3,246 research outputs found
Radio frequency fingerprint identification for Internet of Things: A survey
Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field
Deep generative models for network data synthesis and monitoring
Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network.
Although networks inherently
have abundant amounts of monitoring data, its access and effective measurement is
another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset
without leaking commercial sensitive information. Second, it could be very expensive
to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of
flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources
in the network element that can be applied to support the measurement function are
too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex
structure. Various emerging optimization-based solutions (e.g., compressive sensing)
or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet
meet the current network requirements.
The contributions made in this thesis significantly advance the state of the art in
the domain of network measurement and monitoring techniques. Overall, we leverage
cutting-edge machine learning technology, deep generative modeling, throughout the
entire thesis. First, we design and realize APPSHOT , an efficient city-scale network
traffic sharing with a conditional generative model, which only requires open-source
contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we
design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time
network telemetry system with latent GANs and spectral-temporal networks. Finally,
we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through
this research are summarized, and interesting topics are discussed for future work in
this domain. All proposed solutions have been evaluated with real-world datasets and
applied to support different applications in real systems
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Integrating IoT Analytics into Marketing Decision Making: A Smart Data-Driven Approach
With the advent of the Internet of Things (IoT), businesses have gained access to vast amounts of data generated by interconnected devices. Leveraging IoT analytics and marketing intelligence, organizations can extract valuable insights from this data to enhance decision-making processes. This paper presents a comprehensive methodology for data-driven decision-making in the context of IoT analytics and marketing intelligence. A real-time example is used to illustrate the application of this methodology, followed by an inference and discussion of the results. The rise of IoT has enabled real-time data collection from a wide array of interconnected devices, offering unprecedented opportunities for businesses to gain actionable insights. This paper focuses on the intersection of IoT analytics and marketing intelligence, exploring how data-driven decision-making can empower organizations to optimize their marketing strategies, customer experiences, and overall business performance
Modern computing: Vision and challenges
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach
Cyberattacks always remain the major threats and challenging issues in the modern digital world. With the increase in the number of internet of things (IoT) devices, security challenges in these devices, such as lack of encryption, malware, ransomware, and IoT botnets, leave the devices vulnerable to attackers that can access and manipulate the important data, threaten the system, and demand ransom. The lessons from the earlier experiences of cyberattacks demand the development of the best-practices benchmark of cybersecurity, especially in modern Smart Environments. In this study, we propose an approach with a framework to discover malware attacks by using artificial intelligence (AI) methods to cover diverse and distributed scenarios. The new method facilitates proactively tracking network traffic data to detect malware and attacks in the IoT ecosystem. Moreover, the novel approach makes Smart Environments more secure and aware of possible future threats. The performance and concurrency testing of the deep neural network (DNN) model deployed in IoT devices are computed to validate the possibility of in-production implementation. By deploying the DNN model on two selected IoT gateways, we observed very promising results, with less than 30 kb/s increase in network bandwidth on average, and just a 2% increase in CPU consumption. Similarly, we noticed minimal physical memory and power consumption, with 0.42 GB and 0.2 GB memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device with the deployed model. The ML models were able to demonstrate nearly 93% of detection accuracy and 92% f1-score on both utilized datasets. The result of the models shows that our framework detects malware and attacks in Smart Environments accurately and efficiently.publishedVersio
Enabling Deep Neural Network Inferences on Resource-constraint Devices
Department of Computer Science and EngineeringWhile deep neural networks (DNN) are widely used on various devices, including resource-constraint devices such as IoT, AR/VR, and mobile devices, running DNN from resource-constrained devices remains challenging. There exist three approaches for DNN inferences on resource-constraint devices: 1) lightweight DNN for on-device computing, 2) offloading DNN inferences to a cloud server, and 3) split computing to utilize computation and network resources efficiently.
Designing a lightweight DNN without compromising the accuracy of DNN is challenging due to a trade-off between latency and accuracy, that more computation is required to achieve higher accuracy. One solution to overcome this challenge is pre-processing to extract and transfer helpful information to achieve high accuracy of DNN. We design the pre-processing, which consists of three processes. The first process of pre-processing is finding out the best input source. The second process is the input-processing which extracts and contains important information for DNN inferences among the whole information gained from the input source. The last process is choosing or designing a suitable lightweight DNN for processed input. As an instance of how to apply the pre-processing, in Sec 2, we present a new transportation mode recognition system for smartphones called DeepVehicleSense, which aims at achieving three performance objectives: high accuracy, low latency, and low power consumption at once by exploiting sound characteristics captured from the built-in microphone while being on candidate transportations. To achieve high accuracy and low latency, DeepVehicleSense makes use of non-linear filters that can best extract the transportation sound samples. For the recognition of five different transportation modes, we design a deep learning-based sound classifier using a novel deep neural network architecture with multiple branches. Our staged inference technique can significantly reduce runtime and energy consumption while maintaining high accuracy for the majority of samples.
Offloading DNN inferences to a server is a solution for DNN inferences on resource-constraint devices, but there is one concern about latency caused by data transmission. To reduce transmission latency, recent studies have tried to make this offloading process more efficient by compressing data to be offloaded. However, conventional compression techniques are designed for human beings, so they compress data to be possible to restore data, which looks like the original from the perspective of human eyes. As a result, the compressed data through the compression technique contains redundancy beyond the necessary information for DNN inference.
In other words, the most fundamental question on extracting and offloading the minimal amount of necessary information that does not degrade the inference accuracy has remained unanswered. To answer the question, in Sec 3, we call such an ideal offloading semantic offloading and propose N-epitomizer, a new offloading framework that enables semantic offloading, thus achieving more reliable and timely inferences in highly-fluctuated or even low-bandwidth wireless networks. To realize N-epitomizer, we design an autoencoder-based scalable encoder trained to extract the most informative data and scale its output size to meet the latency and accuracy requirements of inferences over a network.
Even though our proposed lightweight DNN and offloading framework with the essential information extractor achieve low latency while preserving DNN performance, they alone cannot realize latency-guaranteed DNN inferences. To realize latency-guaranteed DNN inferences, the computational complexity of the lightweight DNN and the compression performance of the encoder for offloading should be adaptively selected according to current computation resources and network conditions by utilizing the DNN's trade-off between computational complexity and DNN performance and the encoder's trade-off between compression performance and DNN performance. To this end, we propose a new framework for latency-guaranteed DNN inferences called LG-DI, which predicts DNN performance degradation given a latency budget in advance and utilizes the better method between the lightweight DNN and offloading with compression. As a result, our proposed framework for DNN inferences can guarantee latency regardless of changes in computation and network resources while maintaining DNN performance as much as possible.ope
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