A mobile agent based routing protocol for mobile ad hoc networks
Bibliographic record
Abstract
Ad-hoc networking is a concept in computer communications, which allows users $'anting to communicate with each other to form a temporary net- work, without any form of centralized administration or infrastructure.Each node participating in the network acts both as host and as router and must therefore be wiiling to fo¡ward packets for other nodes.For this purpose, a routing protocol is needed.The goal of this research is to design a flexible and efficient rout- ing scheme for a mobile ad hoc network (MANET).Because an MANET is composed of wireless mobile computing devices forming an ad-hoc network without existing v¡ired infrastructure or base stations, the network topol- ogy changes frequently.Hence, routing in such a dynamic environment is a challenging task.Some previous routing schemes in mobile ad hoc net- works include Dynamic Source Routing (DSR) [JM96], Cluster-Based Routing Protocol (CBRP) [KVCP97], and Temporally-Ordered Routing Protocol( TORA) [PC97], among others.In these routing schemes, some nodes may be loaded unnecessarily heavil¡ end-to-end delay may be high and these schemes are also hard to upgrade once they are in operation.To address these prob- lems, a new routing algorithm using mobile agents is presented.Mobile agents are software entities that can move freely between network nodes, and can exe- cute programs that they carry with them at whicheve¡ node they are currently running on.The approach proposed in this thesis is a demand-based routing algorithm that provides efficient routing at the application layer.This new proposed routing scheme has been implemented in Java using Aglets [LOKK97] .In addition, the routing scheme was also simulated on a mobile ad hoc network simulatorANtrJOS lSM01] to evaluate its perfor- mance.Ilt 5.11 5.12 ð. 1J 5.74 5.15 Average Number of Control Packets Average Number of Data Packets Percentage of Out-of-Order Data Packet Delivery End-to-end Delay for Completing 10 Data Packets Transmission Probability of Completing a Routing Successfully in MARP 90 91 91 93 94 95 96 96 97 5.16 Route Acquisition Time 5.17 Average Number of Control Packets, 5.18 Average Number of Data Packets.5.19 Percentage of Out-of-Order Data Packet Delivery.8.1 The implementation class of MARP Packet .1I2 8.2 The implementation class of Data Packet ....... 113 8.3 The implementation class of Acknowledgement Packet .113 B.4 The implementation class of link Failure Packet 114 xlll
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".