Resource Location for Opportunistic Networks

Poersch, Andre Marques; Macedo, Daniel F.; Nogueira, José Marcos S.

doi:10.1109/ntms.2012.6208762

Cited by 3 publications

(1 citation statement)

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“…A new resource location algorithm has been proposed in [50] where the individuality of OppNets is taken into account by employing the caching of resource metadata and proactive resource announcements to improve the discovery rate. Any kind of supply, aid, function, or task can be physical resources and the nodes periodically advertise themselves and their resources.…”

Section: Buffer Management Techniquementioning

confidence: 99%

Routing Protocols in Opportunistic Networks – A Survey

Alajeely

Doss

Ahmad

2017

IETE Technical Review

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Opportunistic networks (OppNets) refer to a number of wireless nodes opportunistically communicating with each other in a form of "Store-Carry-Forward". This occurs when they come into contact with each other without a proper network infrastructure. OppNets are designed to operate in an environment characterized by high delay, high error rate, intermittent connectivity, and non-availability of end-to-end route between the source and destination. OppNets use wireless technologies, such as IEEE 802.11, WiMAX, Bluetooth, and other short-range radio communication, and grow from a single node (seed) to become large networks by inviting new nodes (helpers) to join the network. Nodes have the ability to store and carry data and also forward it to other nodes in order to achieve different tasks. In OppNets, there is no end-to-end connection between the source and the destination nodes. Further, due to their inherent features, OppNets suffer from frequent partitions and long delays, while also being subject to serious security challenges. This survey includes an overview of the available OppNets routing protocols, their classification, and an evaluation of six routing protocols (Epidemic routing, PRoPHET, MaxProp, Spray and Wait, Direct Delivery, and First Contact) in terms of complexity/robustness and scalability. Detailed simulation results show that as the load on the network increases, the performance of protocols decrease in terms of delivery delay and network overhead. As for scalability, simulation results show that Epidemic routing and PRoPHET achieved high delivery rates, but with a very high network overhead. MaxProp and Spray and Wait achieved lower delivery rates, but with a low network overhead. First Contact and Direct Delivery achieved low delivery rates with high delivery delays. Results vary depending on the buffer size, contact times, and speed. The results indicate that trams have the capacity to carry and exchange information faster, and improve connectivity in OppNets.

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Section: Buffer Management Techniquementioning

confidence: 99%