Abstract
The number of IoT devices deployed in the environment for longer periods of time is growing rapidly. There is no universally agreed-upon standard for IoT devices, making quality assurance a concern. Many models for smart buildings and cities utilize IoT devices to optimize resource efficiency. In advanced smart environments, where IoT devices are in charge of vital components, the need for IoT devices to produce accurate data and remain functioning in the long term becomes even more critical.
This work proposes the Immune Inspired Phoenix Anamnesis (IIPA) model. The IIPA model can be seen as a model for a framework for failure monitoring and reproduction. In the model, a human immune-inspired process is proposed, which enables IoT devices to automatically and dynamically reproduce the necessary components in order to maintain an interconnected network of devices with minimal human intervention. In the IIPA model, anomalies within IoT devices are detected using a novel hybrid algorithm which incorporates aspects of K-means clustering and negative selection. The IIPA model facilitates the process of replacing the IoT device using a variety of production machinery, such as PCB printers and 3D printers, to produce the components for an IoT device. IoT devices are then assembled and installed by agents of the IIPA model for seamless management and integration of those replacements once they have been manufactured and assembled. The model is validated by way of proof of concept prototyping, the Phoenix prototype, followed by an analysis of the data produced by the implemented system.
Keywords: IoT device networks, Negative selection, K-means clustering, 3D printing