NDLI: High Performance Wireless Sensor-Actuator Networks for Industrial Internet of Things

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High Performance Wireless Sensor-Actuator Networks for Industrial Internet of Things

Content Provider	Semantic Scholar
Author	Gunatilaka, Dolvara
Copyright Year	2019
Abstract	OF THE DISSERTATION High Performance Wireless Sensor-Actuator Networks for Industrial Internet of Things by Dolvara Gunatilaka Doctor of Philosophy in Computer Science Washington University in St. Louis, 2019 Professor Chenyang Lu, Chair Wireless Sensor-Actuator Networks (WSANs) enable cost-effective communication for Industrial Internet of Things (IIoT). To achieve predictability and reliability demanded by industrial applications, industrial wireless standards (e.g., WirelessHART) incorporate a set of unique features such as a centralized management architecture, Time Slotted Channel Hopping (TSCH), and conservative channel selection. However, those features also incur significant degradation in performance, efficiency, and agility. To overcome these key limitations of existing industrial wireless technologies, this thesis work develops and empirically evaluates a suite of novel network protocols and algorithms. The primary contributions of this thesis are four-fold. (1) We first build an experimental testbed realizing key features of the WirelessHART protocol stack, and perform a series of empirical studies to uncover the limitations and potential improvements of existing network features. (2) We then investigate the impacts of the industrial WSAN protocol’s channel selection mechanism on routing and real-time performance, and present new channel and link selection strategies that improve route diversity and real-time performance. (3) To further enhance performance, we propose and design conservative channel reuse, a novel approach to support concurrent transmissions in a same wireless channel while maintaining a high
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=1489&context=eng_etds
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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