Abstract
Opportunistic communication is an active research area in wireless sensor networks. Exploiting the opportunities to communicate between devices in an unstable network is one of the main challenges of the opportunistic communication. In this thesis, we propose an infrastructure-independent opportunistic mobile social networking strategy for efficient message broadcasting in campus environments. Specifically, we focus on the application scenario of university campuses. In our model, the students' smartphones forward messages to each other. The messages are created spontaneously as independent events in various places of the campus. The events can be either urgent security alerts or private announcements to the students who are currently on the campus. Our proposed state-based campus routing (SCR) protocol is based on the idle and active states of the students in indoor and outdoor environments. The proposed model is analyzed through extensive network simulations using mobility datasets collected from students on University of Milano, University of Cambridge and University of St Andrews campuses. The opportunistic network model and the SCR protocol is compared with epidemic, epidemic with TTS (Times-To-Send), PROPHET(Probabilistic Routing on History of Encounters), NDAO (Nodes Density Aware Opportunistic) and random routing protocols. We observe that the message delivery performance of SCR is close to Epidemic, PROPHET and NDAO while SCR reduces the amount of message transmissions.
Notes
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Graduation Date
2015
Semester
Fall
Advisor
Turgut, Damla
Degree
Master of Science (M.S.)
College
College of Engineering and Computer Science
Department
Computer Science
Degree Program
Computer Science
Format
application/pdf
Identifier
CFE0006258
URL
http://purl.fcla.edu/fcla/etd/CFE0006258
Language
English
Release Date
6-15-2017
Length of Campus-only Access
1 year
Access Status
Masters Thesis (Open Access)
STARS Citation
Bacanli, Salih Safa, "Opportunistic Networks in Campus Environments" (2015). Electronic Theses and Dissertations. 5154.
https://stars.library.ucf.edu/etd/5154