Name
Abstract | ||
|---|---|---|
In a Delay Tolerant Network (DTN), the nodes have intermittent connectivity and complete path(s) between the source and destination may not exist. The communication takes place opportunistically when any two nodes enter the effective range. One of the major challenges in DTNs is message forwarding when a sender must select a best neighbor that has the highest probability of forwarding the message to the actual destination. However, finding an appropriate route remains an NP-hard problem. This paper presents a concept of Checkpoint (CP) based message forwarding in DTNs. The CPs are autonomous high-end wireless devices with large buffer storage and are responsible for temporarily storing the messages to be forwarded. The CPs are deployed at various places within the city parameter that are covered by bus routes and where human meeting frequencies are higher. For the simulative analysis a synthetic human mobility model in ONE simulator is constructed for the city of Fargo, ND, USA. The model is tested over various DTN routing protocols and the results indicate that using CP overlay over the existing DTN architecture significantly decreases message delivery time as well as buffer usage. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.7148/2012-0512-0518 | PROCEEDINGS 26TH EUROPEAN CONFERENCE ON MODELLING AND SIMULATION ECMS 2012 |
Keywords | Field | DocType |
Mobility, Routing, DTN | Message forwarding,Wireless,Delay-tolerant networking,Computer science,Communication source,Computer network,Mobility model,Message delivery,Overlay,Routing protocol,Distributed computing | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Osman Khalid | 1 | 120 | 10.08 |
| Samee Ullah Khan | 2 | 1605 | 81.01 |
| Joanna Kolodziej | 3 | 920 | 55.57 |
| Limin Zhang | 4 | 226 | 8.35 |
| Juan Li | 5 | 2 | 2.73 |
| Khizar Hayat | 6 | 248 | 19.71 |
| Sajjad Ahmad Madani | 7 | 409 | 26.21 |
| Lizhe Wang | 8 | 2973 | 191.46 |
| Dan Chen | 9 | 1096 | 59.02 |