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--- projects [2017/01/05 21:14] – roumani
+++ projects [2017/01/05 21:15] (current) – roumani
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 **Supervisors**: Professors Michael Jenkin and Patrick Dymond
-**Project*: How does changing the model of synchronization changing run time bounds on infection algorithms?
+**Project**: How does changing the model of synchronization changing run time bounds on infection algorithms?
 Infection algorithms are a class of algorithms within which individual agents exchange information via infection. That is, the algorithm proceeds by the various agents transmitting (infecting) each other with information. Under an assumption of synchronization — that is, a model in which no two agents can infect each other at precisely the same time — it is possible to derive models of expected time until all agents have been infected. But how does this algorithm adapt when agents can actually infect each other simultaneously?  This project will explore this problem. First, a simple simulation algorithm will be implemented to test infection rates when it is assumed that at a given time instant only one infection can occur. This algorithm will then be generalized to a model under which within a given time interval more than one infection can occur. Experimental validation will explore how existing infection algorithms perform under this more realistic model.  Simulations will be supported using a collection of real devices (Android devices) who can communicate with each other in the local environment using bluetooth and/or WIFI.