Data-driven transfer optimizations for big data in the industrial internet of things [article]

Niklas Bernhard Semmler, Technische Universität Berlin, Anja Feldmann
In the last two decades, the Internet of Things (IoT) has grown from a mere vision to everyday reality. Its fundamental idea is that devices become interconnected with each other and digital services. The consumer side of the IoT, the Consumer Internet of Things (CIoTs), has become omnipresent in the form of wearables, virtual assistants, and smart home solutions. The industrial side of the IoT, the Industrial Internet of Things (IIoT), has received less attention from the general public. The
more » ... oT takes the shape of industrial-grade devices, from trucks to industrial robots, that are equipped with sensors and networking chipsets. It promises to reduce waste, increase machine lifespans, improve energy efficiency, and enable mass customization. The CIoT predominantly creates big data sparsely across wide areas, e.g., distributed over many households. CIoT applications collect and process this data in the cloud. In contrast, the IIoT predominantly creates big data at industrial facilities that are densely populated with devices. Because these industrial facilities are often connected to the cloud by low-bandwidth access networks, IIoT big data cannot be entirely transferred to the cloud. Simultaneously, industrial facilities are often equipped with limited computing resources. This creates a data-compute asymmetry where most data stays at resource-constrained industrial facilities, and only a fraction is transferred to the resource rich cloud. Unmitigated, the network bottleneck delays the installation of IIoT applications. This thesis introduces software solutions that reduce the impact of the network bottleneck. Systems processing IIoT big data face complexity from both the data sources and application requirements. On the one side, the data is generated by inherently hierarchical and distributed industrial processes and retains these qualities. On the other side, IIoT applications have diverse requirements on data access and processing (e.g., requiring database-like access to historic IIoT big data or processing [...]
doi:10.14279/depositonce-15529 fatcat:7aqxsi4yz5fstooj3n3nhu3j3u