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Title page for ETD etd-03222015-024034

Type of Document Dissertation
Author An, Kyoungho
Author's Email Address kyoungho.an@gmail.com
URN etd-03222015-024034
Title Algorithms and Techniques for Scalable, Reliable Edge-to-Cloud Industrial Internet of Things
Degree PhD
Department Computer Science
Advisory Committee
Advisor Name Title
Aniruddha S. Gokhale Committee Chair
Christopher J. White Committee Member
Douglas C. Schmidt Committee Member
Janos Sztipanovits Committee Member
Sumant Tambe Committee Member
  • Cloud Computing
  • Publish/Subscribe
  • Middleware
  • Data Distribution Service
  • Discovery
  • Coordination
  • Distributed Systems
  • Industrial Internet of Things
Date of Defense 2015-03-18
Availability unrestricted
The Industrial Internet of Things (IIoT), which is a special class of Internet of Things (IoT), operates in large, distributed and dynamic environments comprising sensors all the way to large server clusters. IIoT is envisioned to support mission critical applications deployed in domains such as transportation, healthcare, manufacturing, and energy. Realizing the vision of IIoT requires scientific advances in the systems software for (a) the discovery and data dissemination between machines at the edge and the cloud, and (b) timely and reliable analytics conducted in the cloud for proactive maintenance and safety of the industrial systems that use IIoT. To address these requirements, this dissertation makes three contributions. First, it presents algorithms for a scalable discovery protocol as well as a coordination service in wide area network (WAN) environments. These algorithms are evaluated in the context of a standardized data-centric publish/subscribe messaging service called Object Management Group (OMG)’s Data Distribution Service (DDS). Second, it provides algorithms and a systems framework for highly available and real-time cloud infrastructures to satisfy the timeliness and reliability requirements of cloud-based data analytics. Finally, it provides a model-based testing automation framework for validating the performance of OMG DDS applications that must meet specific service levels through the use of different combinations of DDS quality of service (QoS) configurations.
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