Related papers: U-TOE: Universal TinyML On-board Evaluation Toolki…
Models are used in both Software Engineering (SE) and Artificial Intelligence (AI). SE models may specify the architecture at different levels of abstraction and for addressing different concerns at various stages of the software…
In recent years we have witnessed a boom in Internet of Things (IoT) device deployments, which has resulted in big data and demand for low-latency communication. This shift in the demand for infrastructure is also enabling real-time…
The implementation, deployment and testing of secure services for Internet of Things devices is nowadays still at an early stage. Several frameworks have recently emerged to help developers realize such services, abstracting the complexity…
Prototyping is an important part in research and development of tangible user interfaces (TUIs). On the way from the idea to a working prototype, new hardware prototypes usually have to be crafted repeatedly in numerous iterations. This…
There has been increasing interest in deploying IoT devices to study human behaviour in locations such as homes and offices. Such devices can be deployed in a laboratory or `in the wild' in natural environments. The latter allows one to…
For Internet of Things (IoT) systems operating in areas with limited network connectivity, reliable and safe functionality must be ensured. This can be done using special test cases which are examining system behavior in cases of network…
Energy-constrained sensor nodes can adaptively optimize their energy consumption if a continuous measurement exists. This is of particular importance in scenarios of high dynamics such as energy harvesting or adaptive task scheduling.…
The availability of pre-trained models (PTMs) has enabled faster deployment of machine learning across applications by reducing the need for extensive training. Techniques like quantization and distillation have further expanded PTM…
While there exist many ways to deploy machine learning models on microcontrollers, it is non-trivial to choose the optimal combination of frameworks and targets for a given application. Thus, automating the end-to-end benchmarking flow is…
Batteryless IoT systems face energy constraints exacerbated by checkpointing overhead. Approximate computing offers solutions but demands manual expertise, limiting scalability. This paper presents CheckMate, an automated framework…
Deploying machine learning applications on edge devices can bring clear benefits such as improved reliability, latency and privacy but it also introduces its own set of challenges. Most works focus on the limited computational resources of…
This doctoral dissertation proposes a novel approach to enhance the development of smart services for the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS). The proposed approach offers abstraction and automation to the…
Tiny Machine Learning (TinyML) is an upsurging research field that proposes to democratize the use of Machine Learning and Deep Learning on highly energy-efficient frugal Microcontroller Units. Considering the general assumption that TinyML…
Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML…
Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique security challenges. These devices, restricted by RAM and CPU…
Recent advances in state-of-the-art ultra-low power embedded devices for machine learning (ML) have permitted a new class of products whose key features enable ML capabilities on microcontrollers with less than 1 mW power consumption…
This letter introduces an energy-efficient pull-based data collection framework for Internet of Things (IoT) devices that use Tiny Machine Learning (TinyML) to interpret data queries. A TinyML model is transmitted from the edge server to…
The Internet of Things (IoT) has become integral to modern technology, enhancing daily life and industrial processes through seamless connectivity. However, the rapid expansion of IoT systems presents significant sustainability challenges,…
Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart…
Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics…