Enhancing The Design Of NextG For Critical And Massive IoT Devices And Applications
2024-02
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Enhancing The Design Of NextG For Critical And Massive IoT Devices And Applications
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2024-02
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The IoT world is evolving with the latest technology trends like edge computing, augmented & virtual reality, machine learning, robotics and 5G. But still there is less business productivity due to the slower technical adoption in the industrial automation system. There is a tremendous need for autonomous networks in the manufacturing industry to increase productivity and allow communication between people, devices and sensors. And there are massive numbers (hundreds to thousands) of IoT devices in a single factory depending on the scale of the industry. These factories consist of critical IoT devices like fire or gas sensors which need to operate reliably with less latency. But the existing wired and/or wireless networks are struggling to fulfill the computational and resources for operations demand of the emerging technologies. In order to address these needs of the industries with digital transformation happening in Industry 4.0, the evolution of private 5G and 5G standards opens bigger opportunities. In this thesis, we discuss the challenges and explore new opportunities of using 5G for critical/massive IoT devices. While exploring the possibilities, we uncover some of the challenges in IoT especially due to the vendor-locked IoT solutions and unavailability of large scale IoT devices for evaluating end-to-end systems. We discussed the challenges in detail and proposed novel solutions to overcome these challenges. First, as the plethora of Internet of Things (IoT) devices gradually make their way into our lives, several Cloud Service Providers (CSPs) have developed IoT gateway platforms (SDKs) that solely connect IoT devices to their respective cloud. Such gateways are cloud-centric. We study the state-of-the-art vendor-locked IoT Gateway solutions and approaches and propose an edge-centric paradigm through an evolutionary framework, dubbed VeerEdge for developing IoT gateways. We leverage computing and storage capabilities at the network edge for edge-based device & IoT service. Second, the IoT world is evolving with the latest technology trends like edge computing, augmented & virtual reality, machine learning, robotics and 5G. With the digital transformation happening in Industry 4.0, many industries are moving towards private 5G networks. There are a massive number (hundreds to thousands) of IoT devices in a single factory depending on the scale of the industry and these factories consist of critical IoT devices like fire or gas sensors which need to operate reliably with less latency. In order to efficiently realize the capabilities such as ultra reliable low latency communications (URLLC), enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC) offered by 5G, the next generation IoT devices/applications need a paradigm shift in their design and need to be evaluated under simulation using 5G networks before getting deployed in the real-world. However, many IoT simulators run in isolation and do not interface with real-world IoT cloud systems or support 5G networks. This isolation makes it difficult to design, develop and evaluate IoT applications/devices for industrial automation systems and for experiments to fully replicate the diversity that exists in end-to-end, real-world systems using 5G networks. Kaala 2.0 is the first scalable, hybrid, end-to-end IoT and NextG system simulator that can integrate with real-world IoT cloud services through simulated or real-world 5G networks. Kaala 2.0 is intended to bridge the gap between IoT simulation experiments and the real world using 5G networks. The simulator can interact with cloud IoT services, such as those offered by Amazon, Microsoft and Google. Depending on the configuration, Kaala 2.0 supports simulation of User Equipment (UE), 5G Radio Access Network (RAN) and 5G Core and at the same time supports real-world User Equipment (UE), 5G Radio Access Network (RAN) and 5G Core. Kaala 2.0 has the ability to simulate a large number of diverse IoT devices to evaluate mMTC, simulate events that may simultaneously affect several sensors to evaluate URLLC and finally simulate large amounts of data to evaluate eMBB. Third, we argue that existing 5G network architecture is too rigid to support many future applications with high bandwidth and low latency. This is in spite of the O-RAN vision to endow radio access networks (RAN) with agility and intelligence via RAN intelligent controllers (RICs). We posit that not all data is of equal utility to applications, and advocate an (application) semantics-aware, fine-grained, cross-layer and software-defined framework to re-architect next-generation (NextG) networks. We focus on the design of HyperRAN, an intelligent NextG RAN architecture that embeds application semantics across the RAN protocol stack to enable agile and intelligent decision making. At the core of HyperRAN is a declarative, programmable \Hypersch that takes into account application semantics, service requirements, user context as well as channel conditions for intelligent and adaptive radio resource scheduling. Finally, we advocate an eBPF (extended Berkeley Packet Filter)+XDP (eXpress Data Path) based framework for scaling and accelerating software packet processing in (O-RAN compliant) NextG RANs. Using 5G Central Unit User Plane (CU-UP) as a key case study, we present an initial design of our proposed framework, dubbed PRANAVAM, and its key components. We also discuss additional design and options for further improvements.
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University of Minnesota Ph.D. dissertation. February 2024. Major: Computer Science. Advisor: Zhi-Li Zhang. 1 computer file (PDF); xiii, 126 pages.
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Dayalan, Udhaya Kumar. (2024). Enhancing The Design Of NextG For Critical And Massive IoT Devices And Applications. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/262006.
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