5,693 research outputs found
QUALITY-DRIVEN CROSS LAYER DESIGN FOR MULTIMEDIA SECURITY OVER RESOURCE CONSTRAINED WIRELESS SENSOR NETWORKS
The strong need for security guarantee, e.g., integrity and authenticity, as well as privacy and confidentiality in wireless multimedia services has driven the development of an emerging research area in low cost Wireless Multimedia Sensor Networks (WMSNs). Unfortunately, those conventional encryption and authentication techniques cannot be applied directly to WMSNs due to inborn challenges such as extremely limited energy, computing and bandwidth resources. This dissertation provides a quality-driven security design and resource allocation framework for WMSNs. The contribution of this dissertation bridges the inter-disciplinary research gap between high layer multimedia signal processing and low layer computer networking. It formulates the generic problem of quality-driven multimedia resource allocation in WMSNs and proposes a cross layer solution. The fundamental methodologies of multimedia selective encryption and stream authentication, and their application to digital image or video compression standards are presented. New multimedia selective encryption and stream authentication schemes are proposed at application layer, which significantly reduces encryption/authentication complexity. In addition, network resource allocation methodologies at low layers are extensively studied. An unequal error protection-based network resource allocation scheme is proposed to achieve the best effort media quality with integrity and energy efficiency guarantee. Performance evaluation results show that this cross layer framework achieves considerable energy-quality-security gain by jointly designing multimedia selective encryption/multimedia stream authentication and communication resource allocation
A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems
Recent technological advances have greatly improved the performance and
features of embedded systems. With the number of just mobile devices now
reaching nearly equal to the population of earth, embedded systems have truly
become ubiquitous. These trends, however, have also made the task of managing
their power consumption extremely challenging. In recent years, several
techniques have been proposed to address this issue. In this paper, we survey
the techniques for managing power consumption of embedded systems. We discuss
the need of power management and provide a classification of the techniques on
several important parameters to highlight their similarities and differences.
This paper is intended to help the researchers and application-developers in
gaining insights into the working of power management techniques and designing
even more efficient high-performance embedded systems of tomorrow
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
Influenced by the great success of deep learning via cloud computing and the
rapid development of edge chips, research in artificial intelligence (AI) has
shifted to both of the computing paradigms, i.e., cloud computing and edge
computing. In recent years, we have witnessed significant progress in
developing more advanced AI models on cloud servers that surpass traditional
deep learning models owing to model innovations (e.g., Transformers, Pretrained
families), explosion of training data and soaring computing capabilities.
However, edge computing, especially edge and cloud collaborative computing, are
still in its infancy to announce their success due to the resource-constrained
IoT scenarios with very limited algorithms deployed. In this survey, we conduct
a systematic review for both cloud and edge AI. Specifically, we are the first
to set up the collaborative learning mechanism for cloud and edge modeling with
a thorough review of the architectures that enable such mechanism. We also
discuss potentials and practical experiences of some on-going advanced edge AI
topics including pretraining models, graph neural networks and reinforcement
learning. Finally, we discuss the promising directions and challenges in this
field.Comment: 20 pages, Transactions on Knowledge and Data Engineerin
Teacher-Student Architecture for Knowledge Distillation: A Survey
Although Deep neural networks (DNNs) have shown a strong capacity to solve
large-scale problems in many areas, such DNNs are hard to be deployed in
real-world systems due to their voluminous parameters. To tackle this issue,
Teacher-Student architectures were proposed, where simple student networks with
a few parameters can achieve comparable performance to deep teacher networks
with many parameters. Recently, Teacher-Student architectures have been
effectively and widely embraced on various knowledge distillation (KD)
objectives, including knowledge compression, knowledge expansion, knowledge
adaptation, and knowledge enhancement. With the help of Teacher-Student
architectures, current studies are able to achieve multiple distillation
objectives through lightweight and generalized student networks. Different from
existing KD surveys that primarily focus on knowledge compression, this survey
first explores Teacher-Student architectures across multiple distillation
objectives. This survey presents an introduction to various knowledge
representations and their corresponding optimization objectives. Additionally,
we provide a systematic overview of Teacher-Student architectures with
representative learning algorithms and effective distillation schemes. This
survey also summarizes recent applications of Teacher-Student architectures
across multiple purposes, including classification, recognition, generation,
ranking, and regression. Lastly, potential research directions in KD are
investigated, focusing on architecture design, knowledge quality, and
theoretical studies of regression-based learning, respectively. Through this
comprehensive survey, industry practitioners and the academic community can
gain valuable insights and guidelines for effectively designing, learning, and
applying Teacher-Student architectures on various distillation objectives.Comment: 20 pages. arXiv admin note: substantial text overlap with
arXiv:2210.1733
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
Improving Performance of Feedback-Based Real-Time Networks using Model Checking and Reinforcement Learning
Traditionally, automatic control techniques arose due to need for automation in mechanical systems. These techniques rely on robust mathematical modelling of physical systems with the goal to drive their behaviour to desired set-points. Decades of research have successfully automated, optimized, and ensured safety of a wide variety of mechanical systems. Recent advancement in digital technology has made computers pervasive into every facet of life. As such, there have been many recent attempts to incorporate control techniques into digital technology. This thesis investigates the intersection and co-application of control theory and computer science to evaluate and improve performance of time-critical systems. The thesis applies two different research areas, namely, model checking and reinforcement learning to design and evaluate two unique real-time networks in conjunction with control technologies. The first is a camera surveillance system with the goal of constrained resource allocation to self-adaptive cameras. The second is a dual-delay real-time communication network with the goal of safe packet routing with minimal delays.The camera surveillance system consists of self-adaptive cameras and a centralized manager, in which the cameras capture a stream of images and transmit them to a central manager over a shared constrained communication channel. The event-based manager allocates fractions of the shared bandwidth to all cameras in the network. The thesis provides guarantees on the behaviour of the camera surveillance network through model checking. Disturbances that arise during image capture due to variations in capture scenes are modelled using probabilistic and non-deterministic Markov Decision Processes (MDPs). The different properties of the camera network such as the number of frame drops and bandwidth reallocations are evaluated through formal verification.The second part of the thesis explores packet routing for real-time networks constructed with nodes and directed edges. Each edge in the network consists of two different delays, a worst-case delay that captures high load characteristics, and a typical delay that captures the current network load. Each node in the network takes safe routing decisions by considering delays already encountered and the amount of remaining time. The thesis applies reinforcement learning to route packets through the network with minimal delays while ensuring the total path delay from source to destination does not exceed the pre-determined deadline of the packet. The reinforcement learning algorithm explores new edges to find optimal routing paths while ensuring safety through a simple pre-processing algorithm. The thesis shows that it is possible to apply powerful reinforcement learning techniques to time-critical systems with expert knowledge about the system
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Authentication with Distortion Criteria
In a variety of applications, there is a need to authenticate content that
has experienced legitimate editing in addition to potential tampering attacks.
We develop one formulation of this problem based on a strict notion of
security, and characterize and interpret the associated information-theoretic
performance limits. The results can be viewed as a natural generalization of
classical approaches to traditional authentication. Additional insights into
the structure of such systems and their behavior are obtained by further
specializing the results to Bernoulli and Gaussian cases. The associated
systems are shown to be substantially better in terms of performance and/or
security than commonly advocated approaches based on data hiding and digital
watermarking. Finally, the formulation is extended to obtain efficient layered
authentication system constructions.Comment: 22 pages, 10 figure
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