Context-Aware Data Analytics Variability in IoT Neural Network-Based Systems

Emergent software applications are increasingly becoming (self-)adaptive and autonomous. Further, Internet of Things (IoT) applications increasingly involve data analytics. The introduction of neural networks in IoT systems has enabled a new generation of applications capable of performing complex sensing and actuation analysis tasks that were not previously possible with other approaches. A key component in the development of these systems is the ability to represent data analytics variability, which captures the ways in which the system can adapt in terms of the data analysis at design and run times. Although variability has been explored in the domain of software product lines (SPLs), data analytics variability in IoT neural network-based systems still seems to be poorly understood and needs to be investigated appropriately. In this paper, we introduce an approach to capture data analytics variability in IoT neural network-based systems (IoTNNSs). The approach represents several types of variability inherent in the development of these analytics systems, including those related to the application context, behavior, quality attributes, IoT devices, and neural networks.