agent based topology discovery framework that is distributed, sustainable and less resource intensive. This paper outlines our experiences of implementation of a mobile agent based topology discovery framework. We also present a comparison of performance between our framework and the more common "ANT" based topology discovery frameworks.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".