Doctoral Dissertation Oral Proposal, Jiachen Wang

Title: Towards Efficient Resource Partitioning in Real-Time Wireless Networks

Ph.D. Candidate: Jiachen Wang
Major Advisor: Dr. Song Han
Associate Advisors: Dr. Shengli Zhou, Dr. Bing Wang
Committee Members: Dr. Suining He, Dr. Zhijie Jerry Shi
Date/Time: Monday, March 25th, 2024, 12:00 pm
Location: HBL1947 room

Meeting link:
https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m862a29d2cb216d5efd3bf6e24d2f39bc
Meeting number: 2634 012 9422
Password: JQai2GbcT26

Abstract

The Internet of Things (IoT) has emerged as a transformative force in modern technology, enabling connectivity across everyday objects and significantly impacting various industries through the Industrial Internet of Things (IIoT). IIoT integrates industrial assets with information systems and business processes, streamlining manufacturing and optimizing industrial operations. Recent years have seen a shift towards real-time wireless networking (RTWN) technologies due to their deployment ease, mobility, and lower maintenance costs. Technologies such as WirelessHART, ISA100.11a, and 6TiSCH have become mature and are widely implemented in industrial automation, supporting critical sensing and control applications.

Despite the widespread adoption of RTWN, challenges persist, particularly in network management. Current RTWN systems often struggle to balance network efficiency with real-time performance. Additionally, the dynamic nature of industrial networks further complicates management, necessitating prompt and adaptive reconfiguration to maintain reliable timing guarantees.

To address these challenges, we propose a series of partition-based resource management strategies for RTWN. The first approach focuses on minimizing end-to-end latency by creating adaptive time-dimension resource partitions, which aligns with different layers of the network routing graph and dynamically adjust to topology changes. The second approach introduces a hierarchical resource partition framework, enhancing resource allocation efficiency and reducing reconfiguration overhead by constructing partitions subtree-by-subtree and limiting adjustments to nodes within these subtrees. Finally, to support multiple applications with diverse requirements on a shared RTWN infrastructure, we propose a resource virtualization framework. This framework allocates specific resources to each application, ensuring conflict resolution and isolation, thus maintaining seamless operation across applications. To further improve the applicability of the proposed real-time network virtualization framework on dynamic environments, we are currently working on a flexibility-aware resource partitioning framework. By formally defining a quantifiable metric that reflects a partition’s ability to support flows with varying timing requirements, we construct partitions with predictable performance upon dynamics and maximize the availability.