626 research outputs found
Towards Scalable, Private and Practical Deep Learning
Deep Learning (DL) models have drastically improved the performance of Artificial Intelligence (AI) tasks such as image recognition, word prediction, translation, among many others, on which traditional Machine Learning (ML) models fall short. However, DL models are costly to design, train, and deploy due to their computing and memory demands. Designing DL models usually requires extensive expertise and significant manual tuning efforts. Even with the latest accelerators such as Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU), training DL models can take prohibitively long time, therefore training large DL models in a distributed manner is a norm. Massive amount of data is made available thanks to the prevalence of mobile and internet-of-things (IoT) devices. However, regulations such as HIPAA and GDPR limit the access and transmission of personal data to protect security and privacy. Therefore, enabling DL model training in a decentralized but private fashion is urgent and critical. Deploying trained DL models in a real world environment usually requires meeting Quality of Service (QoS) standards, which makes adaptability of DL models an important yet challenging matter. In this dissertation, we aim to address the above challenges to make a step towards scalable, private, and practical deep learning. To simplify DL model design, we propose Efficient Progressive Neural-Architecture Search (EPNAS) and FedCust to automatically design model architectures and tune hyperparameters, respectively. To provide efficient and robust distributed training while preserving privacy, we design LEASGD, TiFL, and HDFL. We further conduct a study on the security aspect of distributed learning by focusing on how data heterogeneity affects backdoor attacks and how to mitigate such threats. Finally, we use super resolution (SR) as an example application to explore model adaptability for cross platform deployment and dynamic runtime environment. Specifically, we propose DySR and AdaSR frameworks which enable SR models to meet QoS by dynamically adapting to available resources instantly and seamlessly without excessive memory overheads
5G Multi-access Edge Computing: Security, Dependability, and Performance
The main innovation of the Fifth Generation (5G) of mobile networks is the
ability to provide novel services with new and stricter requirements. One of
the technologies that enable the new 5G services is the Multi-access Edge
Computing (MEC). MEC is a system composed of multiple devices with computing
and storage capabilities that are deployed at the edge of the network, i.e.,
close to the end users. MEC reduces latency and enables contextual information
and real-time awareness of the local environment. MEC also allows cloud
offloading and the reduction of traffic congestion. Performance is not the only
requirement that the new 5G services have. New mission-critical applications
also require high security and dependability. These three aspects (security,
dependability, and performance) are rarely addressed together. This survey
fills this gap and presents 5G MEC by addressing all these three aspects.
First, we overview the background knowledge on MEC by referring to the current
standardization efforts. Second, we individually present each aspect by
introducing the related taxonomy (important for the not expert on the aspect),
the state of the art, and the challenges on 5G MEC. Finally, we discuss the
challenges of jointly addressing the three aspects.Comment: 33 pages, 11 figures, 15 tables. This paper is under review at IEEE
Communications Surveys & Tutorials. Copyright IEEE 202
Threat Modelling Guided Trust-based Task Offloading for Resource-constrained Internet of Things
There is an increasing demand for Internet of Things (IoT) networks consisting of resource-constrained devices executing increasingly complex applications. Due to these resource-constraints, IoT devices will not be able to execute expensive tasks. One solution is to offload expensive tasks to resource-rich edge nodes. Which requires a framework that facilitates the selection of suitable edge nodes to perform task offloading. Therefore, in this paper, we present a novel trust model-driven system architecture, based on behavioural evidence, that is suitable for resource-constrained IoT devices that supports computation offloading. We demonstrate the viability of the proposed architecture with an example deployment of the Beta Reputation System trust model on real hardware to capture node behaviours. The open environment of edge-based IoT networks means that threats against edge nodes can lead to deviation from expected behaviour. Hence, we perform a threat modelling to identify such threats. The proposed system architecture includes threat handling mechanisms that provide security properties such as confidentiality, authentication and non-repudiation of messages in required scenarios and operate within the resource constraints. We evaluate the efficacy of the threat handling mechanisms and identify future work for the standards used
Edge Computing for Internet of Things
The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond
Edge/Fog Computing Technologies for IoT Infrastructure
The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies
Machine Learning Meets Communication Networks: Current Trends and Future Challenges
The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction
Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
Recent years have seen rapid deployment of mobile computing and Internet of
Things (IoT) networks, which can be mostly attributed to the increasing
communication and sensing capabilities of wireless systems. Big data analysis,
pervasive computing, and eventually artificial intelligence (AI) are envisaged
to be deployed on top of the IoT and create a new world featured by data-driven
AI. In this context, a novel paradigm of merging AI and wireless
communications, called Wireless AI that pushes AI frontiers to the network
edge, is widely regarded as a key enabler for future intelligent network
evolution. To this end, we present a comprehensive survey of the latest studies
in wireless AI from the data-driven perspective. Specifically, we first propose
a novel Wireless AI architecture that covers five key data-driven AI themes in
wireless networks, including Sensing AI, Network Device AI, Access AI, User
Device AI and Data-provenance AI. Then, for each data-driven AI theme, we
present an overview on the use of AI approaches to solve the emerging
data-related problems and show how AI can empower wireless network
functionalities. Particularly, compared to the other related survey papers, we
provide an in-depth discussion on the Wireless AI applications in various
data-driven domains wherein AI proves extremely useful for wireless network
design and optimization. Finally, research challenges and future visions are
also discussed to spur further research in this promising area.Comment: Accepted at the IEEE Communications Surveys & Tutorials, 42 page
Street Smart in 5G : Vehicular Applications, Communication, and Computing
Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart vehicular mobility. Specifically, vehicles, roadside units, and other road users can collaborate to deliver novel services and applications that leverage, for example, big vehicular data and machine learning. Relatedly, fifth-generation cellular networks (5G) are being developed and deployed for low-latency, high-reliability, and high bandwidth communications. While 5G adjacent technologies such as edge computing allow for data offloading and computation at the edge of the network thus ensuring even lower latency and context-awareness. Overall, these developments provide a rich ecosystem for the evolution of vehicular applications, communications, and computing. Therefore in this work, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G and big data. In particular, this paper highlights several vehicular applications, investigates their requirements, details the enabling communication technologies and computing paradigms, and studies data analytics pipelines and the integration of these enabling technologies in response to application requirements.Peer reviewe
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