59 research outputs found
A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art
Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research
Internet of Things From Hype to Reality
The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions
Big Data Security (Volume 3)
After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology
Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs
The way we travel is changing rapidly and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper introduces an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster researching, testing, and evaluating the cybersecurity of C-ITSs. We explore the design, functionality, and challenges of CSCE's testing facilities, outlining the technological, security, and societal requirements. Through a thorough survey and analysis, we assess the effectiveness of these systems in detecting and mitigating potential threats, highlighting their flexibility to adapt to future C-ITSs. Finally, we identify current unresolved challenges in various C-ITS domains, with the aim of motivating further research into the cybersecurity of C-ITSs
Scalable and responsive real time event processing using cloud computing
PhD ThesisCloud computing provides the potential for scalability and adaptability in a cost e ective
manner. However, when it comes to achieving scalability for real time applications
response time cannot be high. Many applications require good performance and low
response time, which need to be matched with the dynamic resource allocation. The
real time processing requirements can also be characterized by unpredictable rates
of incoming data streams and dynamic outbursts of data. This raises the issue of
processing the data streams across multiple cloud computing nodes. This research
analyzes possible methodologies to process the real time data in which applications
can be structured as multiple event processing networks and be partitioned over the
set of available cloud nodes. The approach is based on queuing theory principles
to encompass the cloud computing. The transformation of the raw data into useful
outputs occurs in various stages of processing networks which are distributed across
the multiple computing nodes in a cloud. A set of valid options is created to understand
the response time requirements for each application. Under a given valid set of
conditions to meet the response time criteria, multiple instances of event processing
networks are distributed in the cloud nodes. A generic methodology to scale-up and
scale-down the event processing networks in accordance to the response time criteria
is de ned. The real time applications that support sophisticated decision support
mechanisms need to comply with response time criteria consisting of interdependent
data
ow paradigms making it harder to improve the performance. Consideration is
given for ways to reduce the latency,improve response time and throughput of the real
time applications by distributing the event processing networks in multiple computing
nodes
Self-Forecasting Energy Load Stakeholders for Smart Grids
The unpredictability of energy loads is responsible for a significant portion of efficiency loss in power grids. In order to reduce load uncertainties, emerging Smart Grid business models call for the active participation of traditionally passive stakeholders. The contribution of this work enables self-forecasting energy load stakeholders whose deterministic load behaviour make them reliable resources that can greatly benefit themselves and other Smart Grid stakeholders
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
A Multi-Criteria Framework to Assist on the Design of Internet-of-Things Systems
The Internet-of-Things (IoT), considered as Internet first real evolution, has become
immensely important to society due to revolutionary business models with the potential
to radically improve Human life. Manufacturers are engaged in developing embedded
systems (IoT Systems) for different purposes to address this new variety of application
domains and services. With the capability to agilely respond to a very dynamic market
offer of IoT Systems, the design phase of IoT ecosystems can be enhanced. However,
select the more suitable IoT System for a certain task is currently based on stakeholder’s
knowledge, normally from lived experience or intuition, although it does not mean that
a proper decision is being made. Furthermore, the lack of methods to formally describe
IoT Systems characteristics, capable of being automatically used by methods is also an
issue, reinforced by the growth of available information directly connected to Internet
spread.
Contributing to improve IoT Ecosystems design phase, this PhD work proposes a
framework capable of fully characterise an IoT System and assist stakeholder’s on the decision
of which is the proper IoT System for a specific task. This enables decision-makers
to perform a better reasoning and more aware analysis of diverse and very often contradicting
criteria. It is also intended to provide methods to integrate energy consumptionsimulation
tools and address interoperability with standards, methods or systems within
the IoT scope. This is addressed using a model-driven based framework supporting a
high openness level to use different software languages and decision methods, but also
for interoperability with other systems, tools and methods
Information-Theoretic Secure Outsourced Computation in Distributed Systems
Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design
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