Applications that rely on the most modern sensing devices and technologies and combine complex artificial intelligence tasks are now mainstream. It is sufficient to say, “OK-Google/Alexa/Siri switch on the heating system when the temperature is below 18° C” to appreciate the power of the IoT in combination with an Artificial Intelligence engine. However, the typical approach to enable intelligent applications is cloud-centric, meaning that the intelligence (a home assistant) is hosted in the cloud infrastructure, and the sensor data collected by some IoT devices (a microphone array and a temperature sensor) flow from the cyber-physical-system until reaching a remote endpoint to be processed. Finally, the correct command is transmitted to the IoT actuator (a radiator thermostat). Alternative approaches to this are possible, for instance, by considering a more dynamic and configurable intermediate layer placed between the IoT and the Cloud sides, usually dubbed as the Edge layer. Generally, a configurable edge layer reduces the required bandwidth and latency and improves users’ privacy. Moreover, if portions of the application intelligence could be hosted in this layer, the IoT device lifetime would be enlarged. However, reconfiguring and deploying an end-to-end processing flow that involves the three aforementioned architectural layers poses major challenges. Select a more efficient detection algorithm from a rich machine learning algorithms library and pushing the “deploy” button of an application dashboard to see the selected algorithm up and running more effectively (according to a given metric) on my smart home devices is still a dream, in most of the cases. Moreover, depending on the hardware capabilities, the application requirements in terms of bandwidth and latency, and the accuracy required for the machine learning task to execute, different end-to-end configurations are possible, all sub-optimal and possibly non-dominated in the Pareto meaning. The subject of this Ph.D. is to investigate and propose novel optimization and assessment methodologies to efficiently sample such a complex design space in target application sectors such as home, industry, manufacturing, farming, etc. The reference technological environment covers (but is not limited to) embedded device software engineering (micropython, mbed OS, C languages and dialects, etc.), machine learning frameworks deployable on tiny devices (tinyML, TensorFlow lite, etc.), edge-based frameworks (eclipse Kura, edgeX Fundry, etc.) and cloud-based IoT platforms and services with AI support.
