Research Area:  Edge Computing
The mobile crowd sensing (MCS) applications leverage the user data to derive useful information by data-driven evaluation of innovative user contexts and gathering of in-formation at a high data rate. Such access to context-rich data can potentially enable computationally intensive crowd-sourcing applications such as tracking a missing per-son or capturing a highlight video of an event. Using snippets and pictures captured from multiple mobile phone cameras with specific contexts can improve the data acquired in such applications.
This thesis proposes Context Aide an end-end architecture for data-driven distributed applications aware of human mobile interactions using Edge computing.Edge processing supports real-time applications by reducing communication costs.The goal is to optimize the quality and the cost of acquiring the data using a) modeling and prediction of mobile user contexts, b) efficient strategies of scheduling application tasks on heterogeneous devices including multi-core devices such as GPU c)power-aware scheduling of virtual machine (VM) applications in cloud infrastructure e.g. elastic VMs.
Name of the Researcher:  Madhurima Pore
Name of the Supervisor(s):  Sandeep Gupta
Year of Completion:  2019
University:  Arizona State University
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