Rajaei, Aydin.pdf (30.46 MB)
Efficient and flexible geocasting for opportunistic networks
thesis
posted on 2023-06-09, 04:34 authored by Aydin RajaeiWith the proliferation of smartphones and their advanced connectivity capabilities, opportunistic networks have gained a lot of traction during the past years; they are suitable for increasing network capacity and sharing ephemeral, localised content. They can also offload traffic from cellular networks to device-to-device ones, when cellular networks are heavily stressed. Opportunistic networks can play a crucial role in communication scenarios where the network infrastructure is inaccessible due to natural disasters, large scale terrorist attacks or government censorship. Geocasting, where messages are destined to specific locations (casts) instead of explicitly identified devices, has a large potential in real world opportunistic networks, however it has attracted little attention in the context of opportunistic networking. In this thesis, we propose Geocasting Spray And Flood (GSAF), a simple but efficient and flexible geocasting protocol for opportunistic, delay tolerant networks. GSAF follows a simple but elegant and flexible approach where messages take random walks towards the destination cast. Messages that follow directions away from the cast are extinct when the device buffer gets full, freeing space for new messages to be delivered. In GSAF, casts do not have to be pre-defined; instead users can route messages to arbitrarily defined casts. Also, the addressed cast is flexible in comparison to other approaches and can take complex shapes in the network. DA-GSAF as the direction aware version of the GSAF is proposed as well which use location information to aid routing decisions in the GSAF. Extensive evaluation shows that GSAF and DA-GSAF are significantly more efficient than existing solutions, in terms of message delivery ratio and latency as well as network overhead.
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- Published version
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127.0Department affiliated with
- Informatics Theses
Qualification level
- doctoral
Qualification name
- phd
Language
- eng
Institution
University of SussexFull text available
- Yes
Legacy Posted Date
2017-01-05Usage metrics
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