106,560 research outputs found
Semantic-based policy engineering for autonomic systems
This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise
A Survey on Semantic Communications for Intelligent Wireless Networks
With deployment of 6G technology, it is envisioned that competitive edge of
wireless networks will be sustained and next decade's communication
requirements will be stratified. Also 6G will aim to aid development of a human
society which is ubiquitous and mobile, simultaneously providing solutions to
key challenges such as, coverage, capacity, etc. In addition, 6G will focus on
providing intelligent use-cases and applications using higher data-rates over
mill-meter waves and Tera-Hertz frequency. However, at higher frequencies
multiple non-desired phenomena such as atmospheric absorption, blocking, etc.,
occur which create a bottleneck owing to resource (spectrum and energy)
scarcity. Hence, following same trend of making efforts towards reproducing at
receiver, exact information which was sent by transmitter, will result in a
never ending need for higher bandwidth. A possible solution to such a challenge
lies in semantic communications which focuses on meaning (context) of received
data as opposed to only reproducing correct transmitted data. This in turn will
require less bandwidth, and will reduce bottleneck due to various undesired
phenomenon. In this respect, current article presents a detailed survey on
recent technological trends in regard to semantic communications for
intelligent wireless networks. We focus on semantic communications architecture
including model, and source and channel coding. Next, we detail cross-layer
interaction, and various goal-oriented communication applications. We also
present overall semantic communications trends in detail, and identify
challenges which need timely solutions before practical implementation of
semantic communications within 6G wireless technology. Our survey article is an
attempt to significantly contribute towards initiating future research
directions in area of semantic communications for intelligent 6G wireless
networks
Capturing stance dynamics in social media: open challenges and research directions
Social media platforms provide a goldmine for mining public opinion on issues
of wide societal interest and impact. Opinion mining is a problem that can be
operationalised by capturing and aggregating the stance of individual social
media posts as supporting, opposing or being neutral towards the issue at hand.
While most prior work in stance detection has investigated datasets that cover
short periods of time, interest in investigating longitudinal datasets has
recently increased. Evolving dynamics in linguistic and behavioural patterns
observed in new data require adapting stance detection systems to deal with the
changes. In this survey paper, we investigate the intersection between
computational linguistics and the temporal evolution of human communication in
digital media. We perform a critical review of emerging research considering
dynamics, exploring different semantic and pragmatic factors that impact
linguistic data in general, and stance in particular. We further discuss
current directions in capturing stance dynamics in social media. We discuss the
challenges encountered when dealing with stance dynamics, identify open
challenges and discuss future directions in three key dimensions: utterance,
context and influence
Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication
It is anticipated that 6G wireless networks will accelerate the convergence
of the physical and cyber worlds and enable a paradigm-shift in the way we
deploy and exploit communication networks. Machine learning, in particular deep
learning (DL), is expected to be one of the key technological enablers of 6G by
offering a new paradigm for the design and optimization of networks with a high
level of intelligence. In this article, we introduce an emerging DL
architecture, known as the transformer, and discuss its potential impact on 6G
network design. We first discuss the differences between the transformer and
classical DL architectures, and emphasize the transformer's self-attention
mechanism and strong representation capabilities, which make it particularly
appealing for tackling various challenges in wireless network design.
Specifically, we propose transformer-based solutions for various massive
multiple-input multiple-output (MIMO) and semantic communication problems, and
show their superiority compared to other architectures. Finally, we discuss key
challenges and open issues in transformer-based solutions, and identify future
research directions for their deployment in intelligent 6G networks.Comment: 9 pages, 6 figures. The current version has been accepted by IEEE
Wireless Communications Magzin
Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study
Recognizing the tremendous improvements that the integration of generative AI
can bring to intelligent transportation systems, this article explores the
integration of generative AI technologies in vehicular networks, focusing on
their potential applications and challenges. Generative AI, with its
capabilities of generating realistic data and facilitating advanced
decision-making processes, enhances various applications when combined with
vehicular networks, such as navigation optimization, traffic prediction, data
generation, and evaluation. Despite these promising applications, the
integration of generative AI with vehicular networks faces several challenges,
such as real-time data processing and decision-making, adapting to dynamic and
unpredictable environments, as well as privacy and security concerns. To
address these challenges, we propose a multi-modality semantic-aware framework
to enhance the service quality of generative AI. By leveraging multi-modal and
semantic communication technologies, the framework enables the use of text and
image data for creating multi-modal content, providing more reliable guidance
to receiving vehicles and ultimately improving system usability and efficiency.
To further improve the reliability and efficiency of information transmission
and reconstruction within the framework, taking generative AI-enabled
vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning
(DRL)-based approach is proposed for resource allocation. Finally, we discuss
potential research directions and anticipated advancements in the field of
generative AI-enabled vehicular networks.Comment: 8 pages, 4 figure
Federated Embedded Systems â a review of the literature in related fields
This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous
computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways
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