As Internet of Things (IoT) networks continue to grow in complexity and scale, ensuring reliable service delivery while defending against cyber attacks such as Distributed Denial of Service (DDoS) has become increasingly critical. IoT networks, with their resource-constrained devices, diverse traffic patterns, and real-time requirements, amplify the limitations of existing DDoS detection and mitigation solutions. These solutions often prioritize classification accuracy, but rely on static policies that do not adapt to evolving traffic behaviour or prioritize critical services. To address these challenges, Intent-Based Networking (IBN) offers a promising approach by enabling networks to dynamically align with high-level service goals, such as prioritizing control traffic or ensuring low-latency communication. However, current security solutions lack integration with IBN, resulting in a gap in context-driven, intent-aware DDoS mitigation. To address this, the paper proposes an intelligent intent-aware system for DDoS attack detection and mitigation (INACT) in IoT networks. The INACT system introduces a dual-output deep learning model that classifies both the type of traffic (benign or malicious) and its operational intent (e.g., control, security, or bandwidth priority), using a multitask learning approach. The INACT system uses a gradient-based method to select the most relevant features, allowing it to run smoothly on lightweight edge devices. To take immediate and meaningful action, the system includes a controller that applies different mitigation strategies depending on the intent of traffic. This ensures that critical services are protected first and that nonessential traffic is managed with minimal disruption during the attack response. The INACT system is evaluated using benchmark datasets such as HL-IoT and CICIoT-2023 and is deployed on a real testbed. The INACT system achieves high detection and intent classification accuracy while maintaining low latency, resource usage, and mitigation effectiveness.

With the rapid development of artificial intelligence (AI) and ubiquitous connectivity, speech communication is becoming increasingly important in achieving intelligent interactions between humans, machines, and objects in computational social systems. However, current neural network-based semantic communication frameworks primarily focus on transmitting semantic information while largely overlooking the emotion features in speech communication, which is vital for naturalness and effective interaction in computational social systems. In this article, we propose an emotion-enhanced speech semantic communication system, which effectively enhances the expressiveness and robustness of emotion AI for speech communication. First, we propose an emotion fusion encoding module at the transmitter, where features are dynamically fused via attention mechanisms and subsequently encoded through a channel encoder. Then, we introduce an emotion orthogonal decoding module at the receiver, which reconstructs the fused features via a channel decoder followed by an orthogonally constrained disentanglement network. In addition, a conditional diffusion model guided by emotion features reconstructs high-fidelity speech with enriched emotional expressiveness. Finally, experimental evaluations demonstrate that the proposed framework significantly improves both the bit error rate and the mean opinion score over state-of-the-art models. Furthermore, the system achieves notable reductions in transmission dimensionality.

Personalized healthcare increasingly relies on AI-driven multimodal fusion to enhance diagnostic precision and treatment planning. However, long MRI acquisition times, imaging artifacts, and missing modalities often lead to incomplete critical imaging information, limiting the application of multimodal MRI in personalized diagnostics. To address this challenge, we propose Dual-Scale Multimodal Fusion Network (Dual-MFNet), a novel AI-driven approach to personalized MRI synthesis for reconstructing missing modalities with high anatomical fidelity. Our method leverages state-space models to capture long-range contextual dependencies while preserving local structural integrity, ensuring accurate cross-modal synthesis. The Dual-Scale Feature Fuser (Dual-Fuser) balances global coherence with fine-grained detail preservation, while the Twin-Stream Fusion module (TSF) dynamically enhances critical cross-modal information. In addition, the Feature Aggregation (FA) module consolidates multimodal input into a cohesive representation, producing high-fidelity synthesized MRI customized to individual patient needs. To assess clinical relevance, we conducted extensive quantitative evaluations and a radiological reader study with five experienced radiologists. The results demonstrate that Dual-MFNet outperforms state-of-the-art methods, particularly in preserving tumor boundaries, fine tissue textures, and anatomical clarity, making it a valuable tool to advance personalized MRI-based diagnostics.

Today, electric vehicles (EVs) have gained significant recognition in the global market as an innovative mode of transport. However, the development of EVs is based on the interaction with the energy Internet. The increasing number of EVs has presented significant challenges to the existing charging infrastructure. Traditional fixed-location charging stations are increasingly inadequate to meet fluctuating charging demand, leading to inefficiencies such as long waiting times and uneven distribution of charging load. To address the problem, this paper proposes a Mobile-Fixed Charging Stations Collaboration Network (MFCSCN), which integrates fixed charging stations with mobile charging stations to dynamically adapt to real-time charging demands. Firstly, LightGBM is utilized to predict the charging load at fixed charging stations considering historical data and real-time EV mobility patterns. Secondly, the reinforcement learning algorithm is employed to optimize the placement of mobile charging stations based on predicted demand. Third, within the MFCSCN framework, LPPO (LightGBM and Proximal Policy Optimization) is proposed, which combines predictive modeling and reinforcement learning to optimize the dynamic placement of mobile charging stations. Through extensive simulations, we demonstrate that the MFCSCN significantly improves the responsiveness and scalability of the EV charging infrastructure, offering a robust solution to the evolving needs of urban mobility.

Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure to effectively capture the spatial relationships within high-dimensional data to recover them accurately. To address these problems, we present a novel Non-local Self-similarity and Low-rank Prior Knowledge based tensor ring completion method. Firstly, we incorporate the BM3D denoising operator within a Plug-and-Play framework to exploit the self-similarity in the data. Then a logarithmic determinant function is integrated to distinguish singular values in the cyclic unfolding matrix of the tensor and adopts a tensor ring completion approach based on weighted nuclear norms. Finally, in order to evaluate the effectiveness of our proposed method, we conducted a series of experiments by using the missing image dataset and the missing traffic data dataset respectively, and the experimental results show that our method achieves the highest level in terms of data recovery accuracy.

Large Language Models (LLMs) have enormous potential in healthcare, but we also need to be careful and implement strong safety measures. These advanced computer systems can understand complex medical questions written in everyday language and often provide accurate medical information. This article explores how the field has evolved, from the first language models to the LLMs we have now, specifically designed or adjusted for use in the medical world. We explore how LLMs can make important language-related tasks in healthcare more efficient and effective, such as identifying medical terms, understanding how different pieces of information are connected, drawing conclusions from written text, sorting documents, and answering medical queries. We also shed light on new and exciting ways LLMs can use more than just text, like combining written information with images or other types of data. We compare some of the best recent LLMs and discuss how they can be used in everyday medical situations. We also measure how well these models perform in the biomedical field, explaining what each measurement tells us and where it falls short. Last, by discussing the biggest hurdles LLMs face in healthcare—things like generating incorrect information (hallucinations), unfair biases, protecting patient privacy, making sure the models are dependable, and fitting these new tools into how doctors and nurses already work. We also provide a roadmap for future research. We aim to present a clear and fair picture of what LLMs can offer healthcare right now and what is necessary to use them safely and dependably. This includes explaining how researchers are working to make these models easier for doctors and patients to understand. We followed the PRISMA research method to guide our review of the field, from early language models to current LLMs adapted for clinical use.

The integration of Large Language Models (LLM) with multimodal agentic AI within the Medical Internet of Things (MIoT) ecosystem is redefining modern healthcare intelligence. This convergence enables continuous patient observation, adaptive clinical decision-making, and context-aware interaction between humans and machines across various biomedical data modalities. Healthcare systems generate a wide range of multimodal data, including textual records such as EHRs, prescriptions, and pathology notes; medical imagery such as CT, MRI, fundus, and radiographs; spoken data from consultations and transcriptions; video streams for rehabilitation and physiotherapy monitoring; and sensor readings such as ECG, SpO \({}_{2}\) , and glucose levels. Conventional unimodal algorithms fall short in interpreting this diversity, whereas LLM-augmented agentic frameworks fuse and reason over these heterogeneous sources, grounding their outputs in medical ontologies and coordinating task-specific agents to enhance real-world clinical workflows. This article presents a comprehensive overview of multimodal agentic AI powered by LLM for MIoT-enabled healthcare systems. Introduces a 6D unified taxonomy that covers multimodal input channels, fusion mechanisms, core LLM reasoning capabilities, agentic coordination models, computational deployment layers, and ethical governance frameworks. To contextualize this taxonomy, the discussion includes a Virtual Hospital case study centered on cancer that demonstrates how multimodal signals such as imaging, genomics, patient dialogues, and clinical updates integrate through intelligent agents to enable personalized diagnosis, automated documentation, home rehabilitation, and rapid intervention in emergencies. The survey also consolidates current progress on datasets, benchmarks, and evaluation protocols for AI in multimodal and agentic healthcare. The survey identifies critical research gaps, such as the lack of longitudinal multimodal datasets, standardized evaluation frameworks for multi-agent reasoning, and reliable methods to assess trustworthiness in clinical AI. Furthermore, it examines security and compliance issues such as adversarial manipulation, data leakage, and accountability across distributed agent networks, and it proposes countermeasures through federated data governance, secure MCP-oriented orchestration, and privacy-aware edge deployment strategies. By situating recent advances within the Virtual Hospital paradigm and oncology workflows, this study provides a systematic foundation for developing scalable, secure, and ethically aligned multimodal agentic systems based on LLMs, guiding the next generation of intelligent MIoT-driven healthcare ecosystems.

Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encrypted medical data is not always easy and often compromises performance and security. To address these limitations, this research introduces a secure framework consisting of a learnable encryption method based on block-pixel operation to encrypt the data and subsequently integrate it with the Vision Transformer (ViT). The proposed framework ensures data privacy and security by creating unique scrambling patterns per key, providing robust performance against leading bit attacks and minimum difference attacks.

Continuous and accurate monitoring of cancer patients’ Quality of Life (QoL)-related health status, including Patient-Reported Experience Measures (PREM), Patient-Reported Outcome Measures (PROM), and co-morbidity occurrences, is vital for effective cancer management. Traditionally, oncologists only access these critical insights during patients’ physical visits, leading to gaps in real-time monitoring and delayed interventions. Since patients primarily remain at home, structured QoL data collection using PREM and PROM instruments is often absent, leaving critical side effects and comorbidities undocumented. This paper presents ONCO-LLM, an innovative framework using agentic Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) over Internet of Medical Things (IoMT) networks for seamless in-home QoL data collection and integration. ONCO-LLM enhances patient-doctor communication via AI-driven interactive conversations, enabling continuous health status updates. Diversified AI agents within the LLM-RAG architecture collaboratively communicate to parse multimodal Electronic Health Records (EHR), synthesize QoL data, and dynamically integrate patient information into treatment profiles. Leveraging secure, efficient IoMT communication, the framework ensures timely transmission of QoL data and actionable medical insights. Preliminary evaluation demonstrates ONCO-LLM effectively bridges the patient data gap, enhances patient engagement through continuous communication, supports real-time clinical decisions, and significantly improves overall cancer care quality and outcomes.

In response to the rapid growth of data in cyberspace and the resulting challenge to storage capacity, this paper proposes a new off-chain storage scheme for blockchain based on erasure codes. The scheme allows for the application of different coding methods tailored to various scenarios. To validate its feasibility, an off-chain distributed storage test system built on the proposed framework is implemented. The test results demonstrate that the proposed scheme ensures blockchain data integrity from local and global perspectives reducing the system's repair bandwidth. This novel off-chain storage approach addresses blockchain's storage limitations and has the potential to enhance the overall robustness and reliability of the system.

Industry 5.0 aims to transform the healthcare sector by integrating emerging technologies like Artificial Intelligence (AI) and the Internet of Things (IoT) with a human-centered focus on patient wellness and preventive care. Although this approach promises personalized care and improved sustainability in healthcare systems, it also introduces cyber risks that could lead to economic and physical losses. This emphasizes the urgent need for enhanced cybersecurity measures in healthcare Industry 5.0 systems. Therefore, this paper presents a new framework for detecting cyberattacks while protecting patient data. The framework employs a human-centric approach and Quantum Random Forest (QRF) with local differential privacy for effective attack detection. It also integrates active learning with threat intelligence feeds and generative AI tools like ChatGPT to further support human roles and improve detection capabilities. We evaluated the efficiency of our proposed framework in terms of performance metrics such as accuracy, detection rate, time, and memory complexity. The experimental results show that our proposed framework excelled in attack detection using the ICU and WUST-EHMS-2020 datasets.

The Cognitive Internet of Things (CIoT) represents an advanced paradigm that equips IoT devices with cognitive abilities, enabling them to perceive, communicate, learn, reason, and adapt intelligently. This forms a key enabler for next-generation industrial systems. However, the limited local computing resources of CIoT devices make it challenging to perform complex computational tasks. To address this limitation, we propose a CIoT architecture supported by Digital Twin (DT), where the DT serves as a one-to-one virtual replica of the CIoT device, functioning as a “brain” for virtual simulation, data analysis, and intelligent decision-making. We deploy the DT in the drones, which act as aerial computing servers, providing a flexible, scalable, and cost-effective solution, particularly in areas with limited infrastructure. However, selecting reliable drones and designing appropriate incentive mechanisms remain challenging. To address these issues, we propose a hierarchical game-theoretic framework. First, we develop a reputation evaluation model based on the Theory of Planned Behavior (TPB) and the subjective logic model, followed by a coalition game approach to select reliable drones. Under conditions of information asymmetry, we then design a contract theory-based incentive mechanism to encourage drone participation in DT task execution. The Age of Information (AoI) metric is used to ensure the freshness of DT tasks. Numerical results demonstrate the effectiveness of the proposed hierarchical game-theoretic framework and reputation evaluation scheme.

Federated learning (FL) is a novel paradigm for distribute edge intelligence for the Internet-of-Vehicles (IoV) application, which can enable superior performance in model training without the need to share local data. However, in the actual architecture of FL, the existence of nonindependent and identically distributed (non-IID) data at the edge device, along with the involvement of randomly participating distributed nodes, can result in model bias and a subsequent decrease in overall performance. To solve this problem, a new federated self-supervised learning method based on prototypes clustering contrastive learning (FedPCC) is proposed, which can effectively addresses the issue of asynchronous edge training and global model bias by introducing an unsupervised prototypes layer. The prototypes layer maps edge features to a global space and performs clustering, facilitating the new aggregation method of global prototypes on the server. Then, models from other components are aggregated based on data weight. Besides that, during the parameter deployment phase, we replace the prototype layer to acquire global knowledge, while employing momentum updates to preserve the local knowledge of the other components. Finally, to assess the efficacy of our proposed approach, we carried out comprehensive experiments across the various data sets. The findings show that our method gains state-of-the-art performance, which also validates its effectiveness.

Internet of things (IoT) is crucial for the hierarchical medical system, which enables the real-time monitoring and collection of data, thereby improving patient treatment outcomes. However, achieving secure, efficient, timely, and controllable medical IoT data sharing between higher-lever hospital (HLH) and lower-level hospital (LLH) is a challenging task for the hierarchical medical system. Consortium blockchain, which is an effective way to achieve secure and trustworthy data sharing, has the potential to address these issues. In this article, we propose a novel cloud-chain sharing scheme for medical IoT data based on consortium blockchain. In this scenario, HLH and LLH establish a consortium blockchain, where medical IoT data is stored both on-chain and off-chain. On-chain data adopt a proxy re-encryption based on elliptic curve cryptography (ECC-PRE) strategy and attribute-based strategy to facilitate secure access and controlled sharing of data. Off-chain data sharing provides three different modes, namely, private data collection (PDC), direct channel (DC), and cloud storage (CS), according to the urgency of patient and the sensitivity of the data. Furthermore, a file security breakpoint resume scheme, rooted in the consortium blockchain, and a file weighting strategy are employed to enhance the efficiency and timeliness of data sharing. Finally, the security and performance of our proposed scheme are verified, and the results demonstrate that our scheme is secure, feasible, and efficient.

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing demand for the Internet of Medical Things (IoMT) integration. The advent of generative artificial intelligence (GenAI) has further elevated multimodal large language models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can handle multimodal inputs and generate different kinds of data by utilizing large-scale training on massive multimodal datasets. Nevertheless, significant challenges remain in developing medical MLLMs, especially security and data freshness concerns, which impact the quality of MLLM outputs. To this end, this article proposes a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. The proposed framework enables secure data training by utilizing a hierarchical cross-chain design. Furthermore, it improves the output quality of MLLMs by using hybrid RAG that filters different unimodal RAG results using multimodal metrics and integrates these retrieval results as additional inputs for MLLMs. Furthermore, we utilize the age of information (AoI) to indirectly assess the influence of data freshness on MLLMs and apply contract theory to motivate healthcare data stakeholders to disseminate their current data, thereby alleviating information asymmetry in the data-sharing process. Finally, we employ a generative diffusion model-based deep reinforcement learning (DRL) technique to find the optimal contract for efficient data sharing. Numerical results show the effectiveness of the proposed approach in achieving secure and efficient healthcare data management.

The rapid advancement of Internet of Everything (IoE) technologies has transformed the landscape of urban mobility management, necessitating innovative approaches to optimize traffic flow and reduce congestion. This article presents the dynamic multisemantic graph attention network (DMSGAT), leveraging real-time IoE data to construct dynamic spatial graphs that capture complex spatial dependencies across multiple semantic layers. These graphs are processed through the multisemantic graph attention module, which dynamically learns and integrates information from semantic relationships, ensuring accurate spatial modeling. Simultaneously, the temporal module, incorporating a self-forgetting residual connection network with temporal convolution, effectively models temporal dependencies, allowing the system to adapt to the dynamic nature of traffic flows. Additionally, we extend the model for adaptive traffic control by integrating reinforcement learning (RL), namely dynamic multisemantic graph attention with RL (DMSGARL). The integrated RL module further optimizes traffic control strategies in real time, responding to the continuous influx of data from IoE devices. Experimental results demonstrate that the proposed DMSGAT framework significantly improves traffic prediction accuracy and efficiency in urban mobility scenarios. The integrated RL DMSGARL model shows significant throughput improvements, offering a robust solution for smart city traffic management.

Smart grids, powered by Industrial Internet of Things (IIoT) technologies, enable real-time monitoring and intelligent energy management, promoting carbon reduction and sustainability. However, integrating distributed energy resources like solar, wind, and prosumers introduces challenges such as mismatches in production and consumption, grid imbalances, and energy losses. Centralized, static supply-demand balancing approaches struggle to address these complexities in carbon-aware intelligent IIoT. This paper presents a Cooperative Distributed Multi-Agent System (CD-MAS) for Smart Grid IIoT, fostering decentralized, real-time collaboration among producers, distributors, consumers, and prosumers. The system leverages self-optimizing agents to enhance carbon efficiency and grid resilience. Key features include consumer profile analysis to create adaptive Virtual Neighborhoods (VNs) for targeted energy programs and distributed energy prediction for accurate demand forecasting and demand-supply balancing with localized load-shedding strategies. The proposed CD-MAS advances smart grid capabilities, optimizing energy use and achieving net-zero carbon goals.

During labor, transperineal ultrasound imaging can acquire real-time midsagittal images, through which the pubic symphysis and fetal head can be accurately identified, and the angle of progression (AoP) between them can be calculated, thereby quantitatively evaluating the descent and position of the fetal head in the birth canal in real time. However, current segmentation methods based on convolutional neural networks (CNNs) and Transformers generally depend heavily on large-scale manually annotated data, which limits their adoption in practical applications. In light of this limitation, this paper develops a new Transformer-based Semi-supervised Segmentation Network (TransSeg). This method employs a Vision Transformer as the backbone network and introduces a Channel-wise Cross Attention (CCA) mechanism to effectively reconstruct the features of unlabeled samples into the labeled feature space, promoting architectural innovation in semi-supervised segmentation and eliminating the need for complex training strategies. In addition, we design a Semantic Information Storage (S-InfoStore) module and a Channel Semantic Update (CSU) strategy to dynamically store and update feature representations of unlabeled samples, thereby continuously enhancing their expressiveness in the feature space and significantly improving the model's utilization of unlabeled data. We conduct a systematic evaluation of the proposed method on the FH-PS-AoP dataset. Experimental results demonstrate that TransSeg outperforms existing mainstream methods across all evaluation metrics, verifying its effectiveness and advancement in semi-supervised semantic segmentation tasks.

Interest in Intelligent Transportation Systems (ITS) has increased significantly with the development of 6G. Owning an extremely high transmission speed, 6G is able to support low-latency service for edge-intelligence applications by Machine Learning(ML) techniques. However, traditional centralized learning is not suitable for this scenario due to the requirement for users to upload local data to the server, which can compromise data privacy. To overcome this challenge, Federated Learning (FL) and Split Learning (SL), as progressive distributed learning techniques, have been proposed as a solution. They enable the training of ML models while preserving data privacy. However, conventional FL has poor convergence when data heterogeneity occurs, also fails to meet personalized demands. To address these issues, We propose a novel personalized Federated Learning(pFL) framework, which trains models in SL and collaborates in FL. It offers a personalized solution for each client while retaining a global solution for newcomers. Experimental results demonstrate that our method outperforms other advanced baselines on benchmark datasets.

With the widespread adoption of unmanned aerial vehicles (UAVs) in various applications (e.g., aerial transportation, traffic monitoring), there have been apprehensions regarding the associated risks of employing UAVs in both civilian and military contexts, including concerns about privacy infringement, safety issues, and security threats. Although several methods have been proposed to detect UAVs, a pressing open challenge is posed by varying adverse weather conditions that could degrade the performance of many existing methods. To address these limitations, this work proposes a YOLO-based (You Only Look Once) novel model,YOLO-RAWthat exhibits improved performance in adverse weather conditions and considers different scales of UAVs. Furthermore, to facilitate a more comprehensive evaluation of the proposed model’s effectiveness in UAV detection, we have curated a complex background dataset and introduced three distinct test sets affected by adverse weather conditions. These three test sets comprise the Rainy Test Set (RTS), the AWGN (Additive White Gaussian Noise) Test Set (ATS), and the Motion Blurred Test Set (MBTS). The comprehensive experiments demonstrate the effectiveness of the proposed YOLO-RAW model over its counterparts in detecting UAVs under adverse conditions. The code and datasets could be found at: https://github.com/AdnanMunir338/YOLO-RAW.

The emergence of 6G networks enhances the speed and compatibility of Internet-of-Things (IoT) devices in vehicular ad hoc networks (VANETs), leveraging underutilized bands to improve wireless communication and security, though its adaptability may introduce cyber vulnerabilities; to address this, we propose an energy-efficient consortium-based blockchain-enabled heterogeneous (EBH) 6G network for IoT devices, offering secure VANET control through a lattice-based ring signcryption scheme that ensures timely message relaying while preserving vehicle anonymity and cloud data confidentiality, with blockchain blocks formed via secure peer nodes and service provider data; our protocol’s security was rigorously validated through analysis and Python-based implementation, achieving 42.1 ms computational cost and 1026-bit communication overhead, and proving effectiveness across varying block and transaction loads, while guaranteeing key security properties-anonymity, linkable privacy, unforgeability, and confidentiality-even under quantum threats, using lattice-based cryptography, Zero-Knowledge Proofs (ZKP), and blockchain immutability.

Digital twins-enabled transportation cyber-physical systems are employed in the transportation sector to enhance environmental quality, mobility, and safety. They are digital representations of transportation networks, enable the simulation of these networks’ behavior under various conditions. They can be employed in various transportation sectors, including forecasting traffic congestion, facilitate the optimization of flow and safety, enhance the efficiency of public transportation, improve the efficiency of freight transportation by offering support in this process, facilitates the consideration of future multimodal infrastructure development requirements, furnish drivers with up-to-date information about weather alerts, road closures, and traffic situations in real time, assess numerous aspects like, impacts of various mobility operators, transport users, and environmental conditions. However, the real-time data synchronization in digital twins-enabled transportation cyber-physical systems is accomplished using an open communication channel. Regrettably, the utilization of virtual-reality synthesizing security threats in the network necessitates the implementation of stringent privacy and security procedures, including authentication, encryption, and signature approaches. This study proposes a quantum-key-distribution (QKD)-based authentication protocol for secure communication of digital twins-enabled transportation cyber-physical systems. The integrated quantum in the system ensures the compactness and verifiability of data. The protocol’s security is examined using the Scyhter tool and is verified to be secure by informal security analysis. The proposed scheme achieves its efficiency over current solutions. Moreover, the latest technology and techniques are used to examine the operational capabilities and security features.

The Internet of Vehicles (IoV) enables technology that allows IoV and vehicles to connect everything. IoV has become an essential component of modern life. This exponential growth of IoV technology has introduced significant security and privacy issues, which pose potential threats to different types of attacks and cause different threats to the normal operation of vehicles. To prevent intelligent vehicle accidents and identify malicious attacks within IoV networks, various researchers have focused on machine learning (ML)-based methods to detect attacks. Intrusion detection systems (IDS) are a prominent solution for cyber attacks in IoV using ensemble learning. To achieve higher accuracy and detection rate, designing an improved detection framework using ensemble learning is a challenging task. The design of an ensemble-based IDS depends on two main challenges: selecting base classifiers and their combination methods. Therefore, in this study, we propose a hybrid ML model to detect various attacks in IoV. We have used different ML algorithms to develop an enhanced algorithm that can efficiently detect attacks in IoV networks. To evaluate the performance of the proposed system, we have used two well-known datasets, (CIC-IDS2017) and (UNSW-NB15). The proposed algorithm shows outstanding performance from the performance results, with an average attack detection accuracy of 99.75% and 100% and an F1 score of 99.74% and 100%, respectively, for both datasets. Further performance scores, that is, recall, precision, and F1 score metrics, validate the exceptional effectiveness of the proposed framework.

This study integrates traditional Chinese ink-and-wash painting with multimedia technology to create immersive ink-and-wash landscape animations. The study explores the psychological intervention effects of these animations on individuals experiencing anxiety and depression. Based on an interdisciplinary approach, a qualitative research methodology is employed, examining the fundamental theories and key issues of art therapy and patient psychology within the context of art therapy. A mixed-methods design was adopted, utilizing a specialized hospital as the implementation site for case studies. In-depth interviews were conducted with 15 patients, and 3D virtual reality (VR) ink-and-wash animations were developed based on their needs. A survey questionnaire was administered to 50 participants, and the results demonstrate that the application of immersive ink-and-wash animations in art therapy exhibits positive therapeutic effects. Among the 50 participants, anxiety scores decreased from 10.26 to 7.58 (a 26.1% reduction) after the intervention, and depression scores decreased from 11.14 to 8.36 (a 25.0% reduction), significantly improving their emotional states. Additionally, patients generally provided positive feedback on the natural imagery, traditional elements, and soft color tones within the ink-and-wash animations. Notably, middle-aged and elderly groups showed the most significant effects in terms of emotional regulation and psychological comfort. This study demonstrates that immersive art scenes, which merge traditional Chinese ink-and-wash aesthetics with digital multimedia technology, can effectively alleviate anxiety and depression symptoms. These scenes possess high emotional regulation value and cross-population adaptability, offering an innovative pathway for digital art therapy within an Eastern cultural context.

AI has numerous applications in virtual reality (VR), particularly in analyzing the progress of its implementation, identifying existing limitations, and determining future development paths. This study finds that AI’s main contributions to VR are in four key areas: creating intelligent virtual characters, advancing education and training, providing medical assistance, and generating dynamic scenes. This interdisciplinary convergence also brings significant opportunities, particularly in sectors such as education, healthcare, gaming, and corporate training. Additionally, the study discusses technical challenges related to computational costs, real-time feedback, user privacy, and algorithmic ethics. Critical challenges persist in data processing bottlenecks, privacy protection issues, and user adaptation. While AI enhances VR’s intelligence and interactivity, breakthroughs are still needed in cross-modal integration, privacy, security, and user experience. Future advancements in deep learning and reinforcement learning may unlock unlimited potential for AI-driven VR in personalized adaptation and immersive interaction. Therefore, this study provides a comprehensive analysis of AI–VR integration, providing valuable insights for academic research and technological development.

Action quality assessment (AQA) has become crucial in video analysis, finding wide applications in various domains, such as healthcare and sports. A significant challenge faced by AQA is the background bias due to the dominance of the background in videos. Especially, the background bias tends to overshadow subtle foreground differences, which is crucial for precise action evaluation. To address the background bias issue, we propose a novel data augmentation method named Scaled Background Swap. First, the background regions between different video samples are swapped to guide models focus toward the dynamic foreground regions and mitigate its sensitivity to the background during training. Second, the video’s foreground region is upscaled to further enhance models’ attention to the critical foreground action information for AQA tasks. In particular, the proposed Scaled Background Swap method can effectively improve models’ accuracy and generalization by prioritizing foreground motion and swapping backgrounds. It can be flexibly applied with various video analysis models. Extensive experiments on AQA benchmarks demonstrate that Scaled Background Swap method achieves better performance than baselines. Specifically, the Spearman’s rank correlation on datasets AQA-7 and MTL-AQA reaches 0.8870 and 0.9526, respectively. The code is available at: https://github.com/Emy-cv/Scaled-Background Swap.

Internet of Things (IoT) is crucial for the hierarchical medical system, and enables the real-time monitoring and collection of data, thereby improving patient treatment outcomes. However, achieving secure, efficient, timely, and controllable medical IoT data sharing between higher-lever hospital (HLH) and lower-level hospital (LLH) is a challenging task for the hierarchical medical system. Consortium blockchain, which is an effective way to achieve secure and trustworthy data sharing, has the potential to address these issues. In this article, we propose a novel cloud-chain sharing scheme for medical IoT data based on consortium blockchain. HLH and LLH establish a consortium blockchain, where medical IoT data is stored both on-chain and off-chain. On-chain data adopt a proxy re-encryption based on elliptic curve cryptography (ECC-PRE) strategy and attribute-based strategy to facilitate secure access and controlled sharing of data. Off-chain data sharing provides three different modes: private data collection (PDC), direct channel, and cloud storage (CS), according to the urgency of patient and the sensitivity of the data. Furthermore, a file security breakpoint resume scheme, rooted in the consortium blockchain, and a file weighting strategy are employed to enhance the efficiency and timeliness of data sharing. Finally, the performance of our proposed solution is verified by experimental results, and the results demonstrate our solution is feasible and efficient. In future work, we plan to use searchable encryption technology to make this scheme more versatile and gradually implement dynamic adjustment of permissions.

Abstract Many academics are interested in content‐based image retrieval techniques like image segmentation. In computer vision, the most popular method for segmenting a digital image into different parts is known as image segmentation. We assigned the artificially intelligent algorithm to the image's critical areas by modeling human features in specific regions. In order to detect the object and identify the key parts in the ‘RGB’ photographs, we combined scenes based on a colour and depth map, or ‘RGB‐D’, and used cosine modulated filter bank (CMFB), which conducts cross‐scale extraction of joint features from the images during feature extraction. The proposed ‘CMFB’ combines the discovered collaborative elements with the discovered supplementary data. The features in multi‐scale images is combined using fusion blocks with the goal of producing additional features (FB). Then, a saliency mapping calculation is made for the loss linked to two blocks. The suggested ‘CMFB’ is tested with the aid of five data sets, and it is shown that, the proposed ‘CMFB’ outperforms other conventional techniques.

Abstract The threat of side‐channel attacks poses a significant risk to the security of cryptographic algorithms. To counter this threat, we have designed an AES system capable of defending against such attacks, supporting AES‐128, AES‐192, and AES‐256 encryption standards. In our system, the CPU oversees the AES hardware via the AHB bus and employs true random number generation to provide secure random inputs for computations. The hardware implementation of the AES S‐box utilizes complex domain inversion techniques, while intermediate data is shielded using full‐time masking. Furthermore, the system incorporates double‐path error detection mechanisms to thwart fault propagation. Our results demonstrate that the system effectively conceals key power information, providing robust resistance against CPA attacks, and is capable of detecting injected faults, thereby mitigating fault‐based attacks.

Abstract Pose estimation is a computer vision task used to detect and estimate the pose of a person or an object in images or videos. It has some challenges that can leverage advances in computer vision research and others that require efficient solutions. In this paper, we provide a preliminary review of the state‐of‐the‐art in pose estimation, including both traditional and deep learning approaches. Also, we implement and compare the performance of Hand Pose Estimation (HandPE), which uses PoseNet architecture for hand sign problems, for an ASL dataset by using different optimizers based on 10 common evaluation metrics on different datasets. Also, we discuss some related future research directions in the field of pose estimation and explore new architectures for pose estimation types. After applying the PoseNet model, the experiment results showed that the accuracy achieved was 99.9%, 89%, 97%, 79%, and 99% for the ASL alphabet, HARPET, Yoga, Animal, and Head datasets, comparing those with common optimizers and evaluation metrics on different dataset.

Artificial intelligence of vehicles (AIVs) is poised to revolutionize transportation by promoting low-carbon alternatives, such as electric vehicles (EVs). However, the deployment of fixed charging stations (FCSs) lags behind the growing demand, particularly in rural areas, causing range anxiety among potential EV owners. This article proposes a smart transportation solution within the Artificial Intelligence of Things (AIoT) framework to establish a sustainable, low-carbon system. AIoT systems enable real-time data acquisition and analysis through extensive embedded IoT EV sensors and communication networks for pattern recognition and decision making on the cloud. The proposed solution integrates sensor information from vehicle-to-vehicle (V2V) charging, smart home charging stations (HCSs), and mobile charging services (MCSs), coordinated by the cloud-fog nodes in geographically distributed zones. This article employs the Hungarian matching algorithm for optimal decision making of matching EVs with charging services. Our approach incorporates AIV and AIoT technologies to enhance decision making by using an ensemble-based machine learning (ML) model for precise EV range estimation. The comprehensive details and specifications of these proposed models are elaborated in this article.

Vehicular Metaverses represent emerging paradigms arising from the convergence of vehicle road cooperation, Metaverse, and augmented intelligence of things. Users engaging with Vehicular Metaverses (VMUs) gain entry by consistently updating their Vehicular Twins (VTs), which are deployed on RoadSide Units (RSUs) in proximity. The constrained RSU coverage and the consistently moving vehicles necessitate the continuous migration of VTs between RSUs through vehicle road cooperation, ensuring uninterrupted immersion services for VMUs. Nevertheless, the VT migration process faces challenges in obtaining adequate bandwidth resources from RSUs for timely migration, posing a resource trading problem among RSUs. In this paper, we tackle this challenge by formulating a game-theoretic incentive mechanism with multi-leader multi-follower, incorporating insights from social-awareness and queueing theory to optimize VT migration. To validate the existence and uniqueness of the Stackelberg Equilibrium, we apply the backward induction method. Theoretical solutions for this equilibrium are then obtained through the Alternating Direction Method of Multipliers (ADMM) algorithm. Moreover, owing to incomplete information caused by the requirements for privacy protection, we proposed a multi-agent deep reinforcement learning algorithm named MALPPO. MALPPO facilitates learning the Stackelberg Equilibrium without requiring private information from others, relying solely on past experiences. Comprehensive experimental results demonstrate that our MALPPO-based incentive mechanism outperforms baseline approaches significantly, showcasing rapid convergence and achieving the highest reward.

The increasing prevalence of malicious activities in Internet of Things (IoT)-enabled healthcare and Internet of Medical Things (IoMT) systems necessitates robust intrusion detection mechanisms. This article introduces a novel approach combining meta-heuristic optimization and machine learning techniques to analyze network traffic for enhanced detection accuracy. Our proposed method utilizes 11 chaotic maps and the K-nearest neighbor (KNN) algorithm to identify malicious activity in IoMT and IoT network systems. Recognizing the significance of feature selection in network traffic intrusion detection, we employ the chaotic grey wolf optimizer (CGWO) to select the most relevant and impactful features for learning strategically. Our approach demonstrates superior performance through comprehensive experiments compared to well-known meta-heuristic algorithms and prior art methods, as evidenced by various evaluation metrics. This research contributes to advancing intrusion detection systems in healthcare IoMT and IoT, offering a reliable and efficient solution to safeguard against evolving cyber threats.

The Internet of Medical Things (IoMT) is a compelling networking paradigm integrating wireless communications sensors, connected devices, and embedded computing technologies. The IoMT involves the collection of real-time health data using sophisticated medical sensors. In recent years, the IoMT has become increasingly significant within the broader context of the Internet of Things (IoT). It provides accessibility for health monitoring and poses security obstacles to safeguarding the confidentiality and privacy of patient data. Therefore, this article presents a blockchain-integrated quantum authentication scheme in sensor-assisted IoMT networks. The proposed concept utilizes blockchain technology to achieve efficient patient authentication without the need for third-party entities. In addition, a secure quantum authentication scheme is designed not to require patients to authenticate themselves when communicating with multiple doctors simultaneously. This protocol explicitly addresses how clinicians can misuse their professional roles toward patients in IoMT networks. An evaluation analysis assesses the proposed technique’s efficacy compared to existing authentication schemes. The performance analyses demonstrate that the proposed protocol is resilient against various security attacks. Also, the practical usability of the quantum authentication scheme proved its importance as a significant improvement in communication security for IoMT networks.

Immersive service caching, based on the intelligent edge cloud, can meet delay-sensitive service requirements. Although numerous service caching solutions for edge clouds have been designed, they have not been well explored. Moreover, to the best of our knowledge, there is no work to consider the immersive service caching scheme under the supply of renewable energy. In this article, we investigate the service caching problem under the renewable energy supply to minimize service latency while making full use of renewable energy. Specifically, we formulate the service caching and renewable energy harvesting problem, which considers the dynamic renewable energy, unknown service requests, and limited capacity of the edge cloud. To solve this problem, we propose an effective algorithm, called OSCRE. Our algorithm first uses Lyapunov optimization to convert the time-average problem into time-independence optimization and thus realizes optimal renewable energy harvesting. Then, it realizes the service caching scheme using data-driven combinatorial multi-armed bandit learning. The simulation results show that the OSCRE scheme can save service latency while making sufficient use of renewable energy.

The precise reconstruction of accelerated magnetic resonance imaging (MRI) brings about notable advantages, such as enhanced diagnostic precision and decreased examination costs. In contrast, traditional cardiac MRI necessitates repetitive acquisitions across multiple heartbeats, resulting in prolonged acquisition times. Significant strides have been made in accelerating MRI through deep learning-based reconstruction methods. However, these existing methods encounter certain limitations: (1) The intricate nature of heart reconstruction involving multiple complex time-series data poses a challenge in exploring nonlinear dependencies between temporal contexts. (2) Existing research often overlooks weight sharing in iterative frameworks, impeding the effective capturing of non-local information and, consequently, limiting improvements in model performance. In order to improve cardiac MRI reconstruction, we propose a novel temporal-spatial transformer with a strategy in this study. Based on the multi-level encoder and decoder transformer architecture, we conduct multi-level spatiotemporal information feature aggregation over several adjacent views, that create nonlinear dependencies among features and efficiently learn important information among adjacent cardiac temporal frames. Additionally, in order to improve contextual awareness between neighboring views, we add cross-view attention for temporal information fusion. Furthermore, we introduce an iterative strategy for training weights during the reconstruction process, which improves feature fusion in critical locations and reduces the number of computations required to calculate global feature dependencies. Extensive experiments have demonstrated the substantial superiority of this procedure over the most advanced techniques, suggesting that it has broad potential for clinical use.

Existing magnetic resonance imaging translation models rely on generative adversarial networks, primarily employing simple convolutional neural networks. Unfortunately, these networks struggle to capture global representations and contextual relationships within magnetic resonance images. While the advent of Transformers enables capturing long-range feature dependencies, they often compromise the preservation of local feature details. To address these limitations and enhance both local and global representations, we introduceDBGAN, a novel dual-branch generative adversarial network. In this framework, the Transformer branch comprises sparse attention blocks and dense self-attention blocks, allowing for a wider receptive field while simultaneously capturing local and global information. The convolutional neural network branch, built with integrated residual convolutional layers, enhances local modeling capabilities. Additionally, we propose a fusion module that cleverly integrates features extracted from both branches. Extensive experimentation on two public datasets and one clinical dataset validates significant performance improvements with DBGAN. On Brats2018, it achieves a 10% improvement in MAE, 3.2% in PSNR, and 4.8% in SSIM for image generation tasks compared to RegGAN. Notably, the generated MRIs receive positive feedback from radiologists, underscoring the potential of our proposed method as a valuable tool in clinical settings.

Sentiment and sarcasm are intimate and complex, as sarcasm often deliberately elicits an emotional response in order to achieve its specific purpose. Current challenges in multi-modal sentiment and sarcasm joint detection mainly include multi-modal representation fusion and the modeling of the intrinsic relationship between sentiment and sarcasm. To address these challenges, we propose a single-input stream self-adaptive representation learning model (SRLM) for sentiment and sarcasm joint recognition. Specifically, we divide the image into blocks to learn its serialized features and fuse textual feature as input to the target model. Then, we introduce an adaptive representation learning network using a gated network approach for sarcasm and sentiment classification. In this framework, each task is equipped with its dedicated expert network responsible for learning task-specific information, while the shared expert knowledge is acquired and weighted through the gating network. Finally, comprehensive experiments conducted on two publicly available datasets, namely Memotion and MUStARD, demonstrate the effectiveness of the proposed model when compared to state-of-the-art baselines. The results reveal a notable improvement on the performance of sentiment and sarcasm tasks.

The emergence of mobile edge computing (MEC) technology has deployed edge clouds with strong computing capabilities closer to Internet of Thing (IoT) devices, which can effectively meet the demands for computing power and latency. However, in addition to the stringent latency requirements, more and more emerging IoT applications also have higher standards for the freshness and timeliness of collected information. In order to ensure the freshness and high-information value in IoT system, we propose an Age of Information (AoI)-based optimization strategy for computation offloading and transmission scheduling. The strategy considers the AoI during the transmission phase and the execution phase, respectively, under the constraints of delay and remaining energy. Then, a joint optimization model is established based on the comprehensive benefits of AoI and computation rate. To address the strong coupling between the offloading decision and the transmission decision, the original optimization problem is divided into two stages. By the use of the deep deterministic policy gradient (DDPG) algorithm and the dueling double deep$Q$network (D3QN) algorithm, the solution is obtained in terms of the offloading decision and transmission scheduling decision, respectively. The proposed joint optimization strategy considers the impact of the transmission decision on the offloading decision and is adaptable to the dynamic changes in the channel connection between the edge cloud and the user due to user mobility. Experimental results show that compared with other offloading and transmission strategies, the proposed approach has higher overall system revenue and lower AoI.

With the rapid progress of wireless communication technologies along with their digital revolutions, the quantity of the Internet of Things (IoT) has been increased by manifolds, resulting in a huge increase in data volume and network traffic. It became easier for an intruder to pretend as a valid service provider, and generate different types of network attacks. This becomes even more severe when the service involves digital financial transactions for possible urbanization. This article proposes an intrusion detection system (IDS) based on a stacked autoencoder (AE) and a deep neural network (DNN). The stacked AE learns the features of the input network record in an unsupervised manner to decrease the feature width. Then, the DNN is trained in a supervised manner to extract deep-learned features for the classifier. In the proposed system, the stacked AE has two latent layers and the DNN has two or three layers, where each layer has a fully connected layer, a batch normalization, and a dropout. The system was evaluated on three publicly available data sets: 1) KDDCup99; 2) NSL-KDD; and 3) aegean Wi-Fi intrusion data sets. Experimental results exhibited that the proposed IDS achieved 94.2%, 99.7%, and 99.9% accuracy, respectively, for multiclass classification.

Recent advancements of the Industrial Internet of Things (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contain rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time are challenging. Recent advancements in artificial intelligence (AI) allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this article, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. First, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real time. Second, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.

Network sustainability relies on many important parameters where the timely dissemination of information has a prime role to improve network operations and henceforth the network sustainability. Age of Information is a critical metric in many applications of future networks including smart transportation systems as these networks require fresh updates from the various network entities for the successful delivery of their services. This paper considers smart vehicles in a vehicle-to-infrastructure network where each vehicle has a stream of data for transmission to the roadside unit (RSU). The information from vehicles is collected when they enter the communication range of an RSU and stay within the coverage area of that RSU for a particular time. During this time, the RSU attempts to receive information from each vehicle as timely as possible. This paper proposes a hybrid access mechanism consisting of both orthogonal and non-orthogonal multiple access that schedules the transmission of packets from vehicles to the RSU where each vehicle has a finite length queue. The transmission of the packets is modeled using a Markov decision process, where a specific cost function is optimized to collect maximum information from the vehicles in a minimum amount of time.

Electric vehicles (EVs) are the backbone of the future intelligent transportation system (ITS). They are environmentally friendly and can also be integrated as distributed energy resources (DERs) into the smart grid using vehicle-to-grid (V2G) scheme. Specifically, utility companies can push back EV batteries into the electric grid to reduce the peak load. However, integrating EVs into the power grid efficiently requires accurate artificial intelligence (AI) mechanisms to forecast, coordinate, and dispatch the EVs into the grid. This paper proposes a Multi-agent Reinforcement Learning (MARL) mechanism that schedules the day-ahead discharging process of EV batteries to optimize the peak shaving performance of the electric grid. The proposed MARL overcomes the inaccuracy of energy prediction by allowing the agents, i.e. EVs, to make autonomous decisions. These agents are trained in a centralized fashion but make decisions locally to maintain autonomy and privacy. In particular, the model does not require that the EVs communicate with a centralized entity during the execution stage, which assures the model’s integrity and protects the EVs’ private information. To evaluate the model, a comprehensive series of experiments were carried out to prove the effectiveness of the MARL coordination and scheduling mechanism and to show that the model can indeed flatten the peak load.

The paper proposes light convolutional neural network (CNN) models for the use of cognitive networking in an intelligent transportation system (ITS). There are two CNN models, one with 1D convolution and connectors, and the other with a tree-like structure. The 1D CNN model is deployed to process 1D temporal data such as the driver’s body temperature and electrocardiogram (ECG) data to measure emotion, while the deep tree CNN model is used to process image data obtained from car camera sensors. As the driver’s cognitive state can frequently change depending on the situation and location of the car, different edge controllers should handle the car sensors’ data within a short period of time. The tree-based deep learning model that can be branched and processed independently in the edge devices can be executed with less computation. This reduces the load and the time of the execution of the model. The light 1D CNN model has less learnable parameters, and hence can be executed in real-time. The cognitive state of a driver is measured by the facial emotion, body temperature, and ECG signal of the driver. The proposed is tested using a publicly available facial emotion database, and the accuracy and the information density are around 94-96% and 4.4, respectively.

The vigorous development of positioning technology and ubiquitous computing has spawned trajectory big data. By analyzing and processing the trajectory big data in the form of data streams in a timely and effective manner, anomalies hidden in the trajectory data can be found, thus serving urban planning, traffic management, safety control and other applications. Limited by the inherent uncertainty, infinity, time-varying evolution, sparsity and skewed distribution of trajectory big data, traditional anomaly detection techniques cannot be directly applied to anomaly detection in trajectory big data. To solve this problem, we propose a hierarchical trajectory anomaly detection scheme for Intelligent Transportation Systems (ITS) using both machine learning and blockchain technologies. To be specific, a hierarchical federated learning strategy is proposed to improve the generalization ability of the global trajectory anomaly detection model by secondary fusion of the multi-area trajectory anomaly detection model. Then, by integrating blockchain and federated learning, the iterative exchange and fusion of the global trajectory anomaly detection model can be realized by means of on-chain and off-chain coordinated data access. Experiments show that the proposed scheme can improve the generalization ability of the trajectory anomaly detection model in different areas, while ensuring its reliability.

The future Intelligent Transportation System (ITS) will rely heavily on the advancement of the Internet of Things (IoT). Indeed, the IoT infrastructure paves the way toward connecting vehicles for various benefits including traffic monitoring, crowdsourcing, energy trading, and other ITS services. One of the use cases of the IoV is Vehicle-to-Vehicle (V2V) energy charging/discharging, which is expected to be an integral part of the ITS. In V2V paradigm, Electric Vehicles (EVs) with bidirectional chargers can communicate with a grid edge network or directly with another EV using low-power wide-area networks (LPWAN) or 5G wireless connection to offer or demand energy. V2V energy exchange allows EV owners to make money from selling their battery’s excess energy to other EVs. One of the main challenges for the wide adoption of V2V is the development of mechanisms that maximize the benefits for participants. In the V2V paradigm, energy providers and suppliers require mechanisms that ensure maximal matching for the optimal social welfare of the users. In this paper, we propose a double-sided auction mechanism that matches EVs by pairing bids and asks such that the traded volume and the utilities are maximized. Through theoretical analysis, we show that the proposed model can indeed be truthful, individually rationale, and computationally efficient. Finally, we evaluate the proposed model based on real data and provide performance analysis.

Traffic events are one of the main causes of traffic accidents, leading to traffic event detection being a challenging research problem in traffic management and intelligent transportation systems (ITSs). The main gap in this task lies in how to extract and represent the valuable information from various kinds of traffic data. Considering the important role that social networks play in traffic data analysis, we argue that sentiment classification and traffic event detection are two closely related tasks in ITSs, where event and sentiment can reveal both explicit and implicit traffic accidents, respectively. Unfortunately, none of the recent approaches in traffic event detection have taken sentiment knowledge into view. This paper proposes a multimodal coupled graph attention network (MCGAT). It aims to construct a multimodal multitask interactive graphical structure where terms (sucha as words, and pixels) are treated as nodes, and their contextual and cross-modal correlations are formalized as edges. The key components are cross-modal and cross-task graph connection layers. The cross-modal graph connection layer captures the multimodal representation, where each node in one modality connects all nodes in another modality. The cross-task graph connection layer is designed by connecting the multimodal node in one task to two single nodes in another task. Empirical evaluation of two benchmarking datasets, such as MGTES and Twitter, shows the effectiveness of the proposed model over state-of-the-art baselines in terms of F1 and accuracy, with significant improvements of 2.4%, 2.4%, 2.7%, and 2.7%.

Surveillance drones, called as unmanned aerial vehicles (UAVs), are aircrafts that are utilized to collect video recordings, still images, or live video of the targets, such as vehicles, people or specific areas. Particularly in battlefield surveillance, there is high possibility of eavesdropping, inserting, modifying or deleting the messages during communications among the deployed drones and ground station server (GSS). This leads to launch several potential attacks by an adversary, such as main-in-middle, impersonation, drones hijacking, replay attacks, etc. Moreover, anonymity and untraceability are two crucial security properties that need to be maintained in battlefield surveillance communication environment. To deal with such a crucial security problem, we propose a new access control protocol for battlefield surveillance in drone-assisted Internet of Things (IoT) environment, called ACPBS-IoT. Through the detailed security analysis using formal and informal (nonmathematical), and also the formal security verification under automated software simulation tool, we show that the proposed ACPBS-IoT can resist several potential attacks needed in a battlefield surveillance scenario. Furthermore, the testbed experiments for various cryptographic primitives have been performed for measuring the execution time. Finally, a detailed comparative study on communication and computational overheads, and security, as well as functionality features, reveals that the proposed ACPBS-IoT provides superior security and more functionality features, and better or comparable overheads than other existing competing access control schemes.

Internet of Things (IoT) produces massive heterogeneous data from various applications, including digital health, smart hospitals, automated pathology labs, and so forth. IoT sensor nodes are integrated with the medical equipment to enable the health workers to monitor the patients’ health condition and appliances in real time. However, due to security vulnerabilities, an unauthorized user can access health-related information or control the IoT nodes attached to the patient’s body resulting in unprecedented outcomes. Due to wireless channels as a medium of communication, IoT poses several threats such as a denial of service attack, man-in-the-middle attack, and modification attack to the IoT networks’ security and privacy. The proposed research presents a lightweight and anonymity-preserving user authentication protocol to counter these security threats. The given scheme establishes a secure session for the legitimate user and prohibits unauthorized users from gaining access to the IoT sensor nodes. The proposed protocol uses only lightweight cryptography primitives (hash) to alleviate the node’s tiny processor burden. The proposed protocol is efficient and superior because it has low computational and communication costs than conventional protocols. The proposed scheme uses password protection to let only the legitimate user access the IoT sensor nodes to obtain the patient’s real-time health report.

The Industrial Internet of Things (IIoT) is an emerging technology that can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. However, anomalies of IIoT devices might expose sensitive data about users of high authenticity and validity, resulting in security and privacy threats to the IIoT applications. That suggests the significance of anomaly detection executed by proper authorities. To address these problems, in this paper, we propose a reliable anomaly detection strategy for IIoT using federated learning. Specifically, we apply the federated learning technique to build a universal anomaly detection model with each local model trained by the deep reinforcement learning (DRL) algorithm. Since local data sets are not required during the federated learning, the chance of privacy leakage is reduced. In addition, by introducing privacy leakage degree and action relation to anomaly detection design, we can greatly improve the detection accuracy. The validation experiments indicate that the proposed strategy achieves high throughput, low latency, and high anomaly detection accuracy for privacy preservation in various IIoT scenarios.

With the rapid development of the Internet of Things (IoT), more and more data are generated by smart devices to support various edge services. Since these data may contain sensitive information, security and privacy of data aggregation has become a key challenge in IoT. To tackle this problem, a blockchain-based secure data aggregation strategy, namely (BSDA), is proposed for edge computing empowered IoT. Specifically, in order to restrict task receivers [i.e., mobile data collectors (MDCs)] to search and accept tasks, the block header is intergraded with a security label including task security level (SL) and task completion requirement. Accordingly, new block generation rules are developed to improve system performance in throughput and transaction latency. Furthermore, BSDA decomposes both sensitive tasks and task receivers into groups against privacy disclosure. On the other hand, a deep reinforcement learning method, the improved self-adaptive double bootstrapped deep deterministic policy gradient (IDDPG), is developed to design energy-efficient MDC routes under the constrains that the SLs of MDCs should be higher than the SLs of data aggregation tasks. Simulation results indicate that 1) as a privacy-preserving strategy, BSDA obtains high throughput and low transaction latency and 2) BSDA outperforms certain contemporary strategies in aggregation ratio and energy cost.

Text, radiological pictures, audio notes, video, and other types of multimedia healthcare data are all generated by today's smart healthcare system [1].The evolution of COVID-19 has resulted in an incremental rise in current healthcare data.The study of multimodal healthcare data on such a big scale has revealed both obstacles and potential.Thanks to artificial intelligence (AI) and, more specifically, deep learning (DL) algorithms, which have been widely used by researchers for handling massive amounts of epidemic data, predicting live epidemic crises, and initiating new research directions in the analysis of healthcare multimedia data [2].As a result, deep learning for multimedia healthcare data analysis is becoming a hot topic in multimedia and computer vision research.The call for papers attracted 54 submissions and after a rigorous review, 20 papers have been accepted for this special issue.A brief summary of papers in this special issue is presented in the following:The paper titled "A Novel Study for Automatic Twoclass Covid-19 Diagnosis (between Covid-19 and Healthy, Pneumonia) on X-ray Images using Texture Analysis and 2-D/3-D Convolutional Neural Networks" aims to diagnose COVID-19 early using X-ray images, automatic two-class classification was carried out in four different titles: COVID-19/Healthy, COVID-19 Pneumonia/Bacterial Pneumonia, COVID-19 Pneumonia/Viral Pneumonia, and COVID-19 Pneumonia/Other Pneumonia.In the study, besides using

Industrial Internet of Things (IIoT) devices generate and collect massive amounts of industrial data. Monetizing the flood of data generated by the IIoT devices has enabled the creation of the IIoT data ecosystem, where individuals and businesses may trade data. With the rapid expansion of the online data trading industry, the necessity for an edge intelligent reputation system is becoming increasingly important as more individuals and services connect online. In recent years, researchers have proposed blockchain-based reputation systems as a means of offering anonymity, security, transparency, and mutual trust for both providers and customers in Industry 4.0. Unfortunately, they focus on the decentralized reputation system with a single certificate authority, which creates the concern of a single point of failure (SPOF). Moreover, researchers paid little attention to the performance measures of these blockchain-based reputation systems to demonstrate their usability in a real IIoT data ecosystem. This article proposes a robust edge intelligent blockchain-based reputation system capable of avoiding failures by enhancing the Raft consensus mechanism. We provide extensive security analysis and simulation experiments to demonstrate the performance of the blockchain-based reputation system for the IIoT data ecosystem using different metrics, such as transaction throughput, latency, and resource consumption.

The smart power grid is a critical energy infrastructure where real-time electricity usage data is collected to predict future energy requirements. The existing prediction models focus on the centralized frameworks, where the collected data from various home area networks (HANs) are forwarded to a central server. This process leads to cybersecurity threats. This article proposes a federated learning based model with privacy preservation of smart grids data using serverless cloud computing. The model considers the blockchain-enabled dew servers in each HAN for local data storage and local model training. Advanced perturbation and normalization techniques are used to reduce the inverse impact of irregular workload on the training results. The experiment conducted on benchmarks datasets demonstrates that the proposed model minimizes the computation and communication costs, attacking probability, and improves the test accuracy. Overall, the proposed model enables smart grids with robust privacy preservation and high accuracy.

The fast-growing electric vehicles (EVs) industry requires a well-designed recommendation system to locate charging stations while ensuring private data protection. This article proposes a secure cloudlet-based recommendation system for EVs. Unlike conventional methods where training a recommender model involves direct data sharing between data holders, our model utilizes a secure vertical federated learning technique, in which EVs data do not leave the platforms. To improve the efficiency of the model and to alleviate the communication-related concerns in our recommender model, cloudlet-based data aggregator(s) are used as a replacement for the existing centralized architectures. To enhance the security of our system, blockchain technology is incorporated to generate a trusted network of cloudlets that are responsible for transmitting the locally computed training parameters. The simulation results achieved from our proposed recommendation system show that the distribution of EVs over a designated area with charging stations is more optimal, and the proposed decentralized recommender with 10 cloudlets is 5.2 s quicker than a conventional centralized model.

The development and maturity of the fifth-generation (5G) wireless communication technology provides the industrial Internet of Things (IIoT) with ultra-reliable and low-latency communications and massive machine-type communications, and forms a novel IIoT architecture, 5G-IIoT. However, massive data transfer between interconnecting industrial devices also brings new challenges for the 5G-IIoT routing process in terms of latency, load balancing, and data privacy, which affect the development of 5G-IIoT applications. Moreover, the existing research works on IIoT routing mostly focus on the latency and the reliability of the routing, disregarding the privacy security in the routing process. To solve these problems, in this article, we propose a quality of service (QoS) and data privacy-aware routing protocol, named QoSPR, for 5G-IIoT. Specifically, we improve the community detection algorithm info-map to divide the routing area into optimal subdomains, based on which the deep reinforcement learning algorithm is applied to build the gateway deployment model for latency reduction and load-balancing improvement. To eliminate areal differences, while considering the privacy preservation of the routing data, the federated reinforcement learning is applied to obtain the universal gateway deployment model. Then, based on the gateway deployment, the QoS and data privacy-aware routing is accomplished by establishing communications along the load-balancing routes of the minimum latencies. The validation experiment is conducted on real datasets. The experiment results show that as a data privacy-aware routing protocol, the QoSPR can significantly reduce both average latency and maximum latency, while maintaining excellent load balancing in 5G-IIoT.

Intelligent Manufacturing 5.0 of multipulse rectifier systems requires them to be optimized for a variety of use in transportation and factories producing hearty touch technology. The research presented in this article show advances of using heuristic models to set 12-pulse and 24-pulse rectifiers to work under low- and high-voltage load. As a result of heuristic optimization electric systems increase efficiency and reduce energy consumption by efficiency benefits in adopting artificial intelligence. Applied heuristic models helped in computer simulations to optimize system settings in a short time. Results show that optimized models are more efficient and our proposed approach is reducing voltage pulsation. As a result optimized system improves electromagnetic compatibility for beneficial use in modern industry and sensible human–machine cooperation.

Large-scale knowledge in the artificial intelligence of things (AIoT) field urgently needs effective models to understand human language and automatically answer questions. Pretrained language models achieve state-of-the-art performance on some question answering (QA) datasets, but few models can answer questions on AIoT domain knowledge. Currently, the AIoT domain lacks sufficient QA datasets and large-scale pretraining corpora. In this article, we propose RoBERTa$_{\mathrm AIoT}$to address the problem of the lack of high-quality large-scale labeled AIoT QA datasets. We construct an AIoT corpus to further pretrain RoBERTa and BERT. RoBERTa$_{\mathrm AIoT}$and BERT$_{\mathrm AIoT}$leverage unsupervised pretraining on a large corpus composed of AIoT-oriented Wikipedia webpages to learn more domain-specific context and improve performance on the AIoT QA tasks. To fine-tune and evaluate the model, we construct three AIoT QA datasets based on the community QA websites. We evaluate our approach on these datasets, and the experimental results demonstrate the significant improvements of our approach.

Heterogeneous networks (HetNets) are becoming a promising solution for future wireless systems to satisfy the high data rate requirements. This paper introduces a stochastic geometry framework for the analysis of the downlink coverage probability in a multi-tier HetNet consisting of a macro-base station (MBS) operating at sub-6 GHz, millimeter wave (mmWave)-enabled unmanned aerial vehicles (UAVs) operating at 28 GHz, and small BSs operating both at mmWave and THz frequencies. The analytical expressions for the coverage probability for each tier have been derived in the paper. Monte Carlo simulations are then performed to validate the analytical expressions. The effectiveness of the HetNet is analyzed on various performance metrics including association and coverage probabilities for different network parameters. We show that the mmWave and THz-enabled cells provide significant improvement in the achievable data rates because of their high available bandwidths, however, they have a degrading effect on the coverage probability due to their high propagation losses.

Underwater optical wireless communications (UOWCs) are promising and potential wireless carriers to envisage underwater phenomenal activities for various applications towards the futuristic 5G and beyond (5GB) wireless systems. The main challenges to deploy underwater applications are the physicochemical properties and strong turbulence channel conditions. In this regard, the end-to-end (E2E) performance analysis of a dual-hop mixed FSO/UVLC system under the intensity modulation/direct detection (IM/DD) technique in consideration of pulse amplitude modulation (PAM) scheme is investigated. Throughout this study, to tackle the issues of moderate-to-strong turbulence channel conditions, this work deploys the Gamma-Gamma (GG) distribution fading model and the links are designed by unifying plane wave models in the corresponding links, respectively. This investigation outperforms higher achievable data rate with minimal delay response and enhance network connectivity in real-time monitoring scenarios as compared with the traditional underwater wireless communication technologies. In more contrast, the probability distribution function (PDF), cumulative distribution function (CDF), and closed-form expression of the system are derived and presented in terms of Meijer-G function as well as Extended Generalized Bivariate Meijer-G Function (EGBMGF). The significant E2E performance metrics are obtained by employing the decode-and-forward (DF) relay protocol in hostile channel conditions. In aggregating this work, we combine the analytical expressions that present an efficient tool to depict the impact of channel parameters on the system. The simulation results are plausible of the system performance metrics as average BER (ABER) and outage probability$(P_{out})$in the presence of pointing and without pointing error events. Finally, in this work, we use the Monte-Carlo approach for the best fitting curves and validate the numerical expression yields simulation results.

introduction Share on Special Section on Edge-AI for Connected Living Editors: M. Shamim Hossain King Saud University, Saudi Arabia King Saud University, Saudi ArabiaView Profile , Changsheng Xu Chinese Academy of Sciences, China Chinese Academy of Sciences, ChinaView Profile , Josu Bilbao IKERLAN, Spain IKERLAN, SpainView Profile , Md. Abdur Rahman University of Prince Mugrin, KSA University of Prince Mugrin, KSAView Profile , Abdulmotaleb El Saddik University of Ottawa, Canada University of Ottawa, CanadaView Profile , Mohamed Bin Zayed University of Artificial Intelligence, UAE & University of Ottawa, Canada University of Artificial Intelligence, UAE & University of Ottawa, CanadaView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 22Issue 3August 2022 Article No.: 55epp 1–3https://doi.org/10.1145/3514196Published:14 March 2022Publication History 0citation176DownloadsMetricsTotal Citations0Total Downloads176Last 12 Months176Last 6 weeks24 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Green IoT primarily focuses on increasing IoT sustainability by reducing the large amount of energy required by IoT devices. Whether increasing the efficiency of these devices or conserving energy, predictive analytics is the cornerstone for creating value and insight from large IoT data. This work aims at providing predictive models driven by data collected from various sensors to model the energy usage of appliances in an IoT-based smart home environment. Specifically, we address the prediction problem from two perspectives. Firstly, an overall energy consumption model is developed using both linear and non-linear regression techniques to identify the most relevant features in predicting the energy consumption of appliances. The performances of the proposed models are assessed using a publicly available dataset comprising historical measurements from various humidity and temperature sensors, along with total energy consumption data from appliances in an IoT-based smart home setup. The prediction results comparison show that LSTM regression outperforms other linear and ensemble regression models by showing high variability ( R 2 ) with the training (96.2%) and test (96.1%) data for selected features. Secondly, we develop a multi-step time-series model using the auto regressive integrated moving average (ARIMA) technique to effectively forecast future energy consumption based on past energy usage history. Overall, the proposed predictive models will enable consumers to minimize the energy usage of home appliances and the energy providers to better plan and forecast future energy demand to facilitate green urban development.

No abstract available.

The recent booming of artificial intelligence (AI) applications, e.g., affective robots, human-machine interfaces, autonomous vehicles, and so on, has produced a great number of multi-modal records of human communication. Such data often carry latent subjective users’ attitudes and opinions, which provides a practical and feasible path to realize the connection between human emotion and intelligence services. Sentiment and emotion analysis of multi-modal records is of great value to improve the intelligence level of affective services. However, how to find an optimal manner to learn people’s sentiments and emotional representations has been a difficult problem, since both of them involve subtle mind activity. To solve this problem, a lot of approaches have been published, but most of them are insufficient to mine sentiment and emotion, since they have treated sentiment analysis and emotion recognition as two separate tasks. The interaction between them has been neglected, which limits the efficiency of sentiment and emotion representation learning. In this work, emotion is seen as the external expression of sentiment, while sentiment is the essential nature of emotion. We thus argue that they are strongly related to each other where one’s judgment helps the decision of the other. The key challenges are multi-modal fused representation and the interaction between sentiment and emotion. To solve such issues, we design an external knowledge enhanced multi-task representation learning network, termed KAMT. The major elements contain two attention mechanisms, which are inter-modal and inter-task attentions and an external knowledge augmentation layer. The external knowledge augmentation layer is used to extract the vector of the participant’s gender, age, occupation, and of overall color or shape. The main use of inter-modal attention is to capture effective multi-modal fused features. Inter-task attention is designed to model the correlation between sentiment analysis and emotion classification. We perform experiments on three widely used datasets, and the experimental performance proves the effectiveness of the KAMT model.

The new-generation information technology development enables Digital Twins to reshape the physical world into the virtual digital space and provide technical support for Metaverse construction. The Metaverse objects can be mesoscale or macro-micro-scales. Metaverse is a complex collection of both solid substances and liquid, gaseous, plasma, and other uncertain states. Additionally, Metaverse integrates the tangibles with social relations, such as interpersonal relations (friendship, love, and blood relations) and the overall social relations (ethics, morality, and law). This work also introduces some principles or laws to construct the Digital Twins model for social relations, such as broken windows theory, small-world phenomenon, survivor bias, and herd behavior. Thus, from multiple angles, it reviews mapping the tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

Summary Analyzing the correlation between two funds can help investors control investment risks and optimize investment portfolios, which has a strong guiding significance for fund investment in reality. Constructing an intelligent investment system with fund correlation analysis capabilities can help investors automatically make profits from financial markets. In previous research, many researchers have built intelligent investment systems using Bayesian networks, support vector machines (SVM), and LSTM models. However, the strong historical dependence between fund data and the high‐dimensional and high‐noise characteristics of fund data prevent traditional methods from obtaining excellent performance in fund analysis. This paper designs a deep learning‐based fund intelligent trading system‐DLIFT which has functions such as investment push, income prediction, and risk control. The systems data analysis module is implemented using the Improved RNN model. This model employed encoder‐decoder architecture. The encoder is responsible for analyzing the fund's feature, and the decoder is responsible for analyzing the dependency relationship between the historical correlation and the current correlation. LSTM and an attention mechanism are simultaneously applied to the encoder and decoder, which enabled the discovery of the implicit dependence of time series data. This article places the designed system on a historical dataset containing multiple public funds for verification. In specific experiments, the experimental results of the comparative experiments show the superiority of our model. At the same time, the results of the ablation experiment results show that LSTM and attention mechanism play critical role in the proposed system.

With the worldwide large-scale outbreak of COVID-19, the Internet of Medical Things (IoMT), as a new type of Internet of Things (IoT)-based intelligent medical system, is being used for COVID-19 prevention and detection. However, since the widespread use of IoMT will generate a large amount of sensitive information related to patients, it is becoming more and more important yet challenging to ensure data security and privacy of COVID-19 applications in IoMT. The leakage of private information during IoMT data fusion process will cause serious problems and affect people's willingness to contribute data in IoMT. To address these challenges, this article proposes a new privacy-enhanced data fusion strategy (PDFS). The proposed PDFS consists of four important components, i.e., sensitive task classification, task completion assessment, incentive mechanism-based task contract design, and homomorphic encryption-based data fusion. The extensive simulation experiments demonstrate that PDFS can achieve high task classification accuracy, task completion rate, task data reliability and task participation rate, and low average error rate, while improving the privacy protection for data fusion under COVID-19 application environments based on IoMT.

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming increasingly important. Furthermore, data collected by the edge device contain massive user's private data, which is challenging current detection approaches as user privacy has attracted more and more public concerns. With this focus, this article proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce an FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an attention mechanism-based convolutional neural network-long short-term memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based convolutional neural network units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of the long short-term memory unit in predicting time-series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top- k selection to improve communication efficiency. Extensive experimental studies on four real-world data sets demonstrate that our framework accurately and timely detects anomalies and also reduces the communication overhead by 50% compared to the FL framework that does not use the gradient compression scheme.

Future generation communication systems, such as 5G and 6G wireless systems, exploit the combined satellite-terrestrial communication infrastructures to extend network coverage and data throughput for data-driven applications. These ground-breaking techniques have promoted the rapid development of Internet of Things (IoT) in maritime industries. In maritime IoT applications, intelligent vessel traffic services can be guaranteed by collecting and analyzing high volume of spatial data flows from automatic identification system (AIS). This AIS system includes a highly integrated automatic equipment, including functionalities of core communication, tracking, and sensing. The increased utilization of shipboard AIS devices allows the collection of massive trajectory data. However, the received raw AIS data often suffers from undesirable outliers (i.e., poorly tracked timestamped points for vessel trajectories) during signal acquisition and analog-to-digital conversion. The degraded AIS data will bring negative effects on vessel traffic services (e.g., maritime traffic monitoring, intelligent maritime navigation, vessel collision avoidance, etc.) in maritime IoT scenarios. To improve the quality of vessel trajectory records from AIS networks, we propose to develop a two-phase data-driven machine learning framework for vessel trajectory reconstruction. In particular, a density-based clustering method is introduced in the first phase to automatically recognize the undesirable outliers. The second phase proposes a bidirectional long short-term memory (BLSTM)-based supervised learning technique to restore the timestamped points degraded by random outliers in vessel trajectories. Comprehensive experiments on simulated and realistic data sets have verified the dominance of our two-phase vessel reconstruction framework compared to other competing methods. It thus has the capacity of promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems.

The growing use of the Internet of Things (IoT) has increased the volume of data to be processed by manifolds. Edge computing can lessen the load of transmitting a massive volume of data to the cloud. It can also provide reduced latency and real-time experience to the users. This article proposes an emotion recognition system from facial images based on edge computing. A convolutional neural network (CNN) model is proposed to recognize emotion. The model is trained in a cloud during off time and downloaded to an edge server. During the testing, an end device such as a smartphone captures a face image and does some preprocessing, which includes face detection, face cropping, contrast enhancement, and image resizing. The preprocessed image is then sent to the edge server. The edge server runs the CNN model and infers a decision on emotion. The decision is then transmitted back to the smartphone. Two data sets, JAFFE and extended Cohn–Kanade (CK+), are used for the evaluation. Experimental results show that the proposed system is energy efficient, has less learnable parameters, and good recognition accuracy. The accuracies using the JAFFE and CK+ data sets are 93.5% and 96.6%, respectively.

Capturing psychological, emotional, and physiological states, especially during a pandemic, and leveraging the captured sensory data within the pandemic management ecosystem is challenging. Recent advancements for the Internet of Medical Things (IoMT) have shown promising results from collecting diversified types of such emotional and physical health-related data from the home environment. State-of-the-art deep learning (DL) applications can run in a resource-constrained edge environment, which allows data from IoMT devices to be processed locally at the edge, and performs inferencing related to in-home health. This allows health data to remain in the vicinity of the user edge while ensuring the privacy, security, and low latency of the inferencing system. In this article, we develop an edge IoMT system that uses DL to detect diversified types of health-related COVID-19 symptoms and generates reports and alerts that can be used for medical decision support. Several COVID-19 applications have been developed, tested, and deployed to support clinical trials. We present the design of the framework, a description of our implemented system, and the accuracy results. The test results show the suitability of the system for in-home health management during a pandemic.

Medical IoT devices are rapidly becoming part of management ecosystems for pandemics such as COVID-19. Existing research shows that deep learning (DL) algorithms have been successfully used by researchers to identify COVID-19 phenomena from raw data obtained from medical IoT devices. Some examples of IoT technology are radiological media, such as CT scanning and X-ray images, body temperature measurement using thermal cameras, safe social distancing identification using live face detection, and face mask detection from camera images. However, researchers have identified several security vulnerabilities in DL algorithms to adversarial perturbations. In this article, we have tested a number of COVID-19 diagnostic methods that rely on DL algorithms with relevant adversarial examples (AEs). Our test results show that DL models that do not consider defensive models against adversarial perturbations remain vulnerable to adversarial attacks. Finally, we present in detail the AE generation process, implementation of the attack model, and the perturbations of the existing DL-based COVID-19 diagnostic applications. We hope that this work will raise awareness of adversarial attacks and encourages others to safeguard DL models from attacks on healthcare systems.

Mobile-edge crowdsensing is capable of providing a large amount of data via pervasive mobile terminals for Industrial Internet of Things (IIoT). However, the generated data often contain users' sensitive information, which suggests the significance of privacy preserving in data aggregation and analysis for IIoT. Privacy preserving in mobile-edge crowdsensing have conflicting objectives, i.e., the edge fusion center (FC) requires data of better quality for data fusion with higher accuracy whereas participatory users (PUs) desire better privacy preserving by larger noise injection. Therefore, how to select proper noises to achieve the tradeoff between accuracy and privacy is a challenging problem. In addition, FC is subject to data tempering due to the lack of data reliability validations and incentive mechanisms. To tackle these problems, we propose a novel privacy-preserving mobile-edge crowdsensing strategy (PPCS) for IIoT. Specifically, PPCS provides a Kullback-Leibler privacy-preserving data aggregation using a reputation-based incentive mechanism. On the other hand, PPCS offers hypothesis test-based data reliability validation and PU's reputation update, which collaborate to ease the impact of tampered data. Meanwhile, a reinforcement learning algorithm, the expected Sarsa, is applied to obtain the optimal test threshold. Theoretical analysis and experimental results show that PPCS is an energy-efficient strategy and the data provided by PPCS has a better aggregation accuracy than certain baseline strategies.

The traditional natural gas Internet-of-Things (IoT) system has many problems, such as centralized management of resources, noncirculation of data between stations, insecurity of transaction information or account books, and lack of contract consensus. In order to ensure data security and reliable transaction, this article introduces artificial intelligence (AI) and blockchain technology and constructs an AI-enabled and blockchain-powered natural gas IoT system in a smart city. In this article, the natural gas output prediction model based on temporal pattern attention-based LSTMs (TPA-LSTMs) is used to enable the system to sense the change of natural gas deliverability. In addition, we establish a blockchain-based secure natural gas transaction scheme, which dynamically matches the purchase contract and sale contract to maximize the interests of the buyer and the seller and obtain a transaction contract. The experimental results show that our model can predict the output value of natural gas in real time and select the appropriate transaction matching scheme according to the dynamic demand for sales.

An electroencephalogram (EEG)-based remote pathology detection system is proposed in this study. The system uses a deep convolutional network consisting of 1D and 2D convolutions. Features from different convolutional layers are fused using a fusion network. Various types of networks are investigated; the types include a multilayer perceptron (MLP) with a varying number of hidden layers, and an autoencoder. Experiments are done using a publicly available EEG signal database that contains two classes: normal and abnormal. The experimental results demonstrate that the proposed system achieves greater than 89% accuracy using the convolutional network followed by the MLP with two hidden layers. The proposed system is also evaluated in a cloud-based framework, and its performance is found to be comparable with the performance obtained using only a local server.

Sixth generation (6G) network is a revolutionary technology to satisfy the ever-growing demands from the sustainable development of emerging industrial applications and services. Due to its high flexibility, convenient and rapid deployment, self-organization capability, and outstanding expansibility, network-in-box (NIB) represents a promising approach for future networks. The integration of NIB with 6G can lead to many new applications in geoscience, robotics, and industrial automation. For 6G-enabled NIB, services are deployed directly on the NIB, which increases the fault tolerance and reduces the traffic volume on the backhaul link. As more and more data are processed and shared in industrial applications and services, the security of data aggregation becomes a key challenge for 6G-enabled NIB. To address this challenge, in this article, we propose a blockchain based privacy-aware distributed collection (BPDC) oriented strategy for data aggregation. In BPDC, an improved blockchain with a new block header structure and two different block generation rules are designed and introduced, which restricts the task receivers to search and receive the tasks beyond their levels of security permission. While guaranteeing the data aggregation performance, BPDC can also achieve privacy protection by decomposing sensitive tasks and task receivers into multiple groups. Validation experiments show that the BPDC accomplishes low overhead, high throughput, and privacy preservation in various industrial applications.

The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked over wired or wireless channel for data transmission. IoT has grown rapidly over the past decade with more than 25 billion devices expected to be connected by 2020. The volume of data released from these devices will increase many-fold in the years to come. In addition to an increased volume, the IoT devices produces a large amount of data with a number of different modalities having varying data quality defined by its speed in terms of time and position dependency. In such an environment, machine learning (ML) algorithms can play an important role in ensuring security and authorization based on biotechnology, anomalous detection to improve the usability, and security of IoT systems. On the other hand, attackers often view learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from these, in this article, we propose the security of the IoT devices by detecting spam using ML. To achieve this objective, Spam Detection in IoT using Machine Learning framework is proposed. In this framework, five ML models are evaluated using various metrics with a large collection of inputs features sets. Each model computes a spam score by considering the refined input features. This score depicts the trustworthiness of IoT device under various parameters. REFIT Smart Home data set is used for the validation of proposed technique. The results obtained proves the effectiveness of the proposed scheme in comparison to the other existing schemes.

Fifth generation and beyond networks are envisioned to support multi industrial Internet of Things (IIoT) applications with a diverse quality-of-service (QoS) requirements. Network slicing is recognized as a flagship technology that enables IIoT networks with multiservices and resource requirements by allowing the network-as-infrastructure transition to the network-as-service. Motivated by the increasing IIoT computational capacity, and taking into consideration the QoS satisfaction and private data sharing challenges, federated reinforcement learning (RL) has become a promising approach that distributes data acquisition and computation tasks over distributed network agents, exploiting local computation capacities and agent's self-learning experiences. This article proposes a novel deep RL scheme to provide a federated and dynamic network management and resource allocation for differentiated QoS services in future IIoT networks. This involves IIoT slices resource allocation in terms of transmission power (TP) and spreading factor (SF) according to the slices QoS requirements. Toward this goal, the proposed deep federated Q-learning (DFQL) is reached into two main steps. First, we propose a multiagent deep Q-learning-based dynamic slices TP and SF adjustment process that aims at maximizing self-QoS requirements in term of throughput and delay. Second, the deep federated learning is proposed to learn multiagent self-model and enable them to find an optimal action decision on the TP and the SF that satisfy IIoT virtual network slice QoS reward, exploiting the shared experiences between agents. Simulation results show that the proposed DFQL framework achieves efficient performance compared to the traditional approaches.

Industrial Internet of Things (IIoT) is ushering in huge development opportunities in the era of Industry 4.0. However, there are significant data security and privacy challenges during automatic and real-time data collection, monitoring for industrial applications in IIoT. Data security and privacy in IIoT applications are closely related to the reliability of users, which is determined by user authentication that have been widely used as an effective approach. However, the existing user authentication mechanisms in IIoT suffer from single factor authentication and poor adaptability with the rapid growth of the number of users and the diversity of user categories. To solve the aforementioned issues, this article proposes a novel Authentication mechanism based on Transfer Learning empowered Blockchain, coined ATLB. In ATLB, blockchains are applied to achieve the privacy preservation for industrial applications. In addition, by introducing the transfer learning based authentication mechanism, trustworthy blockchains are built such that the privacy preservation for industrial applications is further enhanced. Specifically, ATLB first employs a guiding deep deterministic policy gradient algorithm to train the user authentication model of a specific region, which is then transferred locally for foreign user authentication or cross-regionally for another region's user authentication such that the model training time is significantly reduced. Experimental results show that the proposed ATLB not only provides accurate authentications for IIoT applications but also achieves high throughput and low latency.

At present, specific voice control has gradually become an important means for 5G-Internet-of-Things-aided industrial control systems, such as controlling the operation and adjustment of industrial Internet of Things equipment through telephone voice of the controller. However, the security of specific voice control system needs to be improved, because the voice cloning technology based on transfer learning can easily simulate the voice of the controller, which may lead to industrial accidents and other potential security risks. Therefore, this article mainly aims to study and understand the principle of voice cloning attack technology, putting forward a voice clone attack method, in order to prepare for the construction of a specific voice recognition system in the future. At present, the key technology of voice cloning attack is how to solve the problem that the target speaker's personalized speech with high quality cannot be synthesized under small samples. In fact, voice cloning is a very challenging problem because speech is more difficult to be represented in the hidden space of the model. We propose a transductive voice transfer learning method to learn the predictive function from the source domain and fine-tune in the target domain adaptively. The target learning task and the source learning task are both synthesizing speech signals from the given audio, while the datasets of both domains are different. By adding different penalty values to each instances and minimizing the expected risk, an optimal precise model can be learned. In addition, an evaluation method to verify the audio similarity of the target speaker was given to show the similarity between the synthesized audio and the original audio. Many details of the experimental results show that our method can effectively synthesize the speech of the target speaker with small samples.

Vehicle taillight detection is essential to analyze and predict driver intention in collision avoidance systems. In this article, we propose an end-to-end framework that locates the rear brake and turn signals from video stream in real-time. The system adopts the fast YOLOv3-tiny as the backbone model and three improvements have been made to increase the detection accuracy on taillight semantics, i.e., additional output layer for multi-scale detection, spatial pyramid pooling (SPP) module for richer deep features, and focal loss for alleviation of class imbalance and hard sample classification. Experimental results demonstrate that the integration of multi-scale features as well as hard examples mining greatly contributes to the turn light detection. The detection accuracy is significantly increased by 7.36%, 32.04% and 21.65% (absolute gain) for brake, left-turn and right-turn signals, respectively. In addition, we construct the taillight detection dataset, with brake and turn signals are specified with bounding boxes, which may help nourishing the development of this realm.

Owing to its benefits such as flexibility, scalability, and interoperability, Software-Defined Networking (SDN) has been incorporated into Internet of Vehicles (IoV) to cope with the increasing demands of vehicular applications. The integration of SDN and IoV, namely SDN-IoV, can enrich many new applications for intelligent transportation such as traffic monitoring, smart navigation, and self-driving. The spatial crowdsourcing technology has been adopted as an effective data collection and processing method that is the premise of various SDN-IoV applications. However, as huge amounts of data are generated in spatial crowdsourcing services, the data privacy and security has become a key challenge for SDN-IoV. To overcome abovementioned challenge, a Deep Reinforcement Learning (DRL) and Blockchain empowered Spatial Crowdsourcing System (DB-SCS) is proposed. In DB-SCS, we design an improved multi-blockchain structure and a blockchain-based hierarchical task management method, which divide the spatial tasks into different categories according to the privacy requirements and the areas of the task and then decompose different categories of tasks and task receivers into sub-blockchains. While guaranteeing the data privacy, DB-SCS can also enhance the spatial crowdsourcing performance by using the proposed DRL-based management strategy to dynamically select the consensus algorithm, block size, and block generation rule. Extensive simulation experiments demonstrate that the DB-SCS can obtain high throughput, low overhead, and data privacy under various SDN-IoV scenarios.

In this article, we investigate the downlink performance of a three-tier heterogeneous network (HetNet). The objective is to enhance the edge capacity of a macro cell by deploying unmanned aerial vehicles (UAVs) as flying base stations and small cells (SCs) for improving the capacity of indoor users in scenarios such as temporary hotspot regions or during disaster situations where the terrestrial network is either insufficient or out of service. UAVs are energy-constrained devices with a limited flight time, therefore, we formulate a two layer optimization scheme, where we first optimize the power consumption of each tier for enhancing the system energy efficiency (EE) under a minimum quality-of-service (QoS) requirement, which is followed by optimizing the average hover time of UAVs. We obtain the solution to these nonlinear constrained optimization problems by first utilizing the Lagrange multipliers method and then implementing a sub-gradient approach for obtaining convergence. The results show that through optimal power allocation, the system EE improves significantly in comparison to when maximum power is allocated to users (ground cellular users or connected vehicles). The hover time optimization results in increased flight time of UAVs thus providing service for longer durations.

Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users' privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.

In the intelligent transportation system, the session data usually represents the users' demand. However, the traditional approaches only focus on the sequence information or the last item clicked by the user, which cannot fully represent user preferences. To address this issue, this paper proposes an Multi-aspect Aware Session-based Recommendation (MASR) model for intelligent transportation services, which comprehensively considers the user's personalized behavior from multiple aspects. In addition, it developed a concise and efficient transformer-style self-attention to analyze the sequence information of the current session, for accurately grasping the user's intention. Finally, the experimental results show that MASR is available to improve user satisfaction with more accurate and rapid recommendations, and reduce the number of user operations to decrease the safety risk during the transportation service.

Volunteer computing based data processing is a new trend in healthcare applications. Researchers are now leveraging volunteer computing power to train deep learning networks consisting of billions of parameters. Breast cancer is the second most common cause of death in women among cancers. The early detection of cancer may diminish the death risk of patients. Since the diagnosis of breast cancer manually takes lengthy time and there is a scarcity of detection systems, development of an automatic diagnosis system is needed for early detection of cancer. Machine learning models are now widely used for cancer detection and prediction research for improving the successive therapy of patients. Considering this need, this study implements pre-trained convolutional neural network based models for detecting breast cancer using ultrasound images. In particular, we tuned the pre-trained models for extracting key features from ultrasound images and included a classifier on the top layer. We measured accuracy of seven popular state-of-the-art pre-trained models using different optimizers and hyper-parameters through fivefold cross validation. Moreover, we consider Grad-CAM and occlusion mapping techniques to examine how well the models extract key features from the ultrasound images to detect cancers. We observe that after fine tuning, DenseNet201 and ResNet50 show 100% accuracy with Adam and RMSprop optimizers. VGG16 shows 100% accuracy using the Stochastic Gradient Descent optimizer. We also develop a custom convolutional neural network model with a smaller number of layers compared to large layers in the pre-trained models. The model also shows 100% accuracy using the Adam optimizer in classifying healthy and breast cancer patients. It is our belief that the model will assist healthcare experts with improved and faster patient screening and pave a way to further breast cancer research.

In this article, we share our experiences in designing and developing a suite of deep neural network–(DNN) based COVID-19 case detection and recognition framework. Existing pathological tests such as RT-PCR-based pathogen RNA detection from nasal swabbing seem to display low detection rates during the early stages of virus contraction. Moreover, the reliance on a few overburdened laboratories based around an epicenter capable of supplying large numbers of RT-PCR tests makes this testing method non-scalable when the rate of infections is high. Similarly, finding an effective drug or vaccine with which to combat COVID-19 requires a long time and many clinical trials. The development of pathological COVID-19 tests is hindered by shortages in the supply chain of chemical reagents necessary for testing on a large scale. This diminishes the speed of diagnosis and the ability to filter out COVID-19 positive patients from uninfected patients on a national level. Existing research has shown that DNN has been successful in identifying COVID-19 from radiological media such as CT scans and X-ray images, audio media such as cough sounds, optical coherence tomography to identify conjunctivitis and pink eye symptoms on the ocular surface, body temperature measurement using smartphone fingerprint sensors or thermal cameras, the use of live facial detection to identify safe social distancing practices from camera images, and face mask detection from camera images. We also investigate the utility of federated learning in diagnosis cases where private data can be trained via edge learning. These point-of-care modalities can be integrated with DNN-based RT-PCR laboratory test results to assimilate multiple modalities of COVID-19 detection and thereby provide more dimensions of diagnosis. Finally, we will present our initial test results, which are encouraging.

Diabetic retinopathy (DR) is one of the most common causes of vision loss in people who have diabetes for a prolonged period. Convolutional neural networks (CNNs) have become increasingly popular for computer-aided DR diagnosis using retinal fundus images. While these CNNs are highly reliable, their lack of sufficient explainability prevents them from being widely used in medical practice. In this article, we propose a novel explainable deep learning ensemble model where weights from different models are fused into a single model to extract salient features from various retinal lesions found on fundus images. The extracted features are then fed to a custom classifier for the final diagnosis of DR severity level. The model is trained on an APTOS dataset containing retinal fundus images of various DR grades using a cyclical learning rates strategy with an automatic learning rate finder for decaying the learning rate to improve model accuracy. We develop an explainability approach by leveraging gradient-weighted class activation mapping and shapely adaptive explanations to highlight the areas of fundus images that are most indicative of different DR stages. This allows ophthalmologists to view our model's decision in a way that they can understand. Evaluation results using three different datasets (APTOS, MESSIDOR, IDRiD) show the effectiveness of our model, achieving superior classification rates with a high degree of precision (0.970), sensitivity (0.980), and AUC (0.978). We believe that the proposed model, which jointly offers state-of-the-art diagnosis performance and explainability, will address the black-box nature of deep CNN models in robust detection of DR grading.

Medical systems incorporate modern computational intelligence in healthcare. Machine learning techniques are applied to predict the onset and reoccurrence of the disease, identify biomarkers for survivability analysis depending upon certain health conditions of the patient. Early prediction of diseases like diabetes is essential as the number of diabetic patients of all age groups is increasing rapidly. To identify underlying reasons for the onset of diabetes in its early stage has become a challenging task for medical practitioners. Continuously increasing diabetic patient data has necessitated for the applications of efficient machine learning algorithms, which learns from the trends of the underlying data and recognizes the critical conditions in patients. In this article, an ensemble-based framework named e DiaPredict is proposed. It uses ensemble modeling, which includes an ensemble of different machine learning algorithms comprising XGBoost, Random Forest, Support Vector Machine, Neural Network, and Decision tree to predict diabetes status among patients. The performance of eDiaPredict has been evaluated using various performance parameters like accuracy, sensitivity, specificity, Gini Index, precision, area under curve, area under convex hull, minimum error rate, and minimum weighted coefficient. The effectiveness of the proposed approach is shown by its application on the PIMA Indian diabetes dataset wherein an accuracy of 95% is achieved.

Summary ECG and EEG signals are very helpful in the early diagnosis of epileptic seizures. The research focuses on analysis of ECG and EEG signals applying a deep learning technique to study early prediction of epileptic seizure. Signal processing methods like Empirical Mode Decomposition, spectral analysis, and statistical methods were used. The algorithms were implemented in MATLAB, and the EEG and ECG data were collected from Physiobank and EPILEPSIAE databases. In the window‐based analysis of low‐frequency spectral area of EEG signals, 78.5% of the cases displayed a significant change as the windows progressed and the onset of seizure was approached. The spectral area of IMF components indicated a possible seizure prediction in 68.9% of the analyzed cases. Considering signals from individual EEG electrodes, the least percentage of seizure prediction was indicated by signals from T4 and F4 electrodes (52.3% and 40.7%, respectively, for spectral peaks and 23.8% and 29.6%, respectively, for spectral area). The results of regression analysis show that prediction of seizures can be possible around 20‐30 minutes prior to the actual occurrence of seizures.

The Internet of Things (IoT) plays a vital role in the real world by providing autonomous support for communications and operations, thus enabling and promoting novel services that are commonly used in day-to-day life. It is important to do research on security frameworks for next-generation IoT and develop state-of-the-art confidentiality protection schemes to deal with various attacks on IoT networks. In order to offer prominent features like continuous confidentiality, authentication, and robustness, the blockchain technology comes out as a sustainable solution. A blockchain-enabled distributed security framework using edge cloud and software-defined networking (SDN) is presented in this article. The security attack detection is achieved at the cloud layer, and security attacks are consequently reduced at the edge layer of the IoT network. The SDN-enabled gateway offers dynamic network traffic flow management, which contributes to the security attack recognition through determining doubtful network traffic flows and diminishes security attacks through hindering doubtful flows. The results obtained show that the proposed security framework can efficiently and effectively meet the data confidentiality challenges introduced by the integration of blockchain, edge cloud, and SDN paradigm.

This article proposes a tree-based deep model for effective load distribution to edge devices without much loss of accuracy. The input image is divided into groups of volumes, and each volume is passed through a tree structure. The tree structure has many branches and levels, each of which is represented by a convolutional layer. The layers are independent of each other. Therefore, various edge devices can update the parameters of the layers in parallel independently. Experiments are performed using a benchmark dataset and a publicly available date fruits database. Experimental results show that the proposed model has a high information density by reducing the number of parameters without much loss of accuracy.

Adversary models have been fundamental to the various cryptographic protocols and methods. However, their use in most of the branches of research in computer science is comparatively restricted, primarily in case of the research in cyberphysical security (e.g., vulnerability studies, position confidentiality). In this article, we propose an energy-aware green adversary model for its use in smart industrial environment through achieving confidentiality. Even though, mutually the hardware and the software parts of cyberphysical systems can be improved to decrease its energy consumption, this article focuses on aspects of conserving position and information confidentiality. On the basis of our findings (assumptions, adversary goals, and capabilities) from the literature, we give some testimonials to help practitioners and researchers working in cyberphysical security. The proposed model that runs on real-time anticipatory position-based query scheduling in order to minimize the communication and computation cost for each query, thus, facilitating energy consumption minimization. Moreover, we calculate the transferring/acceptance slots required for each query to avoid deteriorating slots. The experimental results confirm that the proposed approach can diminish energy consumption up to five times in comparison to existing approaches.

Malaria is a contagious disease that affects millions of lives every year. Traditional diagnosis of malaria in laboratory requires an experienced person and careful inspection to discriminate healthy and infected red blood cells (RBCs). It is also very time-consuming and may produce inaccurate reports due to human errors. Cognitive computing and deep learning algorithms simulate human intelligence to make better human decisions in applications like sentiment analysis, speech recognition, face detection, disease detection, and prediction. Due to the advancement of cognitive computing and machine learning techniques, they are now widely used to detect and predict early disease symptoms in healthcare field. With the early prediction results, healthcare professionals can provide better decisions for patient diagnosis and treatment. Machine learning algorithms also aid the humans to process huge and complex medical datasets and then analyze them into clinical insights. This paper looks for leveraging deep learning algorithms for detecting a deadly disease, malaria, for mobile healthcare solution of patients building an effective mobile system. The objective of this paper is to show how deep learning architecture such as convolutional neural network (CNN) which can be useful in real-time malaria detection effectively and accurately from input images and to reduce manual labor with a mobile application. To this end, we evaluate the performance of a custom CNN model using a cyclical stochastic gradient descent (SGD) optimizer with an automatic learning rate finder and obtain an accuracy of 97.30% in classifying healthy and infected cell images with a high degree of precision and sensitivity. This outcome of the paper will facilitate microscopy diagnosis of malaria to a mobile application so that reliability of the treatment and lack of medical expertise can be solved.

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.

Once a subject is diagnosed with cancer, a patient goes through a series of diagnosis and tests, referred to as after cancer treatment. Due to the nature of the treatment and side effects on regular lifestyles, maintaining quality of life in the home environment is a challenging task. Sometimes within a home environment, a cancer patient's situation changes abruptly, as the functionality of certain organs deteriorate, which affects their quality of life. In this paper, we propose a Blockchain and off-chain based framework which will allow multiple medical and ambient intelligent IoT sensors to capture quality of life information from one's home environment and securely share it with one's community of interest. Using our proposed framework, both transactional records and multimedia big data - consisting of a user's physiological as well as mental states - can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed Blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient's data available from an in-home secure monitoring system for a better understanding of a patient's current or historical states. We further designed a generic oncologist smart contract and digital wallet for different stakeholders to automate the treatment plan of a particular patient. Finally, we will present our current implementation status, which provides significant encouragement for further development.

Fruit classification is an important task in many industrial applications. A fruit classification system may be used to help a supermarket cashier identify the fruit species and prices. It may also be used to help people decide whether specific fruit species meet their dietary requirements. In this paper, we propose an efficient framework for fruit classification using deep learning. More specifically, the framework is based on two different deep learning architectures. The first is a proposed light model of six convolutional neural network layers, whereas the second is a fine-tuned visual geometry group-16 pretrained deep learning model. Two color image datasets, one of which is publicly available, are used to evaluate the proposed framework. The first dataset (dataset 1) consists of clear fruit images, whereas the second dataset (dataset 2) contains fruit images that are challenging to classify. Classification accuracies of 99.49% and 99.75% were achieved on dataset 1 for the first and second models, respectively. On dataset 2, the first and second models obtained accuracies of 85.43% and 96.75%, respectively.

Position-based services (PBSs) that deliver networked amenities based on roaming user's positions have become progressively popular with the propagation of smart mobile devices. Position is one of the important circumstances in PBSs. For effective PBSs, extraction and recognition of meaningful positions and estimating the subsequent position are fundamental procedures. Several researchers and practitioners have tried to recognize and predict positions using various techniques; however, only few deliberate the progress of position-based real-time applications considering significant tasks of PBSs. In this paper, a method for conserving position confidentiality of roaming PBSs users using machine learning techniques is proposed. We recommend a three-phase procedure for roaming PBS users. It identifies user position by merging decision trees and k-nearest neighbor and estimates user destination along with the position track sequence using hidden Markov models. Moreover, a mobile edge computing service policy is followed in the proposed paradigm, which will ensure the timely delivery of PBSs. The benefits of mobile edge service policy offer position confidentiality and low latency by means of networking and computing services at the vicinity of roaming users. Thorough experiments are conducted, and it is confirmed that the proposed method achieved above 90% of the position confidentiality in PBSs.

The interactive robot is expected to support emotion analysis and utilize the deep learning and machine learning to provide users with continuous emotional care. However, it is a great challenge to securely acquire sufficient data for emotion analysis such that the privacy of emotional data is adequately protected. To address the security issue, this paper proposes a security policy based on identity authentication and access control to ensure the security certificate through an interactive robot or edge devices while the access control of private data stored in the edge cloud is adequately protected. Specifically, this paper adopts a polynomial-based access control policy and designs a secure and effective access control scheme. At the same time, this paper puts forward an identity authentication mechanism in view of edge cloud systems, which can reduce the computational overhead and authentication delay in a collaborative authentication of multiple edge clouds. The effectiveness of the proposed access control policy and identity authentication mechanism is verified by an actual testbed platform.

Deep Convolutional Neural Network (CNN) has achieved remarkable results in computer vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn robust features from raw Electroencephalogram (EEG) data to detect seizures. Seizures are hard to detect, as they vary both inter- and intra-patient. In this article, we use a deep CNN model for seizure detection task on an open-access EEG epilepsy dataset collected at the Boston Children's Hospital. Our deep learning model is able to extract spectral, temporal features from EEG epilepsy data and use them to learn the general structure of a seizure that is less sensitive to variations. For cross-patient EEG data, our method produced an overall sensitivity of 90.00%, specificity of 91.65%, and overall accuracy of 98.05% for the whole dataset of 23 patients. The system can detect seizures with an accuracy of 99.46%. Thus, it can be used as an excellent cross-patient seizure classifier. The results show that our model performs better than the previous state-of-the-art models for patient-specific and cross-patient seizure detection task. The method gave an overall accuracy of 99.65% for patient-specific data. The system can also visualize the special orientation of band power features. We use correlation maps to relate spectral amplitude features to the output in the form of images. By using the results from our deep learning model, this visualization method can be used as an effective multimedia tool for producing quick and relevant brain mapping images that can be used by medical experts for further investigation.

Summary Sybil attacks are increasingly prevalent in online social networks. A malicious user can generate a huge number of fake accounts to produce spam, impersonate other users, commit fraud, and reach many legitimate users. For security reasons, such fake accounts have to be detected and deactivated immediately. Various defense schemes have been proposed to deal with fake accounts. However, most identify fake accounts using only the structure of social graphs, leading to poor performance. In this paper, we propose a new and scalable defense scheme, SybilTrap. SybilTrap uses a semi‐supervised technique that automatically integrates the underlying features of user activities with the social structure into one system. Unlike other machine learning–based approaches, the proposed defense scheme works on unlabeled data, and it is effective in detecting targeted attacks, because it manipulates different levels of features of user profiles. We evaluate SybilTrap on a dataset collected from Twitter. We show that our proposed scheme is able to accurately detect Sybil nodes as well as huge conspiracies among them.

The recent development of big data-oriented wireless technologies in terms of emerging 5G, edge computing, interconnected devices of the Internet of Things (IoT), and data analytics, as well as techniques, have enabled connected healthcare services for a happier and healthier life. Although, the quality of the healthcare services can be enhanced through big data-oriented wireless technologies, however, the challenges remain for not considering emotional care, especially for children, elderly, and mentally ill people. In this paper, we propose an emotion-aware connected healthcare system using a powerful emotion detection module. Different IoT devices are used to capture speech and image signals of a patient in a smart home scenario. These signals are used as the input to the emotion detection module. Speech and image signals are processed separately, and classification scores using these signals are fused to produce a final score to take a decision about the emotion. If the emotion is detected as pain, caregivers can visit the patient. Several experiments were performed to validate the proposed system, and good accuracies, up to 99.87%, were achieved for emotion detection. The proposed framework would greatly contribute personalized and seamless emotion-aware healthcare services toward 5G.

As a new type of low power wide area (LPWA) technology, the narrowband Internet of Things (NB-IoT) technology supports wide coverage and low bitrate services, thus it has a great potential to be the future commercial technology of LPWA network. Therefore, it has attracted attention of both academia and industry. In this paper, we present the NB-IoT development, and main characteristics and design objectives of NB-IoT according to 3GPP R13. In addition, we provide the review of related literatures about NB-IoT modeling and algorithm analysis. And we explain current problems of NB-IoT system-level modeling based on visualized simulation platform. Moreover, this paper is devoted to the construction of the NB-IoT model based on OPNET and the verification of its characteristics, such as wide coverage and high channel utilization. This paper mainly considers NB-IoT model design and realization in terms of NB-IoT physical layer characteristics. We summarize the correlated characteristics of NB-IoT uplink and downlink. Then we design and construct the NB-IoT model based on Long Term Evolution (LTE) network. Lastly, we use the constructed NB-IoT model for simulations and conduct an experiment on it using the LTE network with channel bandwidths of 3 MHz, 5 MHz, 10 MHz, 15 MHz, and 20 MHz. The simulation results have verified the performance of NB-IoT, wherein uplink time delay is lower than 10 s, channel utilization is higher than that of LTE network, and coverage area is larger than LTE network.

As for deployment of security strategy, corresponding forwarding rules for switches can be given in allusion to different traffic conditions. However, due to lack of global cognitive control for security strategy deployment in traditional Internet of Vehicles (IoV), it is quite difficult to realize global and optimized security strategy deployment scheme so as to meet security requirements in different traffic conditions. On basis of traditional IoV, cognitive engine is added in cognitive IoV (CIoV) to enhance the intelligence of traditional IoV. In allusion to CIoV, and in consideration of restrictions on transmission delay, the security strategy deployment for switches on core network is formulated in this paper, thus not only the safe transmission rules are met, but the transmission delay can also be the lowest. To be specific, the path selection of switches is modeled as 0-1 programming problem in this paper, and that optimization problem is proved to be a nonconvex optimization problem. Then we convert that problem into a convex optimization problem by log-det heuristic algorithm, thus to give path selection scheme to meet security requirements with the lowest delay on the whole. Experiment proves that cognitive engine-based security strategy deployment put forth in this paper is much better than other schemes.

Social Internet of Things is assumed to provide health services by incorporating social networks and the Internet of Things (IoT). Although, much development in therapy monitoring has been observed recently, few advancements have been achieved in the domain of in-home therapy. Existing industrial and medical solutions require complex and expensive hardware and software that are impractical for home use. Another challenge for in-home therapy is that therapists cannot confirm whether patients are conducting the therapy correctly and for the prescribed number of times. To address these challenges, we propose the multisensor therapy (m-Therapy) framework, in which multiple gesture-tracking sensors and environmental sensors are used to collect therapy and ambient data. The m-Therapy framework compresses the collected data and uploads to a big data server. The framework uses a model of the therapy to guide a patient performing therapy exercises outside medical institutions and even at home. Ambient IoT sensors can help maintain an appropriate ambient environment, which is generally maintained at the medical institutions. We have developed analytics that can provide live or statistical kinematic data, including rotational and angular range of motion of the joints of interest, and ambient environmental data, which can be shared with therapists and caregivers. We present our findings, which shows that the proposed m-Therapy monitoring system can be deployed in real-life scenarios.

With the enormous growth and volume of online social networks and their features, along with the vast number of socially connected users, it has become difficult to explain the true semantic value of published content for the detection of user behaviors. Without understanding the contextual background, it is impractical to differentiate among various groups in terms of their relevance and mutual relations, or to identify the most significant representatives from the community at large. In this paper, we propose an integrated social media content analysis platform that leverages three levels of features, i.e., user-generated content, social graph connections, and user profile activities, to analyze and detect anomalous behaviors that deviate significantly from the norm in large-scale social networks. Several types of analyses have been conducted for a better understanding of the different user behaviors in the detection of highly adaptive malicious users. We attempted a novel approach regarding the process of data extraction and classification to contextualize large-scale networks in a proper manner. We also collected a significant number of user profiles from Twitter and YouTube, along with around 13 million channel activities. Extensive evaluations were conducted on real-world datasets of user activities for both social networks. The evaluation results show the effectiveness and utility of the proposed approach.

With the development of LPWA (Low Power Wide Area) technology, the emerging NB‐IoT (Narrowband Internet of Things) technology is becoming popular with wide area and low‐data‐rate services. In order to achieve objectives such as huge amount of connection and wide area coverage within NB‐IoT, the problem of network congestion generated by random access of numerous devices should be solved. In this paper, we first introduce the background of NB‐IoT and investigate the research on random access optimization algorithm. Then we summarize relevant features of NB‐IoT uplink and narrowband physical random access channel and design random access with differentiated barring (RADB), which can improve the insufficiency of traditional dynamic access class barring method. At last, the algorithms proposed in this paper are realized with established NB‐IoT model using OPNET Modeler platform, and simulations are conducted. The simulation results show that RADB is able to effectively solve preamble request conflict generated by random access of numerous devices and preferentially provide efficient and reliable random access for latency‐sensitive devices.

Video transmission is an indispensable component of most applications related to the mobile cloud networks (MCNs). However, because of the complexity of the communication environment and the limitation of resources, attempts to develop an effective solution for video transmission in the MCN face certain difficulties. In this paper, we propose a novel green video transmission (GVT) algorithm that uses video clustering and channel assignment to assist in video transmission. A video clustering model is designed based on game theory to classify the different video parts stored in mobile devices. Using the results of video clustering, the GVT algorithm provides the function of channel assignment, and its assignment process depends on the content of the video to improve channel utilization in the MCN. Extensive simulations are carried out to evaluate the GVT with several performance criteria. Our analysis and simulations show that the proposed GTV demonstrates a superior video transmission performance compared with the existing methods.

Location-based services, especially for vehicular localization, are an indispensable component of most technologies and applications related to the vehicular networks. However, because of the randomness of the vehicle movement and the complexity of a driving environment, attempts to develop an effective localization solution face certain difficulties. In this paper, an overlapping and hierarchical social clustering model (OHSC) is first designed to classify the vehicles into different social clusters by exploring the social relationship between them. By using the results of the OHSC model, we propose a social-based localization algorithm (SBL) that use location prediction to assist in global localization in the vehicular networks. The experiment results validate the performance of the OHSC model and show that the presented SBL algorithm demonstrates superior localization performance compared with the existing methods.

Newly emerging location-based social media network services (LBSMNS) provide valuable resources to understand users’ behaviors based on their location histories. The location-based behaviors of a user are generally influenced by both user intrinsic interest and the location preference, and moreover are spatial-temporal context dependent. In this article, we propose a spatial-temporal context-aware personalized location recommendation system (STCAPLRS), which offers a particular user a set of location items such as points of interest or venues (e.g., restaurants and shopping malls) within a geospatial range by considering personal interest, local preference, and spatial-temporal context influence. STCAPLRS can make accurate recommendation and facilitate people’s local visiting and new location exploration by exploiting the context information of user behavior, associations between users and location items, and the location and content information of location items. Specifically, STCAPLRS consists of two components: offline modeling and online recommendation. The core module of the offline modeling part is a context-aware regression mixture model that is designed to model the location-based user behaviors in LBSMNS to learn the interest of each individual user, the local preference of each individual location, and the context-aware influence factors. The online recommendation part takes a querying user along with the corresponding querying spatial-temporal context as input and automatically combines the learned interest of the querying user, the local preference of the querying location, and the context-aware influence factor to produce the top- k recommendations. We evaluate the performance of STCAPLRS on two real-world datasets: Dianping and Foursquare. The results demonstrate the superiority of STCAPLRS in recommending location items for users in terms of both effectiveness and efficiency. Moreover, the experimental analysis results also illustrate the excellent interpretability of STCAPLRS.

An ontology hierarchically encodes concepts and concept relationships, and has a variety of applications such as semantic understanding and information retrieval. Previous work for building ontologies has primarily relied on labor-intensive human contributions or focused on text-based extraction. In this paper, we consider the problem of automatically constructing a folksonomy-based visual ontology (FBVO) from the user-generated annotated images. A systematic framework is proposed consisting of three stages as concept discovery, concept relationship extraction, and concept hierarchy construction. The noisy issues of the user-generated tags are carefully addressed to guarantee the quality of derived FBVO. The constructed FBVO finally consists of 139 825 concept nodes and millions of concept relationships by mining more than 2.4 million Flickr images. Experimental evaluations show that the derived FBVO is of high quality and consistent with human perception. We further demonstrate the utility of the derived FBVO in applications of complex visual recognition and exploratory image search.

Relative attribute (RA) learning aims to learn the ranking function describing the relative strength of the attribute. Most of current learning approaches learn a linear ranking function for each attribute by use of the hand-crafted visual features. Different from the existing study, in this paper, we propose a novel deep relative attributes (DRA) algorithm to learn visual features and the effective nonlinear ranking function to describe the RA of image pairs in a unified framework. Here, visual features and the ranking function are learned jointly, and they can benefit each other. The proposed DRA model is comprised of five convolutional neural layers, five fully connected layers, and a relative loss function which contains the contrastive constraint and the similar constraint corresponding to the ordered image pairs and the unordered image pairs, respectively. To train the DRA model effectively, we make use of the transferred knowledge from the large scale visual recognition on ImageNet [1] to the RA learning task. We evaluate the proposed DRA model on three widely used datasets. Extensive experimental results demonstrate that the proposed DRA model consistently and significantly outperforms the state-of-the-art RA learning methods. On the public OSR, PubFig, and Shoes datasets, compared with the previous RA learning results [2], the average ranking accuracies have been significantly improved by about 8%, 9%, and 14%, respectively.

Online video sharing sites are increasingly encouraging their users to connect to the social network venues such as Facebook and Twitter, with goals to boost user interaction and better disseminate the high-quality video content. This in turn provides huge possibilities to conduct cross-network collaboration for personalized video recommendation. However, very few efforts have been devoted to leveraging users’ social media profiles in the auxiliary network to capture and personalize their video preferences, so as to recommend videos of interest. In this article, we propose a unified YouTube video recommendation solution by transferring and integrating users’ rich social and content information in Twitter network. While general recommender systems often suffer from typical problems like cold-start and data sparsity, our proposed recommendation solution is able to effectively learn from users’ abundant auxiliary information on Twitter for enhanced user modeling and well address the typical problems in a unified framework. In this framework, two stages are mainly involved: (1) auxiliary-network data transfer, where user preferences are transferred from an auxiliary network by learning cross-network knowledge associations; and (2) cross-network data integration, where transferred user preferences are integrated with the observed behaviors on a target network in an adaptive fashion. Experimental results show that the proposed cross-network collaborative solution achieves superior performance not only in terms of accuracy, but also in improving the diversity and novelty of the recommended videos.

The proliferation of mobile computing and smartphone technologies has resulted in an increasing number and range of services from myriad service providers. These mobile service providers support numerous emerging services with differing quality metrics but similar functionality. Facilitating an automated service workflow requires fast selection and composition of services from the services pool. The mobile environment is ambient and dynamic in nature, requiring more efficient techniques to deliver the required service composition promptly to users. Selecting the optimum required services in a minimal time from the numerous sets of dynamic services is a challenge. This work addresses the challenge as an optimization problem. An algorithm is developed by combining particle swarm optimization and k-means clustering. It runs in parallel using MapReduce in the Hadoop platform. By using parallel processing, the optimum service composition is obtained in significantly less time than alternative algorithms. This is essential for handling large amounts of heterogeneous data and services from various sources in the mobile environment. The suitability of this proposed approach for big data-driven service composition is validated through modeling and simulation.

The promising potential and emerging applications of cloud gaming have drawn increasing interest from academia, industry, and the general public. However, providing a high-quality gaming experience in the cloud gaming framework is a challenging task because of the tradeoff between resource consumption and player emotion, which is affected by the game screen. We tackle this problem by leveraging emotion-aware screen effects in the cloud gaming framework and combining them with remote display technology. The first stage in the framework is the learning or training stage, which establishes a relationship between screen features and emotions using Gaussian mixture model-based classifiers. In the operating stage, a linear programming model provides appropriate screen changes based on the real-time user emotion obtained in the first stage. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high quality gaming experience while generating an acceptable amount of workload for the cloud server in terms of resource consumption.

Nowadays, more and more people are engaged in social media to generate multimedia information, i.e., creating text and photo profiles and posting multimedia messages . Such multimodal social networking activities reveal multiple user attributes such as age, gender, and personal interest. Inferring user attributes is important for user profiling, retrieval , and personalization . Existing work is devoted to inferring user attributes independently and ignores the dependency relations between attributes. In this work, we investigate the problem of relational user attribute inference by exploring the relations between user attributes and extracting both lexical and visual features from online user-generated content. We systematically study six types of user attributes: gender, age, relationship , occupation , interest, and emotional orientation. In view of methodology , we propose a relational latent SVM (LSVM) model to combine a rich set of user features, attribute inference, and attribute relations in a unified framework. In the model, one attribute is selected as the target attribute and others are selected as the auxiliary attributes to assist the target attribute inference. The model infers user attributes and attribute relations simultaneously . Extensive experiments conducted on a collected dataset from Google+ with full attribute annotations demonstrate the effectiveness of the proposed approach in user attribute inference and attribute-based user retrieval.

Most existing approaches on aspect-opinion mining focus on the text domain and cannot be applied to social media where the aspects are essentially multimodal and the opinions depend on the specific aspects. To address the problem of multimodal aspect-opinion mining for entities by leveraging multiple cross-collection sources in social media, in this paper we propose a multimodal aspect-opinion model (mmAOM) considering both user-generated photos and textual documents to simultaneously capture correlations between textual and visual modalities, as well as associations between aspects and opinions . By identifying the aspects and the corresponding opinions related to entities, we apply the mmAOM to entity association visualization and multimodal aspect-opinion retrieval. We have conducted extensive experiments on real-world datasets of entities including Flickr photos, Tripadvisor reviews, and news articles. Qualitative and quantitative evaluation results have validated the effectiveness of the multimodal aspect-opinion mining model, and demonstrated the utility of the derived aspects and opinions from mmAOM in applications of entity association visualization and aspect-opinion retrieval.

In this paper, we investigate a novel cross- platform multimedia problem: given two platforms, Flickr and Foursquare, we conduct the recommendation between these two platforms, namely the photo recommendation from Flickr to Foursquare users and the venue recommendation from Foursquare to Flickr users. Such inter-platform recommendations enable users from one single platform to enjoy different recommendation services effectively . To solve the problem, we propose a cross- platform multi-modal topic model ( CM3TM), which is capable of: 1) differentiating between two kinds of topics, i.e., platform- specific topics only relevant to a certain platform and shared topics characterizing the knowledge shared by different platforms and 2) aligning multiple modalities from different platforms. Specifically, CM3TM can not only split the topic space into the shared topic space and platform-specific topic space and learn them simultaneously, but also enable the alignment among different modalities through the learned topic space. Given the location information, we applied the proposed CM3TM into two inter-platform recommendation applications: 1) personalized venue recommendation from Foursquare to Flickr users and 2) personalized image recommendation from Flickr to Foursquare users. We have conducted experiments on the collected large-scale real-world dataset from Flickr and Foursquare. Qualitative and quantitative evaluation results validate the effectiveness of our method and demonstrate the advantage of connecting different platforms with different modalities for the inter-platform recommendation.

The emergence and rapid proliferation of various social media networks have reshaped the way how video contents are generated, distributed, and consumed in traditional video sharing portals. Nowadays, online videos can be accessed from far beyond the internal mechanisms of the video sharing portals, such as internal search and front page highlight. Recent studies have found that external referrers, such as external search engines and other social media websites, arise to be the new and important portals to lead users to online videos. In this paper, we introduce a novel cross-network collaborative application to help drive the online traffic for given videos in the traditional video portal YouTube by leveraging the high propagation efficiency of the popular Twitter followees. Since YouTube videos and Twitter followees distribute on heterogeneous spaces, we present a cross-network association-based solution framework. In this framework, we first represent YouTube videos and Twitter followees in the corresponding topic spaces separately by employing generative topic models. Then, the cross-network topic spaces are associated from both semantic-based and network-based perspectives through the collective intelligence of the observed overlapped users. Based on the derived cross-network association, we finally match the query YouTube videos and candidate Twitter followees in the same topic space with a unified ranking method. The experiments on a real-world large-scale dataset of more than 2.2 million YouTube videos and 31.8 million tweets from 38,540 YouTube users and 39,400 Twitter users demonstrate the effectiveness and superiority of our solution in which network-based and semantic-based association are integrated.

Vision-based event analysis is extremely difficult due to the various concepts (object, action, and scene) contained in videos. Though visual concept-based event analysis has achieved significant progress, it has two disadvantages: visual concept is defined manually, and has only one corresponding classifier in traditional methods. To deal with these issues, we propose a novel automatic visual concept learning algorithm for social event understanding in videos. First, instead of defining visual concept manually, we propose an effective automatic concept mining algorithm with the help of Wikipedia, N-gram Web services, and Flickr. Then, based on the learned visual concept, we propose a novel boosting concept learning algorithm to iteratively learn multiple classifiers for each concept to enhance its representative discriminability. The extensive experimental evaluations on the collected dataset well demonstrate the effectiveness of the proposed algorithm for social event understanding.

Feature representation learning is an important and fundamental task in multimedia and pattern recognition research. In this paper, we propose a novel framework to explore the hierarchical structure inside the images from the perspective of feature representation learning, which is applied to hierarchical image annotation. Different from the current trend in multimedia analysis of using pre-defined features or focusing on the end-task “flat” representation, we propose a novel layer-wise tag- embedded deep learning (LTDL) model to learn hierarchical features which correspond to hierarchical semantic structures in the tag hierarchy . Unlike most existing deep learning models, LTDL utilizes both the visual content of the image and the hierarchical information of associated social tags. In the training stage, the two kinds of information are fused in a bottom-up way. Supervised training and multi-modal fusion alternate in a layer-wise way to learn feature hierarchies. To validate the effectiveness of LTDL, we conduct extensive experiments for hierarchical image annotation on a large-scale public dataset. Experimental results show that the proposed LTDL can learn representative features with improved performances.

With the explosive growth of online media platforms in recent years, it becomes more and more attractive to provide users a solution of emerging topic detection and elaboration. And this posts a real challenge to both industrial and academic researchers because of the overwhelming information available in multiple modalities and with large outlier noises. This article provides a method on emerging topic detection and elaboration using multimedia streams cross different online platforms. Specifically, Twitter, New York Times and Flickr are selected for the work to represent the microblog, news portal and imaging sharing platforms. The emerging keywords of Twitter are firstly extracted using aging theory. Then, to overcome the nature of short length message in microblog, Robust Cross-Platform Multimedia Co-Clustering (RCPMM-CC) is proposed to detect emerging topics with three novelties: 1) The data from different media platforms are in multimodalities; 2) The coclustering is processed based on a pairwise correlated structure, in which the involved three media platforms are pairwise dependent; 3) The noninformative samples are automatically pruned away at the same time of coclustering. In the last step of cross-platform elaboration, we enrich each emerging topic with the samples from New York Times and Flickr by computing the implicit links between social topics and samples from selected news and Flickr image clusters, which are obtained by RCPMM-CC. Qualitative and quantitative evaluation results demonstrate the effectiveness of our method.

With the rapidly increasing popularity of social media sites (e.g., Flickr, YouTube, and Facebook), it is convenient for users to share their own comments on many social events, which successfully facilitates social event generation, sharing and propagation and results in a large amount of user-contributed media data (e.g., images, videos, and text) for a wide variety of real-world events of different types and scales. As a consequence, it has become more and more difficult to exactly find the interesting events from massive social media data, which is useful to browse, search and monitor social events by users or governments. To deal with these issues, we propose a novel boosted multimodal supervised Latent Dirichlet Allocation (BMM-SLDA) for social event classification by integrating a supervised topic model, denoted as multi-modal supervised Latent Dirichlet Allocation (mm-SLDA), in the boosting framework. Our proposed BMM-SLDA has a number of advantages. (1) Our mm-SLDA can effectively exploit the multimodality and the multiclass property of social events jointly, and make use of the supervised category label information to classify multiclass social event directly. (2) It is suitable for large-scale data analysis by utilizing boosting weighted sampling strategy to iteratively select a small subset of data to efficiently train the corresponding topic models. (3) It effectively exploits social event structure by the document weight distribution with classification error and can iteratively learn new topic model to correct the previously misclassified event documents. We evaluate our BMM-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods.

Cloud-Based Multimedia services and technologies are emerging as an innovative means of accessing and delivering e-health resources and services in the years to come. The research in multimedia cloud for E-health is still in its infancy, and several technical issues remain open. Prior to its general use and adoption, careful consideration and evaluation are required. This article provides a summary and overview of the First International ACM workshop on Cloud-Based Multimedia Applications and Services for E-Health.

The 11 papers in this special section focus on multimedia services and technologies for E-Health (MUST-EH).

E-Health systems provide a collaborative platform for sharing patients medical data typically stored in distributed autonomous healthcare data sources. Each autonomous source stores its medical and multimedia data without following any global structure. This causes heterogeneity in the underlying sources with respect to the data and storage structure. Therefore, a data interoperability mechanism is required for sharing the data among the heterogeneous sources. A proper metadata structure is also necessary to represent multimedia content in the sources to enable efficient query processing. Considering these needs, we present an interoperability solution for sharing data among heterogeneous data sources. We also propose a metadata management framework for medical multimedia con-tent including X-ray, ECG, MRI, and ultrasound images. The framework identifies features, generates and represents metadata, and produces identifiers for the medical multimedia content to facilitate efficient query processing. The framework has been tested with various user queries and the accuracy of the query results evaluated by means of precision, recall, and user feedback methods. The results confirm the effectiveness of the proposed approach.

QoS is essential for ubiquitous access of media services in real-time distributed video surveillance. In order to have ubiquitous access of desired media through emergency official's different handheld devices, appropriate media transcoding services need to be used. Now, the challenge is to select and compose these services for each of the devices to satisfy the desired QoS demand. To this end, this paper presents a QoS-aware service composition algorithm to select the best composition for the target client. We have implemented a prototype of service composition for video surveillance by using the proposed QoS-aware composition algorithm. Simulation is results shows that the framework is scalable and suitable for real-life distributed video surveillance.

Abstract This paper addresses several key issues in distributed multimedia services management and composition such as scalability, heterogeneity, and quality of service (QoS). The proposed framework introduces biologically inspired multimedia service management through the composition of basic multimedia services such as streaming services and different transcoding services. The biologically inspired approach is used for collecting the QoS requirements from individual transcoding services in order to select the most suitable services for the desired composition process. A prototype of the proposed framework is designed, implemented, and evaluated in terms of scalability and load balancing. Copyright © 2009 John Wiley & Sons, Ltd.