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PhD Final Examination – Sherif Abdelwahab


Friday, August 4, 2017 10:00 AM - 12:00 PM

Architectures and Algorithms for Dynamic Overlay Networks
Most of the today’s Internet of Things (IoT) applications assume that data will be moved off devices into centralized cloud platforms. While existing IoT systems leverage cloud-based analytics for meaningful data reasoning, the assumption that data should always be moved off the devices is problematic. The amount of data to be moved from devices over Internet gateways to cloud platforms is huge which potentially make it cost inefficient. In other scenarios, privacy concerns of customers or organizational rules complicate the process of transferring data to third-party data centers.

This dissertation proposes architectures and dynamic overlay network algorithms for in-network and edge processing of data offered by the globally available IoT devices and provides a global platform for meaningful and responsive data analysis and decision making. The proposed techniques shift IoT analytics from a “collect data now and analyze it later” scenario to directly providing meaningful information from the in-network processing of devices data at or near the devices. The techniques serve future IoT use cases including distributed context awareness, on-demand data analysis, and in-network decision making. The dissertation comprises three main components.

The first component is a device management protocol for cloning devices’ data in proximate Edge Computing platforms. Unlike existing application-layer IoT management protocols the proposed protocol uses the LTE/LTE-A radio frame structure, device-to-device communication, and IoT data properties to avoid excessive network access latency in existing technologies.

The second component realizes distributed IoT analytics as overlay networks of devices clones. By means of virtual network embedding, it selects and interconnects devices’ clones to efficiently realize applications’ virtual topologies to achieve goals such as minimum latency, minimum infrastructure cost, or maximum infrastructure utilization.

Finally, the dissertation presents a communication middleware that allows autonomous discovery, self-deployment, and online migration of devices’ clones across heterogeneous Edge computing platforms. The middleware ensures that communication latency between clones is kept minimum despite the uncontrolled variability of the network and hosting platforms conditions.

We evaluate the proposed architectures and algorithms through simulations and prototype implementation of various components in controlled testbed environments, which we evaluate using real user applications. We explore the feasibility of the proposed techniques from both theoretical and practical perspectives.

Major Advisor: Bechir Hamdaoui
Committee: Ben Lee
Committee: Amir Nayyeri
Committee: Attila Yavuz
Committee: Belinda Batten
GCR: Maggie Niess


Kelley Engineering Center (campus map)
1007
Calvin Hughes
1 541 737 3168
Calvin.Hughes at oregonstate.edu
Sch Elect Engr/Comp Sci
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