This letter proposes a cost-efficient convolutional neural network (CNN) for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems. ...The network architecture is designed with several specific convolutional blocks to concurrently learn the spatiotemporal signal correlations via different asymmetric convolution kernels. Additionally, these blocks are associated with skip connections to preserve more initially residual information at multi-scale feature maps and prevent the vanishing gradient problem. In the experiments, MCNet reaches the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.
In this letter, we propose an improved convolutional neural network (CNN)-based automatic modulation classification network (IC-AMCNet), an algorithm to classify the modulation type of a wireless ...signal. Since adaptive coding and modulation is widely used in wireless communication, high accuracy and short computing time of classifier is needed. Compared with the existing CNN architectures, we adjusted the number of layers and added new type of layers to comply with the estimated latency standards in beyond fifth-generation (B5G) communications. According to the simulation results, the proposed scheme significantly outperforms the previous works in terms of both classification accuracy and computing time.
In the IoT-based systems, the fog computing allows the fog nodes to offload and process tasks requested from IoT-enabled devices in a distributed manner instead of the centralized cloud servers to ...reduce the response delay. However, achieving such a benefit is still challenging in the systems with high rate of requests, which imply long queues of tasks in the fog nodes, thus exposing probably an inefficiency in terms of latency to offload the tasks. In addition, a complicated heterogeneous degree in the fog environment introduces an additional issue that many of single fogs can not process heavy tasks due to lack of available resources or limited computing capabilities. To cope with the situation, this article introduces FRATO (Fog Resource aware Adaptive Task Offloading) - a framework for the IoT-fog-cloud systems to offer the minimal service provisioning delay through an adaptive task offloading mechanism. Fundamentally, FRATO is based on the fog resource to select flexibly the optimal offloading policy, which in particular includes a collaborative task offloading solution based on the data fragment concept. In addition, two distributed fog resource allocation algorithms, namely TPRA and MaxRU are developed to deploy the optimized offloading solutions efficiently in cases of resource competition. Through the extensive simulation analysis, the FRATO-based service provisioning approaches show potential advantages in reducing the average delay significantly in the systems with high rate of service requests and heterogeneous fog environment compared with the existing solutions.
This paper proposes a convolutional neural network (CNN), called SCGNet, for low-complexity and robust modulation recognition in intelligent communication receivers. Principally, the network combines ...two types of sparse convolutional layers-depthwise and regular grouped in an architecture to achieve high recognition accuracy while keeping the network more lightweight. The network architecture leverages sparsely connected convolutional layers in three principal modules: speed-accuracy tradeoff (SAT), deep feature extraction and processing (DFEP), and generic feature extraction (GFE) data pre-processing module. For a good tradeoff between complexity and accuracy, SAT deploys depthwise convolutional layers to enrich the relevant features outputted by the former GFE module. In addition to SAT, DFEP employs a cascade of regular grouped convolutional layers for mining more discriminative features from SAT via a multilayer transformation module. This cascade structure aims to prevent a loss of essential details of the signal as the network becomes deeper. Additionally, skip connections are deployed between sub-blocks within SAT and DFEP to allow inter-module feature sharing and to handle inter-block features loss. Experimental results on the RadioML2018.01A dataset indicate that SCGNet achieves an overall recognition accuracy of around 94.39% at a signal-to-noise ratio of +20 dB.
The Metaverse is a concept used to refer to a virtual world that exists in parallel to the physical world. It has grown from a conceptual level to having real applications in virtual reality games. ...The applicability of the Metaverse in numerous sectors like marketing, education, social, and even advertising exists. However, there exists little or no work on Metaverse applicability to the transportation industry. Data‐driven intelligent transportation systems (DDITS) aim to provide more intelligent systems based on exploiting data. This paper reviews the concepts and features of the Metaverse. Also, the review goes over three dominant DDITS challenges: vehicle fault detection and repair, testing new technologies, and anti‐theft systems. In addition, it highlights prospective Metaverse solutions that apply to the DDITS. Buttressing the utility of Metaverse in DDITS, this paper presents two major case studies: the invisible to visible (I2V) and the Metaverse on Wheels (MoW) technologies. Finally, the influence, limitations, and open issues of Metaverse applications to DDITS are discussed.
In oceanic remote sensing operations, underwater acoustic target recognition is always a difficult and extremely important task of sonar systems, especially in the condition of complex sound wave ...propagation characteristics. The expensively learning recognition model for big data analysis is typically an obstacle for most traditional machine learning (ML) algorithms, whereas the convolutional neural network (CNN), a type of deep neural network, can automatically extract features for accurate classification. In this study, we propose an approach using a dense CNN model for underwater target recognition. The network architecture is designed to cleverly reuse all former feature maps to optimize classification rates under various impaired conditions while satisfying low computational cost. In addition, instead of using time-frequency spectrogram images, the proposed scheme allows directly utilizing the original audio signal in the time domain as the network input data. Based on the experimental results evaluated on the real-world data set of passive sonar, our classification model achieves the overall accuracy of 98.85% at 0-dB signal-to-noise ratio (SNR) and outperforms traditional ML techniques, as well as other state-of-the-art CNN models.
The Physical Internet (PI, or <inline-formula> <tex-math notation="LaTeX">\pi </tex-math></inline-formula>) paradigm has been developed to be a global logistics system that aims to move, handle, ...store, and transport logistics products in a sustainable and efficient way. To achieve the goal, the PI requires a high-level interconnectivity in the physical, informational, and operational aspects enabled by an interconnected network of intermodal hubs, collaborative protocols, and standardized, modular, and smart containers. In this context, PI is a key player poised to benefit from the Internet-of-Things (IoT) revolution since it potentially provides an end-to-end visibility of the PI objects, operations, and systems through ubiquitous information exchange. This article is to investigate opportunities of application of the IoT technology in the PI vision. In addition, an IoT ecosystem (<inline-formula> <tex-math notation="LaTeX">\pi </tex-math></inline-formula>-IoT) encompassing key enabling IoT technologies, building blocks, and a service-oriented architecture (SoA) is proposed as a potential component for accelerating the implementation of PI. The major challenges regarding the deployment of IoT into the emerging logistics concept are also discussed intensively for further research.
Artificial intelligence for the metaverse: A survey Huynh-The, Thien; Pham, Quoc-Viet; Pham, Xuan-Qui ...
Engineering applications of artificial intelligence,
January 2023, 2023-01-00, Volume:
117
Journal Article
Peer reviewed
Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in ...cyberspace. Many virtual environments have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse has been introduced as a shared virtual world that is fueled by many emerging technologies. Among such technologies, artificial intelligence (AI) has shown the great importance of enhancing immersive experience and enabling human-like intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI, including machine learning algorithms and deep learning architectures, in the foundation and development of the metaverse. As the main contributions, we convey a comprehensive investigation of AI-based methods concerning several technical aspects (e.g., natural language processing, machine vision, blockchain, networking, digital twin, and neural interface) that have potentials to build virtual worlds in the metaverse. Furthermore, several primary AI-aided applications, including healthcare, manufacturing, smart cities, and gaming, are studied to be promisingly deployed in the virtual worlds. Finally, we conclude the key contribution and open some future research directions of AI for the metaverse. Serving as a foundational survey, this work will help researchers, including experts and non-experts in related fields, in applying, developing, and optimizing AI techniques to polish the appearance of virtual worlds and improve the quality of applications built in the metaverse.
Display omitted
•Studying AI-based technical works that are potential to build the Metaverse.•Surveying AI-aided application works that are promisingly deployed in the Metaverse.•Reviewing of some common AI-fueled Metaverse projects.•Providing some research directions based on some major limitations of AI.
Tides of Blockchain in IoT Cybersecurity Ahakonye, Love Allen Chijioke; Nwakanma, Cosmas Ifeanyi; Kim, Dong-Seong
Sensors (Basel, Switzerland),
05/2024, Volume:
24, Issue:
10
Journal Article
Peer reviewed
Open access
This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with ...intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper's structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT.
Recently, numerous methods have been introduced for three-dimensional (3-D) action recognition using handcrafted feature descriptors coupled traditional classifiers. However, they cannot learn ...high-level features of a whole skeleton sequence exhaustively. In this paper, a novel encoding technique-namely, pose feature to image (PoF2I), is introduced to transform the pose features of joint-joint distance and orientation to color pixels. By concatenating the features of all skeleton frames in a sequence, a color image is generated to depict spatial joint correlations and temporal pose dynamics of an action appearance. The strategy of end-to-end fine-tuning a pretrained deep convolutional neural network, which completely capture multiple high-level features at multiscale action representation, is implemented for learning recognition models. We further propose an efficient data augmentation mechanism for informative enrichment and overfitting prevention. The experimental results on six challenging 3-D action recognition datasets demonstrate that the proposed method outperforms state-of-the-art approaches.
