6,351 research outputs found
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
Edge AI for Internet of Energy: Challenges and Perspectives
The digital landscape of the Internet of Energy (IoE) is on the brink of a
revolutionary transformation with the integration of edge Artificial
Intelligence (AI). This comprehensive review elucidates the promise and
potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a
meticulously curated research methodology, the article delves into the myriad
of edge AI techniques specifically tailored for IoE. The myriad benefits,
spanning from reduced latency and real-time analytics to the pivotal aspects of
information security, scalability, and cost-efficiency, underscore the
indispensability of edge AI in modern IoE frameworks. As the narrative
progresses, readers are acquainted with pragmatic applications and techniques,
highlighting on-device computation, secure private inference methods, and the
avant-garde paradigms of AI training on the edge. A critical analysis follows,
offering a deep dive into the present challenges including security concerns,
computational hurdles, and standardization issues. However, as the horizon of
technology ever expands, the review culminates in a forward-looking
perspective, envisaging the future symbiosis of 5G networks, federated edge AI,
deep reinforcement learning, and more, painting a vibrant panorama of what the
future beholds. For anyone vested in the domains of IoE and AI, this review
offers both a foundation and a visionary lens, bridging the present realities
with future possibilities
Building the Hyperconnected Society- Internet of Things Research and Innovation Value Chains, Ecosystems and Markets
This book aims to provide a broad overview of various topics of Internet of Things (IoT), ranging from research, innovation and development priorities to enabling technologies, nanoelectronics, cyber-physical systems, architecture, interoperability and industrial applications. All this is happening in a global context, building towards intelligent, interconnected decision making as an essential driver for new growth and co-competition across a wider set of markets. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from research to technological innovation, validation and deployment.The book builds on the ideas put forward by the European Research Cluster on the Internet of Things Strategic Research and Innovation Agenda, and presents global views and state of the art results on the challenges facing the research, innovation, development and deployment of IoT in future years. The concept of IoT could disrupt consumer and industrial product markets generating new revenues and serving as a growth driver for semiconductor, networking equipment, and service provider end-markets globally. This will create new application and product end-markets, change the value chain of companies that creates the IoT technology and deploy it in various end sectors, while impacting the business models of semiconductor, software, device, communication and service provider stakeholders. The proliferation of intelligent devices at the edge of the network with the introduction of embedded software and app-driven hardware into manufactured devices, and the ability, through embedded software/hardware developments, to monetize those device functions and features by offering novel solutions, could generate completely new types of revenue streams. Intelligent and IoT devices leverage software, software licensing, entitlement management, and Internet connectivity in ways that address many of the societal challenges that we will face in the next decade
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
With the advent of the IoT, AI, ML, and DL algorithms, the landscape of
data-driven medical applications has emerged as a promising avenue for
designing robust and scalable diagnostic and prognostic models from medical
data. This has gained a lot of attention from both academia and industry,
leading to significant improvements in healthcare quality. However, the
adoption of AI-driven medical applications still faces tough challenges,
including meeting security, privacy, and quality of service (QoS) standards.
Recent developments in \ac{FL} have made it possible to train complex
machine-learned models in a distributed manner and have become an active
research domain, particularly processing the medical data at the edge of the
network in a decentralized way to preserve privacy and address security
concerns. To this end, in this paper, we explore the present and future of FL
technology in medical applications where data sharing is a significant
challenge. We delve into the current research trends and their outcomes,
unravelling the complexities of designing reliable and scalable \ac{FL} models.
Our paper outlines the fundamental statistical issues in FL, tackles
device-related problems, addresses security challenges, and navigates the
complexity of privacy concerns, all while highlighting its transformative
potential in the medical field. Our study primarily focuses on medical
applications of \ac{FL}, particularly in the context of global cancer
diagnosis. We highlight the potential of FL to enable computer-aided diagnosis
tools that address this challenge with greater effectiveness than traditional
data-driven methods. We hope that this comprehensive review will serve as a
checkpoint for the field, summarizing the current state-of-the-art and
identifying open problems and future research directions.Comment: Accepted at IEEE Internet of Things Journa
- …