Abstract Machine learning has become an indispensable part of the existing technological
domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to …

Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

The TinyML paradigm proposes to integrate Machine Learning (ML)-based mechanisms
within small objects powered by Microcontroller Units (MCUs). This paves the way for the …

The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …

Edge computing is an emerging paradigm that offloads the computations and analytics
workloads onto the Internet of Things (IoT) edge devices to accelerate the computation …

The rapid emergence of low-power embedded devices and modern machine learning (ML)
algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks …

The internet of things (IoT) has a significant economic and environmental impact owing to
the billions or trillions of interconnected devices that use various types of sensors to …

Embedding Artificial Intelligence onto low-power devices is a challenging task that has been
partly overcome with recent advances in machine learning and hardware design. Presently …

The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …