Smart buildings are one of the most important emerging applications of Internet of Things (IoT). The astronomical growth in IoT devices, data generated from these devices and ubiquitous connectivity have given rise to a new computing paradigm, referred to as "Edge computing", which argues for data analysis to be performed at the "edge" of the IoT infrastructure, near the data source. The development of efficient Edge computing systems must be based on advanced understanding of performance benefits that Edge computing can offer. The goal of this work is to develop this understanding by examining the end-to-end latency and throughput performance characteristics of Smart building streaming IoT applications when deployed at the resource-constrained infrastructure Edge and to compare it against the performance that can be achieved by utilizing Cloud's data-center resources. This work also presents a real-time streaming application to detect and localize the footstep impacts generated by a building's occupant while walking. We characterize this application's performance for Edge and Cloud computing and utilize a hybrid scheme that (1) offers maximum of around 60% and 65% reduced latency compared to Edge and Cloud respectively for similar throughput performance and (2) enables processing of higher ingestion rates by eliminating network bottleneck. / Master of Science / Among the various emerging applications of Internet of Things (IoT) are Smart buildings, that allow us to monitor and manipulate various operating parameters of a building by instrumenting it with sensor and actuator devices (Things). These devices operate continuously and generate unbounded streams of data that needs to be processed at low latency. This data, until recently, has been processed by the IoT applications deployed in the Cloud at the cost of high network latency of accessing Cloud’s resources. However, the increasing availability of IoT devices, ubiquitous connectivity, and exponential growth in the volume of IoT data has given rise to a new computing paradigm, referred to as “Edge computing”. Edge computing argues that IoT data should be analyzed near its source (at the network’s Edge) in order to eliminate high latency of accessing Cloud for data processing. In order to develop efficient Edge computing systems, an in-depth understanding of the trade-offs involved in Edge and Cloud computing paradigms is required. In this work, we seek to understand these trade-offs and the potential benefits of Edge computing. We examine end to-end latency and throughput performance characteristics of Smart building streaming IoT applications by deploying them at the resource-constrained Edge and compare it against the performance that can be achieved by Cloud deployment. We also present a real-time streaming application to detect and localize the footstep impacts generated by a building’s occupant while walking. We characterize this application’s performance for Edge and Cloud computing and utilize a hybrid scheme that (1) offers maximum of around 60% and 65% reduced latency compared to Edge and Cloud respectively for similar throughput performance and (2) enables processing of higher ingestion rates by eliminating network bottleneck.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/88438 |
| Date | 13 March 2019 |
| Creators | Ghaffar, Talha |
| Contributors | Computer Science, Lee, Dongyoon, Jung, Changhee, Butt, Ali R. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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