Sustainable smart cities require cross-domain, adaptive, and anticipatory intelligence. Current Internet of Things and Artificial Intelligence systems are largely siloed, limiting their ability to support comprehensive urban management. This review addresses this gap by examining the integration of Artificial General Intelligence with the Cyber-Physical-Social-Thinking paradigm. We synthesize research published from January 2015 to September 2025 to provide an overview of how Artificial General Intelligence can serve as the cognitive core, facilitating proactive orchestration across Internet of Things-driven urban layers. Specifically, the paper (1) presents a conceptual analysis of reasoning and meta-cognitive capabilities within the Cyber-Physical-Social-Thinking framework, (2) maps enabling technologies, including digital twins, neuro-symbolic Artificial Intelligence, and the edge-cloud continuum, that operationalize this vision, and (3) reviews applications in mobility, energy management, and climate resilience, highlighting potential gains in efficiency, adaptability, and sustainability. Key technical, ethical, and governance challenges, such as scalability, security, and algorithmic fairness, are discussed. By integrating these insights, the review provides a unified perspective and a roadmap for future research on human-centric, resilient, and sustainable urban intelligent systems.

The integration of the Internet of Everything (IoX) and emerging Artificial General Intelligence (AGI) has given rise to a transformative paradigm aimed at addressing critical bottlenecks across the sensing, network, and application layers in Cyber-Physical-Social-Thinking (CPST) ecosystems. In this survey, we provide a systematic and comprehensive review of pre-AGI and AGI-inspired approaches for IoX, focusing on three key components: sensing-layer data management, network-layer protocol optimization, and application-layer decision-making frameworks. Specifically, this survey explores how pre-AGI and AGI-inspired strategies can mitigate IoX bottlenecks by leveraging adaptive sensor fusion, edge preprocessing, and selective attention mechanisms at the sensing layer. At the network layer, the survey examines solutions to challenges such as protocol heterogeneity and dynamic spectrum management, including approaches based on neuro-symbolic reasoning, active inference, and causal reasoning. Furthermore, the survey investigates AGI-inspired frameworks for managing identity and relationship explosion at the application layer. Key findings suggest that emerging AGI-inspired approaches offer novel solutions to sensing-layer data overload, network-layer protocol heterogeneity, and application-layer identity explosion. These solutions include adaptive sensor fusion, edge preprocessing, and semantic modeling. The survey underscores the importance of cross-layer integration, quantum-enabled communication, and ethical governance frameworks for future AGI-driven IoX systems. Finally, the survey identifies unresolved challenges, including computational requirements, scalability, and real-world validation, and calls for further research to fully realize AGI’s potential in addressing IoX bottlenecks. We believe that AGI-enhanced IoX is emerging as a critical research field at the intersection of interconnected systems and advanced AI.

This review explores the evolution from IoT to the Internet of Everything (IoX) within the cyber-physical-social-thinking (CPST) hyperspace, centering on the emerging challenge of ”relationship explosion.” As interconnected systems grow in scale and complexity, the exponential proliferation of internal (e.g., device coordination, data aggregation) and cross-space (e.g., streaming, translating, adapting) relationships leading to scalability, security, and real-time processing challenges. Through a systematic literature review guided by five research questions, we analyze how this relational explosion intensifies across IoX domains—spanning IoT, IoP, and IoTk—and undermines the efficacy of Artificial Narrow Intelligence (ANI) in managing dynamic, heterogeneous environments. This review proposes that Artificial General Intelligence (AGI) offers a transformative solution, enabling adaptive reasoning, cognitive firewalls, and unified decision-making to navigate complex relationship networks. AGI-driven methodologies enhance system resilience, security, and efficiency in aggregating, moderating, and evolving relationships across CPST spaces. The paper outlines a classification of relationship types, evaluates AGI’s advantages over ANI, and proposes a future research roadmap emphasizing ethical governance, human-AGI collaboration, and sustainable architectures. By framing IoX development around the management of relationship explosion, we provide a roadmap for future research, emphasizing interdisciplinary efforts, ethical governance, and sustainable frameworks to foster intelligent, socially aware IoX ecosystems.

The task of Pedestrian Trajectory Prediction (PTP) aims to forecast the future movement paths of pedestrians based on their past behavioral patterns, which is crucial for autonomous systems (e.g., autonomous vehicles and social robots) in path planning and decision-making processes. In recent years, with the rapid advancement of Artificial Intelligence (AI), especially in the field of deep learning, PTP has achieved remarkable breakthroughs. However, this field still faces numerous challenges and unresolved issues that require further research and exploration. This paper provides a comprehensive review and perspectives of the latest advancements in PTP methods, starting with the problem definition and method classification. Then, guided by the key issues at hand, we compare and analyze physics-based, classic Machine Learning (ML)-based, and AI-based methods, and discuss their applicability in various application scenarios. Finally, the paper provides existing datasets and performance metrics, and outlines potential research directions.

Cyberbullying on social media platforms remains a serious threat to digital well-being, requiring intelligent systems capable of detecting both explicit and subtle, emotionally charged abuse. Sentiment analysis (SA) plays a key role by interpreting emotional tone, polarity, and context, offering more nuanced and timely detection than keyword-based models. Emotions like anger, sarcasm, or veiled hostility often precede cyberbullying, especially during impulsive interactions. SA captures these affective cues, improving sensitivity to implicit abuse and coded language. This study presents the first systematic comparison of sentiment-enhanced transformer models such as ALBERT, DeBERTa, ELECTRA, HateBERT, and DeepSeek-coder-1.3b-base, fine-tuned for cyberbullying detection across Twitter (currently X), IMDB, and Amazon. Models were evaluated on predictive performance (Accuracy, Precision, Recall, F1-score), time and cost efficiency (inference time, memory, CPU/GPU use, and energy). ELECTRA + SA outperformed all models, achieving 91.85% accuracy, precision, and recall, and a 91.84% F1-score. It also excelled in efficiency, with 0.069 seconds inference time, 23.92 MB RAM use, 7.2% CPU/GPU usage, and 0.000075 kWh energy consumption, proving highly generalizable, sentiment-sensitive, and suitable for real-time, resource-aware deployment. These results highlight the importance of sentiment integration, dataset diversity, and computational efficiency in building scalable, real-world cyberbullying detection systems.

Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception. However, achieving high efficiency and long sequence recognition remains a challenge. Despite the extensive investigation of temporal deep learning models, such as convolutional neural networks, RNNs, and transformers, their extensive parameters often pose significant computational and memory constraints, rendering them less suitable for resource-constrained mobile health applications. This study introduces HARMamba, an innovative lightweight and versatile HAR architecture that combines selective bidirectional state-space model and hardware-aware design. To optimize real-time resource consumption in practical scenarios, HARMamba employs linear recursive mechanisms and parameter discretization, allowing it to selectively focus on relevant input sequences while efficiently fusing scan and recompute operations. The model employs independent channels to process sensor data streams, dividing each channel into patches and appending classification tokens to the end of the sequence. It utilizes position embedding to represent the sequence order. The patch sequence is subsequently processed by HARMamba Block, and the classification head finally outputs the activity category. The HARMamba Block serves as the fundamental component of the HARMamba architecture, enabling the effective capture of more discriminative activity sequence features. HARMamba outperforms contemporary state-of-the-art frameworks, delivering comparable or better accuracy with significantly reducing computational and memory demands. Its effectiveness has been extensively validated on four publicly available data sets, namely, PAMAP2, WISDM, UNIMIB SHAR, and UCI. The F1 scores of HARMamba on the four data sets are 99.74%, 99.20%, 88.23%, and 97.01%, respectively.

Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields, such as healthcare and assisted living, where real-time understanding of ongoing and upcoming activities is crucial. This article introduces P2LHAP, a novel Patch-to-Label Seq2Seq framework that tackles all three tasks in an efficient single-task model. P2LHAP divides sensor data streams into a sequence of “patches,” served as input tokens, and outputs a sequence of patch-level activity labels, including the predicted future activities. A unique smoothing technique based on surrounding patch labels, is proposed to identify activity boundaries accurately. Additionally, P2LHAP learns patch-level representation by sensor signal channel-independent Transformer encoders and decoders. All channels share embedding and Transformer weights across all sequences. Evaluated on the three public datasets, P2LHAP significantly outperforms the state-of-the-art in all three tasks, demonstrating its effectiveness and potential for real-world applications.

Biosignal representation learning (BRL) plays a crucial role in emotion recognition for game users (ERGU). Unsupervised BRL has garnered attention considering the difficulty in obtaining ground truth emotion labels from game users. However, unsupervised BRL in ERGU faces challenges, including overfitting caused by limited data and performance degradation due to unbalanced sample distributions. Faced with the above challenges, we propose a novel method of biosignal contrastive representation learning (BCRL) for ERGU, which not only serves as a unified representation learning approach applicable to various modalities of biosignals but also derives generalized biosignals representations suitable for different downstream tasks. Specifically, we solve the overfitting by introducing perturbations at the embedding layer based on the projected gradient descent (PGD) adversarial attacks and develop the sample balancing strategy (SBS) to mitigate the negative impact of the unbalanced sample on the performance. Further, we have conducted comprehensive validation experiments on the public dataset, yielding the following key observations: first BCRL outperforms all other methods, achieving average accuracies of 76.67%, 71.83%, and 63.58% in 1D-2 C Valence, 1D-2 C Arousal, and 2D-4 C Valence/Arousal, respectively; second, the ablation study shows that both the PGD module (+7.58% in accuracy on average) and the SBS module (+14.60% in accuracy on average) have a positive effect on the performance of different classifications; third, BCRL model exhibits the certain generalization ability across the different games, subjects and classifiers.

Biosignal sequence real-time prediction (BSRP) is essential for predicting the future emotional experience of game users. However, BSRP for game users faces challenges, including poor real-time performance and limited feature fusion dimensions. To address these issues, we proposed a method for BSRP based on the features fusion of local–global and time–frequency domain (LGTF) for game users, which integrates real-time capabilities with multidimensional features fusion. Specifically, LGTF meets real-time requirements and achieves the features fusion of local–global (LG) through multichannel synchronized adaptive convolution. In addition, LGTF implements the features fusion of interband and intraband in the frequency domain and the features fusion of time–frequency (TF) domain by incorporating the self-attention mechanism and Fourier Transform. Furthermore, we conducted comprehensive validation experiments on LGTF using the public dataset. The results indicate that: first, in the comparison study, LGTF outperformed other methods, achieving the lowest average mean squared error (MSE) and mean absolute error values across different prediction lengths of 0.61 and 0.47, respectively. Second, ablation studies revealed that the addition of TF domain feature fusion and LG feature fusion both have the positive effect on the prediction performance, reducing the average MSE by 0.11 and 0.09, respectively. Third, generalization study shows that LGTF exhibits stable performance and generalization across different subjects and shows performance advantages in specific game scenarios. Fourth, time performance analysis suggests LGTF has the real-time performance. Finally, case study demonstrates that LGTF is practical for predicting game users' future emotions and enhancing their emotional experiences.

Hybrid human–artificial intelligence (H-AI) in the metaverse is facing a growing trend in theoretical research and practical applications. Based on 101 academic papers published from 1996–2024, this article identifies research themes using thematic analysis, technological analysis, comparative analysis, and knowledge integration. The results show that academic interest and attention to H-AI have gradually increased since 2020. Through the results of thematic analysis, this article synthesizes five applications of H-AI domains: industry field, medical field, entertainment field, transportation field, and other fields. By analyzing the impact of H-AI on the integration of the virtual and real world, the role of H-AI in both the virtual and real worlds and the improvements over artificial intelligence will be outlined. This article also identifies challenges and responses that need further attention: disputes over responsibility ownership, bubble issues, lack of trust, and high-cost issues. This article looks ahead to add new skills to the metaverse platform, drive the metaverse as the next wave of the digital economy, promote the new field of metaverse combined with traditional, increase metaverse assistance to people with disabilities, and promote the systematization of the metaverse. This article helps enhance researchers' and practitioners' understanding of H-AI in the metaverse while raising awareness about the current research frontiers and potential future directions.

Cyberbullying significantly impacts mental health by adversely affecting victims’ psychological well-being. It is a prevalent issue on social media platforms, necessitating effective real-time detection systems to identify harmful content. However, current detection systems face challenges related to performance, dataset quality, time efficiency, and computational costs. This study compares existing text classification techniques for cyberbullying detection, evaluating their effectiveness on social media platforms. Large language models such as BERT, RoBERTa, XLNet, DistilBERT, and GPT-2.0 are assessed for their suitability. Results show that BERT achieves optimal performance, with 95% accuracy, precision, recall, and F1 score; a 5% error rate; 0.053 seconds inference time; 35.28 MB RAM usage; 0.4% CPU/GPU utilization; and 0.000263 kWh energy consumption. These findings highlight that while generative AI models are powerful, fine-tuned models often outperform them when adapted to specific datasets and tasks.

Sustainable AI considers both the environmental impact of AI technologies and their role in advancing global sustainability. This study provides a comprehensive analysis of emerging trends in sustainable AI, highlighting energy-efficient algorithms, green data centers, AI-driven resource management, and ethical governance. It emphasizes AI's dual nature, its potential to drive climate action and socio-economic progress, alongside risks such as carbon emissions, e-waste, and digital inequality. The study underscores the need for interdisciplinary collaboration, inclusive policies, and standardized sustainability metrics to ensure responsible AI deployment. By identifying barriers and proposing strategies, it offers guidance to researchers, policymakers, and industry leaders on aligning AI with long-term environmental, ethical, and social objectives.

In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supporting the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the networks lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, we decomposed our problem into two subproblems. Connectivity, coverage subproblem and network lifespan optimization subproblem. For the Connectivity and coverage subproblem, the optimal connectivity and coverage are optimized by strategically deploying UAV fog nodes in a manner that maximizes network coverage, thoughtfully considering the distinctive constraints that emerge in these high-pressure situations where lives are at stake. We shape our UAV fog deployment problem as a multi-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. For the network lifespan subproblem, after determining the optimal connectivity and coverage of UAV nodes and users within the entire network, the network lifespan optimization subproblem is greatly simplified and efficiently solved via a one-dimensional swapping method. After conducting thorough experiments using our proposed architecture across diverse scenarios, our method consistently surpasses existing approaches. It effectively tackles issues like restricted connectivity and potential node failures. This advancement in deployment efficiency notably enhances rescue operations, enabling us to aid the maximum number of affected individuals swiftly during critical and time-pressing situations.

The massive generation of time-series data by large-scale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a predominant use of the channel dependence (CD) strategy (where each channel represents a univariate sequence). Current state-of-the-art (SOTA) models primarily rely on the channel independence (CI) strategy. The CI strategy treats channel multichannel series as separate single-channel series, expanding the data set to improve generalization performance and avoiding interchannel correlation that disrupts long-term features. However, the CI strategy faces the challenge of interchannel correlation forgetting. To address this issue, we propose an innovative Mixed Channels strategy, combining the data expansion advantages of the CI strategy with the ability to mitigate interchannel correlation forgetting. Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features. The model blends a specific number of channels, leveraging an attention mechanism to effectively capture interchannel correlation information when modeling long-term features. Experimental results demonstrate that the Mixed Channels strategy outperforms pure CI strategy in multivariate time-series forecasting tasks.

The Metaverse, as a paradigm continuously evolving in the next generation of the Internet, aims to integrate various network applications. However, existing applications on the Internet, such as serve computing, and edge computing, have highly complex technical requirements. These applications face compatibility issues with the Metaverse in terms of protocols, applications, and services. So they can’t be directly integrated into the Metaverse. How to efficiently deploy service computing in the Metaverse has become a hotspot research area. Moreover, Metaverse implements innovative services to offer individuals more immersive experiences, such as virtual reality services and augmented Reality services. These new services demand high computational resources including computing power, network performance, data security, etc. Ensuring optimal service quality for these new services in the Metaverse is another critical aspect of Metaverse research. To address the aforementioned challenges, Meta-services, designed to describe, discover, compose, and manage other services, are gradually attracting widespread attention and research. In this paper, we provide a comprehensive review, analysis, and discussion of existing research work. We summarize the services computing and novel services in Metaverse and categorize the meta-services framework into three layers: meta-bottom layer (meta-data), meta-middle layer (meta-models, meta-objects, and meta-languages), and meta-top layer (meta-programming). Based on the meta-services framework, we discuss some current challenges, as well as provide future research directions. We hope that this paper can enable readers to quickly understand the reasons for each problem and the current research progress, thereby providing guidance and motivation for further research in this field.

Using portable tools to monitor and identify daily activities has increasingly become a focus of digital healthcare, especially for elderly care. One of the difficulties in this area is the excessive reliance on labeled activity data for corresponding recognition modeling. Labeled activity data is expensive to collect. To address this challenge, we propose an effective and robust semi-supervised active learning method, which combines the mainstream semi-supervised learning method with expert collaboration. Our method takes a user's trajectory as the only input. In addition, this novel method uses expert collaboration to judge the valuable samples further to enhance its performance. Our method relies on very few semantic activities, outperforms all baseline activity recognition methods, and is close to the performance of supervised learning methods. On the adlnormal dataset with 200 semantic activities data, our work achieved an accuracy of 89.07%, and supervised learning has 91.77%. Our ablation study validated the components in our method using a query strategy and a data fusion approach.

The Social Internet of Things (SIoT) is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item interactions using contextual information, devices’ SIoT relationships, and correlation social groups but these schemes do not account for latent semantic item–item structures underlying the sparse multimodal contents in SIoT environment. In this article, we propose a latent-based SIoT recommendation system that learns item–item structures and aggregates multiple modalities to obtain latent item graphs which are then used in graph convolutions to inject high-order affinities into item representations. Experiments showed that the proposed recommendation system outperformed state-of-theart SIoT recommendation methods and validated its efficacy at mining latent relationships from multimodal features.

Knowledge question and answering (Q&A) is widely used. However, most existing semantic parsing methods in Q&A usually use cascading, which can incur error accumulation. In addition, using only one institution’s Q&A data definitely will limit the Q&A performance, while data privacy prevents sharing between institutions. This article proposes a knowledge graph-based reinforcement federated learning (KGRFL)-based Q&A approach to address these challenges. We design an end-to-end multitask semantic parsing model [MSP-bidirectional and auto-regressive transformers (BART)] that identifies question categories while converting questions into SPARQL statements to improve semantic parsing. Meanwhile, a reinforcement learning (RL)-based model fusion strategy is proposed to improve the effectiveness of federated learning, which enables multi-institution joint modeling and data privacy protection using cross-domain knowledge. In particular, it also reduces the negative impact of low-quality clients on the global model. Furthermore, a prompt learning-based entity disambiguation method is proposed to address the semantic ambiguity problem because of joint modeling. The experiments show that the proposed method performs well on different datasets. The Q&A results of the proposed approach outperform the approach of using only a single institution. Experiments also demonstrate that the proposed approach is resilient to security attacks, which is required for real applications.

Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the skin conductance-based APs and non-APs recognition with machine learning, which could assist in APs detection and localization in clinical practice. Firstly, we collect skin conductance of traditional Five-Shu Point and their corresponding non-APs with wearable sensors, establishing a dataset containing over 36000 samples of 12 different AP types. Then, electrical features are extracted from the time domain, frequency domain, and nonlinear perspective respectively, following which typical machine learning algorithms (SVM, RF, KNN, NB, and XGBoost) are demonstrated to recognize APs and non-APs. The results demonstrate XGBoost with the best precision of 66.38%. Moreover, we also quantify the impacts of the differences among AP types and individuals, and propose a pairwise feature generation method to weaken the impacts on recognition precision. By using generated pairwise features, the recognition precision could be improved by 7.17%. The research systematically realizes the automatic recognition of APs and non-APs, and is conducive to pushing forward the intelligent development of APs and Traditional Chinese Medicine theories.

Autonomous driving systems critically depend on accurately classifying traffic signs, a task that is jeopardized by adversarial attacks. This paper focuses on the relatively unexplored domain of Traffic Sign Classification (TSC) in the context of detecting adversarial attacks. We conduct a rigorous evaluation of six prominent adversarial example detection methods, each representing a distinct detection category and well-regarded within the research community. Our methodology involves adapting these techniques, originally developed for general image classification (IC), to the unique challenges posed by traffic sign images, characterized by their complexity due to factors like varying environmental conditions and a large number of classes. Our study reveals insights into their effectiveness, with the Natural Scene Statistics (NSS) method outperforming others with 83.42%, 86.42%, and 99.96% detection rates; 0.08%, 0.05%, and 0.04%, false positive rates; and 0.02sec, 0.01sec, and 0.02sec detection times for Chinese, Belgium and German traffic sign datasets, respectively. NSS’s superiority is crucial for autonomous vehicles. Our study also sheds light on the often-neglected aspect of detection time in IC, which plays a vital role in ensuring operational efficiency and safety for autonomous vehicles. Our research highlights the need for customized defense strategies tailored to the TSC domain, considering our evaluation’s findings. By identifying promising techniques for detecting adversarial attacks in TSC, we contribute to enhancing the safety and robustness of autonomous driving systems. This study fills a critical knowledge gap, providing valuable insights into understanding and defending against adversarial attacks, specifically in the TSC context.

Intelligent transportation systems (ITS) have made significant advancements in enhancing transportation safety, reliability, and efficiency. However, challenges persist in security, privacy, data management, and integration. Metaverse, an emerging technology enabling immersive and simulated experiences, presents promising solutions to overcome these challenges. By establishing secure communication channels, facilitating virtual simulations for safe testing and training, and enabling centralized data management with real-time analytics, metaverse offers a transformative approach to address these challenges. While metaverse has found extensive applications across industries, its potential in transportation remains largely untapped. This comprehensive review delves into the integration of the metaverse in ITS, exploring key technologies like virtual reality, digital twin, blockchain, and artificial intelligence, and their specific applications in the context of ITS. Real-world case studies, research projects, and initiatives are compiled to showcase the metaverse’s potential for ITS. It also examines the societal, economic, and technological implications of metaverse integration in ITS and highlights the associated integration challenges. Lastly, future research directions are identified to unlock the metaverse’s full potential in enhancing transportation systems.

Time-of-flight magnetic resonance angiography (TOF-MRA) is a common cerebrovascular imaging. Accurate and automatic cerebrovascular segmentation in TOF-MRA images is an important auxiliary method in clinical practice. Due to the complex semantics and noise interference, the existing segmentation methods often fail to pay attention to topological correlation, resulting in the neglect of branch vessels and vascular topology destruction. In this paper, we proposed a topology regularization adversarial model for cerebrovascular segmentation in TOF-MRA images. Firstly, we trained a self-supervised model to learn spatial semantic layout in TOF-MRA images by image context restoration. Subsequently, we exploited initialization based on the self-supervised model and constructed an adversarial model to accomplish parameter optimization. Considering the limitations of uneven distribution of cerebrovascular classes, we introduced skeleton structures as discriminative features to enhance vessel topological strength. We constructed some latest models to test our method over two datasets. Results show that the proposed model attains the highest score. Therefore, our method can obtain accurate connectivity information and higher graph similarity, leading more meaningful clinical utility.

A trust management system (TMS) is an integral component of any Internet of Things (IoT) network. A reliable TMS must guarantee the network security, data integrity, and act as a referee that promotes legitimate devices, and punishes any malicious activities. Trust scores assigned by TMSs reflect devices’ reputations, which can help predict the future behaviors of network entities and subsequently judge the reliability of different entities in the IoT networks. Many TMSs have been proposed in the literature, these systems are designed for small-scale trust attacks and can deal with attacks where a malicious device tries to undermine TMS by spreading fake trust reports. However, these systems are prone to large-scale trust attacks. To address this problem, in this article, we propose a TMS for large-scale IoT systems called Trust2Vec, which can manage trust relationships in large-scale IoT systems and can mitigate large-scale trust attacks that are performed by hundreds of malicious devices. Trust2Vec leverages a random-walk network exploration algorithm that navigates the trust relationship among devices and computes trust network embeddings, which enables it to analyze the latent network structure of trust relationships, even if there is no direct trust rating between two malicious devices. To detect large-scale attacks, such as self-promoting and bad-mouthing, we propose a network embeddings community detection algorithm that detects and blocks communities of malicious nodes. The effectiveness of Trust2Vec is validated through large-scale IoT network simulation. The results show that Trust2Vec can achieve up to 94% mitigation rate in various network settings.

It is well known that the metaverse, also named general cyberspace (GC), is virtual-real fusion spaces, consisting of a virtual space, namely, cyberspace and virtual-real spaces, namely, cyber-enabled physical, social, and thinking (cyber-enabled) spaces. This article discusses the open issues of the metaverse in terms of science and technology and proposes a new discipline and interdiscipline hierarchy for the metaverse (GC), namely, cyberology first. Then, it explores various relevant standards of discipline classification and a discipline and interdiscipline hierarchy based on physical, social, and thinking spaces, and investigates the cyberspace and cyber-enabled spaces. On the basis of the above research, this article enriches the contents of cyberology in two terms: 1) the disciplines in cyberspace and 2) the interdisciplines in cyber-enabled spaces. Finally, this article gives predictions of cyberology on the future development of the metaverse from the aspects of cyber–physical space, cyber–social space and cyber-thinking space.

In recent years, the concept of the Metaverse has attracted considerable attention. This article provides a comprehensive overview of the Metaverse. First, the development status of the Metaverse is presented. We summarize the policies of various countries, companies, and organizations relevant to the Metaverse, as well as statistics on the number of Metaverse-related publications. Characteristics of the Metaverse are identified: 1) multitechnology convergence; 2) sociality; and 3) hyper-spatio-temporality. For the multitechnology convergence of the Metaverse, we divide the technological framework of the Metaverse into five dimensions. For the sociality of the Metaverse, we focus on the Metaverse as a virtual social world. Regarding the characteristic of hyper-spatio-temporality, we introduce the Metaverse as an open, immersive, and interactive 3-D virtual world which can break through the constraints of time and space in the real world. The challenges of the Metaverse are also discussed.

Contrastive learning is an emerging and important self-supervised learning paradigm that has been successfully applied to sensor-based human activity recognition (HAR) because it can achieve competitive performance relative to supervised learning. Contrastive learning methods generally involve instance discrimination, which means that the instances are regarded as negatives of each other, and thus their representations are pulled away from each other during the training process. However, instance discrimination could cause overclustering, meaning that the representations of instances from the same class could be overly separated. To alleviate this overclustering phenomenon, we propose a new contrastive learning framework to select negatives by clustering in HAR, which is named clustering for contrastive learning in human activity recognition (ClusterCLHAR). First, ClusterCLHAR clusters the instance representations, and for each instance, only those from different clusters are regarded as negatives. Second, a new contrastive loss function is proposed to mask the same-cluster instances from the negative pairs. We evaluate ClusterCLHAR on three popular benchmark data sets: 1) USC-HAD; 2) MotionSense; and 3) UCI-HAR, using the mean F1-score as an evaluation metric for downstream tasks. The experimental results show that ClusterCLHAR outperforms all the state-of-the-art methods applied to HAR in self-supervised learning and semi-supervised learning.

Video data is the biggest IoT data which is challenging for effective analysis with good performance. Object misdetection is usually inevitable in edge-based distributed cross-scene video analysis. Traditional centralized model training can potentially result in edge data leakage. Even though joint model can be trained with federated learning while maintaining data privacy, the size of gradient data transmitted is large for computer vision models used. To address these problems, this article proposed an accurate and efficient federated learning-based edge intelligence for effective video analysis method called EIEVA-AEFL. In EIEVA-AEFL, a federation misdetection reinforcement network (FMRN) is designed to alleviate the misdetection problem. FMRN contains a vanilla object detection network and a misdetection reinforcement branch, which finetunes object detection via feature re-extraction to reduce object misdetection. To reduce the communication cost in training, an efficient federated learning strategy is designed. In this strategy, an oscillation suppression loss function is proposed to suppress the loss fluctuation resulting from data on edge clients. Average accuracy and recall increase 0.5 and 0.7 with FMRN on the Microsoft common objects in context (MS COCO) data set, respectively, and with improvements of 4.5 and 5.5 with FMRN on our self-made mis-detection data set, respectively. EIEVA-AEFL can reduce the training speed on the premise of ensuring the accuracy of the model. The model parameters, data amount, transmission delay, and convergence epochs on EIEVA-AEFL model training are reduced by 78%, 89%, 84%, and 36%, respectively.

Federated learning can collaboratively train AI models while protecting data privacy. In practical industry environment, non-independent and identically distributed (Non-IID) characteristics of data affect the effectiveness of federated learning. Personalized federated learning can help resolve this, but it cannot adapt to unknown data. In addition, practical applications also call for trusted training environment and remain stable when there are security threats. In this article, we propose a credible federated self-learning (CFSL), based on the idea of hypernetwork supported by blockchain to achieve secured, credible, personalized federated self-learning, especially, for unknown data in Non-IID environment. Extensive experiments on three Non-IID data sets demonstrate the capabilities on adaptive resilience for security attacks and on accuracy of recognizing unknown objects, with good performance at the same time. CFSL outperforms the existing personalized federated learning methods, with an increase in average accuracy by 4.11%.

Emotional arousal, an essential dimension of game users’ experience, plays a crucial role in determining whether a game is successful. Game users’ emotion arousal assessment (GUEA) is of great importance. However, GUEA often faces challenges, such as selecting emotion-inducing games, labeling emotional arousal, and improving accuracy. In this study, the scheme for verifying the effectiveness of emotion-induced games is proposed so that the selected games can induce the target emotions. In addition, the personalized arousal label generation method is developed to reduce the errors caused by individual differences among subjects. Furthermore, to improve the accuracy of GUEA, the Breath Rate Variability (BRV) signal is used as a GUEA indicator along with commonly used physiological signals. Comparative experiments on GUEA based on multimodal physiological signals are conducted. The experimental result shows that the accuracy of GUEA is improved by adding the BRV signal, up to 92%.

Vehicular social networking is an emerging application of the Internet of Vehicles (IoV) which aims to achieve seamless integration of vehicular networks and social networks. However, the unique characteristics of vehicular networks, such as high mobility and frequent communication interruptions, make content delivery to end-users under strict delay constraints extremely challenging. In this paper, we propose a social-aware vehicular edge computing architecture that solves the content delivery problem by using some vehicles in the network as edge servers that can store and stream popular content to close-by end-users. The proposed architecture includes three main components: 1) the proposed social-aware graph pruning search algorithm computes and assigns the vehicles to the shortest path with the most relevant vehicular content providers. 2) the proposed traffic-aware content recommendation scheme recommends relevant content according to its social context. This scheme uses graph embeddings in which the vehicles are represented by a set of low-dimension vectors (vehicle2vec) to store information about previously consumed content. Finally, we propose a deep reinforcement learning (DRL) method to optimise the content provider vehicle distribution across the network. The results obtained from a real-world traffic simulation show the effectiveness and robustness of the proposed system when compared to the state-of-the-art baselines.

The rapid growth of the Internet of Things (IoT) and the increasing number of connected devices have propelled the proliferation of offered applications, causing “applications exploding.” In the context of IoT, filtering and selecting the most relevant applications in a given situation is a challenging task. Thus, developing techniques that can alleviate applications exploding and meet users’ requirements is highly demanded for IoT development. This survey focuses on applications exploding in the IoT and reviews some of the existing techniques, such as intelligent sensing, content distribution network, microservices, and 5G, which help mitigate the effects of applications exploding. Furthermore, the survey discusses how to describe user requirements and offer application services to better match the two. In addition, this survey presents the smart home as an instance of typical IoT applications and explores how adaptive users’ requirements for food ordering can be better met when various food provider applications are available for choice. Finally, partially resolved and unresolved bottlenecks brought by applications exploding are put forward to be further researched by the technical and scientific community.

Aeronauticalad hocnetworking (AANET) of the air vehicles is envisioned to support future enhanced applications, such as free flight and in-flight Internet, and AANET serves as the middle layer of the air–space–ground integrated network, bridging the space and the ground components. However, intermittent connectivity is the greatest challenge to an AANET. To deal with the intermittence, the air vehicle acts as a relay in an AANET using the buffer onboard to temporally cache the data when an interruption occurs, known as opportunistic transmission. For the highly dynamic topology of an AANET, we use a sampled dynamic graph to capture significant variations while ignoring trivial changes for avoiding extra complexity. And thus we formulate a coalitional game incorporating with the dynamic graph for the AANET to obtain the optimal transmission schedule in terms of the effective throughput with limited transmission delay. The corresponding coalitional dynamic graph game algorithm will then generate an approximately optimal AANET formation, which converges to Nash equilibrium within finite iterations. The simulations conducted with the real flight data show that 700 Mb of buffer size onboard and 1400 Mb of buffer in the Internet gateway station (IGS) are the optimal settings for the opportunistic transmission, and the coalitional dynamic graph game algorithm outperforms the geographic location-based greedy perimeter stateless routing algorithm in terms of the total received data amounts.

Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to thebestdevices, according to the context and task requirements. The main purpose of this article is to study the different connotations of the concept of TAefficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. This article first analyzes the domains of connected devices where TA has an important role, which brings to this classification: 1) Internet of Things (IoT); 2) sensor and actuator networks (SANs); 3) multirobot systems (MRSs); 4) mobile crowdsensing (MCS); and 5) unmanned aerial vehicles (UAV). This article then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. This article also discusses the issues that need to be addressed by future research activities, i.e., allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; the list of tasks performed by objects; and designing TA strategies where network service providers have a role in TA functionalities’ provisioning.

Regular safety inspection is critical to reduce safety risk in industry. Applying the consortium blockchain technology to safety inspection can ensure the effectiveness of the inspection process and tracing of problems. However, there are two major issues when using conventional consortium blockchain. It is challenging to guarantee the authenticity of the retrieved data source, and meanwhile, achieving a balance between performance and security is not easy. Hence, this article proposes a blockchain-based performance-security balanced safety inspection framework (PSB-SIF), in which a safety inspection box is designed to ensure the authenticity of the inspector’s identity while inspection logic is executed automatically via smart contracts. In addition, this article also proposes a novel credit scoring-based Byzantine fault-tolerant (BFT) consensus algorithm, named safety inspection BFT consensus algorithm (SIBFT), which is used to balance the performance and security of consensus network in a safety inspection. We evaluate the proposed approach by comparing with the solutions using RAFT, Practical BFT (PBFT), and SIBFT consensus algorithms in terms of throughput, transaction latency, scalability, and security of PSB-SIF. The evaluation results show that PSB-SIF is efficient for all these quality metrics.

With the convergence of modern computing technology and social sciences, both theoretical research and practical applications of social computing have been extended to new domains. In particular, social computing was significantly influenced by the recent advances of artificial intelligence (AI). However, the conventional technologies of AI have various drawbacks in dealing with complicated and dynamic problems. Such deficiency can be rectified by hybrid human-artificial intelligence (H-AI), which integrates both human intelligence and AI into one unity, forming a new enhanced intelligence. H-AI in dealing with social problems shows some advantages over the conventional AI. This article firstly reviews the latest research progresses of AI in social computing. Secondly, it summarizes typical challenges AI faces in social computing, which motivate the necessity to introduce H-AI to tackle social-oriented problems. Finally, we discuss the concept of H-AI and propose a holistic architecture of H-AI in social computing, which consists of three layers: object layer, intelligent processing layer, and application layer. The proposed architecture shows that H-AI has significant advantages over AI in solving social problems.

With the continuous development of Artificial Intelligence (AI), autonomous driving has become a popular research area. AI enables the autonomous driving system to make a judgment, which makes studies on autonomous driving reaches a period of booming development. However, due to the defects of AI, it is not easy to realize a general intelligence, which also limits the research on autonomous driving. In this paper, we summarize the existing architectures of autonomous driving and make a taxonomy. Then we introduce the concept of hybrid human-artificial intelligence (H-AI) into a semi-autonomous driving system. For making better use of H-AI, we propose a theoretical architecture based on it. Given our architecture, we classify and overview the possible technologies and illustrate H-AI’s improvements, which provides a new perspective for the future development. Finally, we have identified several open research challenges to attract the researchers for presenting reliable solutions in this area of research.

The appearance of fatigue is not conducive to driving activities because this state can affect driving performance and even cause life-threatening consequences. To reduce various traffic accidents caused by fatigue, researchers began to explore effective fatigue monitoring systems to detect this unfavorable state early. This paper systematically surveys the research on driving fatigue monitoring from three aspects: data acquisition, feature extraction, and fatigue assessment. Furthermore, this paper analyzes the differences between active and passive fatigue, fatigue and sleepiness, as well as fatigue and transportation scenarios. Finally, some open issues on driving fatigue monitoring are proposed, which will be the key research directions for future developments.

ZigBee is one of the communication protocols used in the Internet of Things (IoT) applications. In typical deployment scenarios involving low-cost and low-power IoT devices, many communication features are disabled, consequently affecting the security offered by ZigBee. The ZigBee specification assumes that deployment of frame counters is sufficient to mitigate replay attacks in secure ZigBee networks. However, we demonstrate that it is insufficient in this paper (i.e., the network is no longer secure after the coordinator restarts). As a countermeasure, we present a timestamp-based scheme to mitigate replay attacks. Our mitigation strategy does not consume power significantly, and fully powered devices will be responsible for providing power-constrained devices with the current timestamp. The proposed scheme is designed for all ZigBee topologies and different states of ZigBee End Devices (ZEDs). Findings from our evaluation show that the proposed scheme can successfully mitigate replay attacks, with no significant network performance degradation even assuming a worst-case scenario (i.e., many devices are sending data simultaneously).

Researchers have used deep neural networks (DNN) to reconstruct visual stimuli from human brain activities. In particular, convolution neural networks (CNN) processing functional magnetic resonance imaging (fMRI) signals can reconstruct visual stimulation in brain activity. Although visual stimulation has been successfully reconstructed in brain activity, the research on visual stimulation reconstruction is still in initial stage. The decoding model of brain activity based on fMRI is facing three challenges: the mapping ability of the decoding model; the limited paired data of visual stimulation and brain activity; and the presence of noise in fMRI signals. This paper reviews the potential solutions to the above difficulties and the future development directions.

The prevalence and large-scale uptake of the Internet of Things (IoT) have led to a growing trend of Identification Exploding, namely heterogeneous entities respectively identified to provide convenient intelligent services. To address challenges under Identification Exploding, unified modeling has become a promising approach to generalizing identification for heterogeneous entities in IoT. This paper surveys and discusses Identification Exploding's background to explore the possibility of unified modeling as one of the solutions. Meanwhile, challenges of realizing unified modeling are discussed. After that, comprehensive reviews on the latest modeling approaches and methods covering various IoT entities are carried out, including sensed entities and sensing devices. Special attention has been paid to modeling under resource-constrained IoT environments and relevant modeling-based industry solutions with critical analysis. It is proved that unified modeling is extremely significant under Identification Exploding Tendency in the IoT era.

Speech emotion recognition, an important branch of affective computing, has attracted much attention recently, which is of great significance for the realization of natural and harmonious human-robot interaction. Many researches have been carried out and remarkable results have been achieved in this field. At the same time, there are still many problems to be tackled urgently. Therefore, it is necessary to summarize the previous works and find out the problems in speech emotion recognition, so as to provide guidance for further research. This paper systematically summarizes the general process of speech emotion recognition, including speech emotional databases and various leading emotion classification models. By listing some outstanding works of speech emotion recognition in recent 20 years, this paper compares and analyzes the highlights and shortcomings of these works. It can be seen that people show more interest in deep learning in which features are mostly extracted automatically than the traditional machine learning methods. Finally, the main problems in the field of speech emotion recognition and the direction of further exploration are summarized in order to promote speech emotion recognition to a new stage.

The Internet of People (IoP) is a human-centric computing paradigm, where the people are not considered merely as end users, but become the center of the computing architecture. The computing model of IoP requires that the system understand the social characters of the users, such as the users' emotions, personality types, and interests. User interest detection is an important task in IoP. In this article, we propose a user interest detection framework for user interest detection in the context of a signed social network for IoP. First, we propose a new proximity function that measures the similarity between users based on their interests/disinterests with respect to the relative popularity of these interests/disinterests among other users. Second, we propose a greedy community detection algorithm that detects communities of users with common interests with possible overlapping communities using the adaptive clique relaxation technique. Finally, we introduce a novel link prediction algorithm named ComPath that leverages the community affiliation information to predict the unknown links in heterogeneous signed social networks. Experimental results show that ComPath outperforms other computational-based baselines as well as deep-learning-based baselines especially in the cold start phase with only a few training data.

With the recent advances of the Internet of Things (IoT), and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as artificial social intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This article discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

With the development of the cloud-based Internet of Things (IoT), people and things can request services, access data, or control actuators located thousands of miles away. The entity authentication of the remotely accessed devices is an essential part of the security systems. In this vein, physical unclonable functions (PUFs) are a hot research topic, especially for generating random, stable, and tamper-resistant fingerprints. This article proposes a lightweight, robust static random access memory (SRAM)-PUF-based entity authentication scheme to guarantee that the accessed end devices are trustable. The proposed scheme uses challenge-response pairs (CRPs) represented by reordered memory addresses as challenges and the corresponding SRAM cells' startup values as responses. The experimental results show that our scheme can efficiently authenticate resources-constrained IoT devices with a low computation overhead and small memory capacity. Furthermore, we analyze the SRAM-PUF by testing the PUF output under different environmental conditions, including temperature and magnetic field, in addition to exploring the effect of writing different values to the SRAM cells on the stability of their startup values.

Social Internet of Things comes as a new paradigm of Internet of Things to solve the problems of network discovery, navigability, and service composition. It aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings. In IoT scenarios, a device can offer multiple services and different devices can offer the same services with different parameters and interest factors. The proliferation of offered services led to difficulties during service filtering and customization, this problem is known as services explosion. The selection of a suitable service that fits the requirements of the applications and devices is a challenging task. Several works have addressed service discovery, composition, and selection in IoT. However, these works did not emphasize on the fact that incorporating the users’ social features can increase the efficiency of the recommended services and help us to offer context-aware services. In this article, we present a service recommendation system that takes advantage of the social relationships between devices’ owners, where the recommendation is based on the different relationships between the service requester and service provider. Experimental results show, in the context of IoT, that incorporating the users’ social relationships in service recommendation increases the accuracy and diversity of the offered services.

With the emergence of mobile computing offloading paradigms, such as mobile-edge computing (MEC), many Internet of Things applications can take advantage of the computing powers of end devices to perform local tasks without the need to rely on a centralized server. Computation offloading is becoming a promising technique that helps to prolong the device's battery life and reduces the computing tasks' execution time. Many previous works have discussed task offloading to the cloud. However, these schemes do not differentiate between types of application tasks. It is not reasonable to offload all application tasks into the cloud. Some application tasks with low computing and high communication cost are more suitable to be executed on the end devices. On the other hand, most resources on the end devices are idle and can be used to process tasks with low computing and high communication cost. In this article, a three-layer task offloading framework named DCC is proposed, which consists of the device layer, cloudlet layer and cloud layer. In DCC, the tasks with high computing requirement are offloaded to the cloudlet layer and cloud layer. Whereas tasks with low computing and high communication cost are executed on the device layer, hence DCC avoids transmitting large amount of data to the cloud, and can effectively reduce the processing delay. We have introduced a greedy task graph partition offloading algorithm, where the tasks scheduling process is assisted according to the device computing capabilities following a greedy optimization approach to minimize the tasks communication cost. To show the effectiveness of the proposed framework, We have implemented a facial recognition system as usecase scenario. Furthermore, experiment and simulation results show that DCC can achieve high performance when compared to state-of-the-art computational offloading techniques.

As Internet of Things (IoT) is a fascinating paradigm in which all things and objects are connected together, it holds a significant position in fostering intelligent high-level services. However, the future IoT architecture is still under evolution profiting from the overwhelming development of cyberspace and cyber technologies. Based on the traditional physical-based IoT, social-inspired Internet of People (IoP) and brain-abstracted Internet of Thinking (IoTk), an intelligent embryo of cyber-enabled Internet of X (IoX) is being established where all things, entities, people and thinking are interacted seamlessly. In this article, we clearly introduce the cyber-enabled IoX from perspective of both ubiquitous connections and space convergence, and design an architecture with four pillars, namely, things, people, thinking and cyberentities in respective spaces. In addition, we analyze the fundamental issues in IoX development, such as information exploding, link exploding and application exploding from the view of ubiquitous connections, entity exploding and relationship exploding on the basis of space convergence, and service exploding from overall aspects, where potential solutions are discussed at the same time. The intelligent cyber-enabled IoX will be the cornerstone for future techniques and applications, and proves to be the solid foundation for upcoming intelligent and proactive era.

Physical space appeared with the birth of the earth. With the emergence of social space, thinking space, and cyberspace (STCs), things in physical space are continuously enriched. Consequently, the PhiNet, an abstracted network concept such as relationships between things, is becoming more complex. Physical space plays a fundamental role in promoting the connection and development with the other three spaces. We think that PhiNet of Things (PoT) is a unified description of pure physical space and the evolving physical space affected by STCs. While the Internet of Things (IoT) is described from a cyber view, in this article, the definition of PoT is put forward from a natural view. Besides, the evolution of PoT is identified by the time sequence in which the four spaces appeared. At each stage, research is carried out from two perspectives of things and PhiNet, and two specific examples are presented to illustrate the change process of things and the related PhiNet. In addition, two applications are listed to explain the usability of PoT in the current development stage. Finally, the article gives the possible future development direction of PoT. The purpose of this article is to illustrate the fundamental role of physical space in the continuous development through time sequence.

Today, the Internet of Things (IoT) is increasingly flourishing with establishing ubiquitous connections between smart devices and objects, and by 2020, there will be a total of 30 billion connected things reported by IDC. The unprecedented data explosion provides immense opportunities for valuable information mining. At the same time, it also floods the infrastructure with tremendous values it necessarily handles and proposes high challenges to traditional data storing or processing techniques. On the other hand, artificial intelligence (AI) has become a key component for many applications that profoundly change our lives. Machine learning, especially deep learning (DL) technologies, vastly improves traditional computer science and networking technologies. The convergence of AI and IoT enables data to be quickly explored and turned into significant decisions. For companies and enterprises, AI enhances the speed and accuracy of data processing for instant market strategies.

Relevant research has shown the potential to understand the game user experience (GUX) more accurately and reliably by measuring the user’s psychophysiological responses. However, the current studies are still very scarce and limited in scope and depth. Besides, the low-detection accuracy and the common use of the professional physiological signal apparatus make it difficult to be applied in practice. This article analyzes the GUX, particularly flow experience, based on users’ physiological responses, including the galvanic skin response (GSR) and heart rate (HR) signals, captured by low-cost wearable devices. Based on the collected data sets regarding two test games and the mixed data set, several classification models were constructed to detect the flow state automatically. Hereinto, two strategies were proposed and applied to improve classification performance. The results demonstrated that the flow experience of game users could be effectively classified from other experiences. The best accuracies of two-way classification and three-way classification under the support of the proposed strategies were over 90% and 80%, respectively. Specifically, the comparison test with the existing results showed that Strategy1 could significantly reduce the negative interference of individual differences in physiological signals and improve the classification accuracy. In addition, the results of the mixed data set identified the potential of a general classification model of flow experience.

Real-time intelligent video processing on embedded devices with low power consumption can be useful for applications like drone surveillance, smart cars, and more. However, the limited resources of embedded devices is a challenging issue for effective embedded computing. Most of the existing work on this topic focuses on single device based solutions, without the use of cloud computing mechanisms for parallel processing to boost performance. In this paper, we propose a cloud platform for real-time video processing based on embedded devices. Eight NVIDIA Jetson TX1 and three Jetson TX2 GPUs are used to construct a streaming embedded cloud platform (SECP), on which Apache Storm is deployed as the cloud computing environment for deep learning algorithms (Convolutional Neural Networks - CNNs) to process video streams. Additionally, self-managing services are designed to ensure that this platform can run smoothly and stably, in the form of a metric sensor, a bottleneck detector and a scheduler. This platform is evaluated in terms of processing speed, power consumption, and network throughput by running various deep learning algorithms for object detection. The results show the proposed platform can run deep learning algorithms on embedded devices while meeting the high scalability and fault tolerance required for real-time video processing.

A recommendation system is an integral part of any modern online shopping or social network platform. The product recommendation system as a typical example of the legacy recommendation systems suffers from two major drawbacks: recommendation redundancy and unpredictability concerning new items (cold start). These limitations take place because the legacy recommendation systems rely only on the user's previous buying behavior to recommend new items. Incorporating the user's social features, such as personality traits and topical interest, might help alleviate the cold start and remove recommendation redundancy. Therefore, in this article, we propose Meta-Interest, a personality-aware product recommendation system based on user interest mining and metapath discovery. Meta-Interest predicts the user's interest and the items associated with these interests, even if the user's history does not contain these items or similar ones. This is done by analyzing the user's topical interests and, eventually, recommending the items associated with the user's interest. The proposed system is personality-aware from two aspects; it incorporates the user's personality traits to predict his/her topics of interest and to match the user's personality facets with the associated items. The proposed system was compared against recent recommendation methods, such as deep-learning-based recommendation system and session-based recommendation systems. Experimental results show that the proposed method can increase the precision and recall of the recommendation system, especially in cold-start settings.

The unprecedented development of the Internet of Things (IoT), artificial intelligence (AI), and Big Data has stimulated a boom of social networks such as Twitter, WeChat, Facebook, etc., generating a huge amount of social data that are worth further analysis. Social computing has an important focus on mining the deep relationships between social organizations, networks, and media. The increasing volumes and complexities make big social data mining more and more difficult. Hybrid Human–Artificial Intelligence (H-AI) is an approach combining both human intelligence and AI, so as to handle demanding problems in a harmonious way. By adopting H-AI in social computing, it would provide more possibilities for social data analysis, relationship discovery, outlier detection, and prediction, and is proving to be an emerging and promising direction for AI and big data research.

The papers in this special section focus on hybrid human-artificial intelligene (AI) for multimedia computing. Multimedia computing has experienced a tremendous growth in the last decades, with applications ranging from multimedia information retrieval and analysis to multimedia compression and communication. However, the increasing volume and complexity of multimedia data driven by the large-scale spread of various new devices and sensors is posing a serious challenge to traditional multimedia computing algorithms. Artificial intelligence (AI), in particular deep learning techniques, has improved the performance of multimedia computing algorithms for many tasks, including computer vision and natural language processing. But unlike humans, AI is poor at solving tasks across multiple domains or in dealing with an uncontrolled dynamic environment. Hybrid Human-Artificial Intelligence (HH-AI) is an emerging field that aims at combining the benefits of human intelligence, such as semantic association, inference, and generalization with the computing power of AI.

Edge computing is a bridge for realizing the convergence between physical space and cyber space in the Internet of Things (IoT) paradigm. Large numbers of physical objects produce a huge amount of data that needs to be efficiently processed in the edge side. This situation urgently requires novel ideas and framework in the design and management of edge computing to improve and enhance its performance. In this article, we propose an approach and principle of software-defined edge computing (SDEC) from the perspective of cyber-physical mapping, where the ultimate goal is to achieve a highly automatic and intelligent edge computing system. The SDEC can also help realize flexible management and intelligent collaboration among various edge hardware resources and services by way of software. To this end, we design an SDEC-based open IoT system architecture which decouples upper level IoT applications from the underlying physical edge resources and builds dynamically reconfigurable smart edge services. The software-definition mechanism of the SDEC platform is proposed to introduce the detailed processes that the underlying physical devices are defined in the form of software. We also describe an illustrative application case about smart factory to present the practical effectiveness of the proposed scheme. Finally, we outline several challenges which are worthy of in-depth study and research. The SDEC paradigm can share, reuse, recombine, and reconfigure edge resources and services so that the overall service capability of the edge side can be improved.

Internet-of-Things (IoT)-enabled sensors and services have increased exponentially recently. Transmitting the massive generated data and control messages becomes an overhead on the communication system infrastructure. Many architectures and paradigms have been introduced to address the connection exploding, such as cloudlets, fog, and mist computing. Besides, software-related solutions, such as mobile Internet technologies and software-defined network also take part in mitigating the communication overhead. All of those new techniques have the same purposes summarized in achieving low latency, high throughput, and less storage and computing at the cloud level in addition to other objectives discussed through this survey. We list the proposed solutions, show their advantages and schemes, highlight some of the newest IoT-enabled applications, and show how they benefit from applying the new paradigms.

With the development of the Internet of Things (IoT), the physical space we are living in is experiencing unprecedented digitalization and virtualization. It is an overwhelming trend to achieve the convergence between the physical space and cyberspace, where the fundamental problem is to realize the accurate mapping between the two spaces. Therefore, identity modeling and identity addressing, which serve as the main bridge between the physical space and cyberspace, are regarded as important research areas. This article summarizes the related works regarding identity modeling and identity addressing in IoT, and makes a general comparison and analysis based on their respective features. Following that a flexible and low coupling framework, with strong independence between different modules is proposed, where both identity modeling and identity addressing are integrated. Meanwhile, we discuss and analyze the future development and challenges of identity modeling and addressing. It is proved that identity modeling and identity addressing are extremely significant topics in the era of IoT.

Smart vehicles can cooperate in teams to perform crowdsensing tasks in smart cities. A critical challenge in this regard is to build a secure model for nondeterministic vehicle teams to achieve maximum social welfare. Although several crowdsensing models have been proposed, none of them has focused on real-time vehicle teamwork. In this article, to the best of our knowledge, we propose the first secure model, called blockchain-based nondeterministic teamwork cooperation (BNTC), for nondeterministic teamwork cooperation in a vehicular crowdsensing system. We model the system as a multiconditional NP-complete problem by explicitly considering the dynamic features of task issuers and workers. To solve the problem, we propose the winning teams selected (WTS) algorithm based on a reverse auction and utilize a knapsack-based method to solve the models. We consider the credit of teams for determining the payment. Thus, we propose a credit-based team payment (CTP) algorithm for BNTC to maximize the welfare of the system. We also propose a general blockchain-based framework to address trust issues and security challenges to make the method suitable for use in practical applications. Based on theoretical analyses and extensive simulations, we demonstrate that the proposed model performs better than the baselines and can achieve the maximum social welfare. Implementation with Ethereum suggests our model can operate within a reasonable cost.

With the rapid growth in connected vehicles and related innovative applications, it is getting keen interest among the researchers. Vehicular ad-hoc networks (VANETs) comprise of interconnected vehicles with sensing capabilities to exchange traffic, weather and emergency information. Intelligent transportation system (ITS) supports better coordination among vehicles and in providing reliable services. In VANET, topology is dynamic due to the high mobility of vehicles, therefore, the existing topology-based schemes are not suitable. In this paper, we have explored position based routing (PBR) protocols for VANETs by presenting a taxonomy. The existing survey papers have focused on PBR schemes but we have further focused on considering PBR for the city environment along with connectivity aware routing schemes. Moreover, linear programming, genetic algorithms, and regression-based schemes are also included. To further evaluate the strengths and weaknesses of PBR schemes, a categorical evaluation of different architectures, path strategies, and carry-forward strategies are presented. Currently, no architecture is presented for FoG-oriented VANET using parked vehicles as guards for anchor points or junctions. To fill this aspiring demand, we have presented a FoG-oriented VANET architecture that can support the PBR by utilizing road junctions for path selection. It also involves the vehicles in the parking area for packet transmission. Further, we have proposed to use parked vehicles near junction as an option for selecting guarding vehicle along with ITS as well. It reduces the probability of extensive carry forward-based communication due to the absence of guarding nodes. The records at parked vehicles can be upgraded at neighboring parked vehicles as well during the beaconing or exchange of data messages. The architecture supports better packet delivery ratios, end-to-end delay, transmission time, and communication cost. Moreover, opportunities and challenges of the proposed architecture are also explored to attract researchers toward this area of research.

Nature-inspired approaches have been widely used for different purposes over the last two decades and are still extensively researched, especially for complex real-world problems. Biological systems, or nature in general, serve as the source of the intelligence of nature-inspired approaches. The efficiency of nature-inspired approaches is due to their significant ability to imitate the best features of nature that evolved by natural selection over millions of years. These approaches have been successfully used for Internet of Things (IoT) and big data handling and relevant examples of these topics may be artificial neural networks (ANNs) and deep learning applications. On this basis, the main theme of this special issue (SI) addresses recent advances in the use of the nature-inspired approaches for IoT and big data problems.

The Internet of Things (IoT) connects more and more devices and supports an ever-growing diversity of applications. The heterogeneity of the cross-industry and cross-platform device resources is one of the main challenges to realize the unified management and information sharing, ultimately the large-scale uptake of the IoT. Inspired by software-defined networking, we propose the concept of software-defined device (SDD) and further elaborate its definition and operational mechanism from the perspective of cyber-physical mapping. Based on the device-as-a-software concept, we develop an open IoT system architecture which decouples upper-level applications from the underlying physical devices (Physical-D) through the SDD mechanism. A logically centralized controller is designed to conveniently manage Physical-D and flexibly provide the device discovery service and the device control interfaces for various application requests. We also describe an application use scenario which illustrates that the SDD-based system architecture can implement the unified management, sharing, reusing, recombining, and modular customization of device resources in multiple applications, and the ubiquitous IoT applications can be interconnected and intercommunicated on the shared Physical-D.

Along with an integration of wearable devices, wireless communications and big data in the smart health, biomedical data is collected referring to multiple associated patients during interactions. Due to communication channel openness and data sensibility, privacy preservation become increasingly noteworthy in the edge and cloud hybrid computing-based healthcare applications. In this paper, a cooperative privacy preservation scheme is designed for wearable devices with identity authentication and data access control considerations in the space-aware and time-aware contexts. In the space-aware edge computing mode, secret sharing and MinHash-based authentication is designed to enhance privacy preservation along with similarity computing without revealing sensitive data. In the time-aware cloud computing mode, ciphertext policy attribute-based encryption is applied for fine-grained access control, and bloom filter is used to achieve efficient data structure without privacy exposure. The GNY logic-based security formal analysis is performed to prove theoretical correctness, and the proposed scheme achieves cooperative privacy preservation for wearable devices in smart health with communication overhead and computation cost.

The ubiquitous connections of physical objects in Internet of Things (IoT) is undoubtedly challenging the consistency mapping between physical space and cyberspace. As the key techniques for establishing the correspondences between physical objects and cyber entities, the objects identification and resolution (IR) attracted extensive attention. Conventional IR schemes in IoT generally rely on a single-mode of identification (ID) or nonidentification (nID) IR, which has big limitations in adaptability and reliability. In this case, a combined IR scheme based on ID and nID is proposed in this paper. In our proposed scheme, the ID code and nID features complement each other so as to break the restrictions of application domain and to provide better and humanized services. In order to improve the efficiency of the scheme, edge computing is introduced to reduce network transmission load and the computing burden of cloud, especially when the input data requires large amount of computing and storage resources. Furthermore, we design and implement a prototype of electronic product code (EPC) (ID) and fingerprint (nID) combined IR. Simulation results show that the edge computing-based ID and nID combined IR scheme has advantages in resolution accuracy and efficiency.

The increasing growth of big data is certainly challenging ubiquitous sensing in the Internet of Things (IoT) paradigm because of the limitations in sensing resources. Processing huge amounts of sensed data requires an enormous and unnecessary pool of resources. Both reasons strongly support the idea of adopting a selective sensing solution to handle the mapping between physical space and cyberspace and to lighten the load of data processing in IoT applications. Inspired by the ability of creatures that fleetly select the information of interest from a noisy environment and process them with limited attention resources, in this paper the biological attention mechanism is introduced to design a novel selective sensing framework called attention mechanism inspired selective sensing (AMiSS). In order to illustrate the functionality of the AMiSS platform, a use case scenario in reference to the security system of a modern transport station is presented. Further, we implement a proof-of-concept simulation using video-based object tracking to verify the feasibility and effectiveness of the AMiSS framework in IoT applications. Although it is just a narrow demonstration, the simulation still shows the effect of the AMiSS platform in reducing the amount of data processed by the higher layers.

With the alarming increase in breast cancer cases, researchers have considered it a challenging research problem to propose dependable solutions. It is quite essential for early detection, prevention, and control against breast cancer. Existing schemes still does not utilize recent information technology support, and hence preventive measures and factors are also not appropriate. This paper adopts cloud computing to present association rule-based breast cancer prevention and control system. We have categorized our work into two phases. In phase 1 titled prevention and control, we propose item association rule (IAR) algorithm and N-IAR algorithm for n-item associations. It can be used to discover risk factors for breast cancer. Our algorithm discovers more risk factors than the traditional logistics method. Some factors which can be modified are used for breast cancer prevention and control. In addition, existing risk assessment models are not applicable to Chinese women as well. In phase 2, we manage this by introducing a new model based on machine learning. It utilizes real data from Chinese women and more risk factors for breast cancer. Moreover, we have identified and evaluated a number of new common risk factors. Results prove that our system achieves higher assessment values as compared to preliminaries.

Friend recommendation system (FRS) is an essential part of any social network system. With the popularity of social network sites, many FRSs have been proposed in the past few years. However, most of them are homophily based systems, homophily is the propensity to associate and bond with similar others. In other words, these systems will recommend people that you share common features with them as friends. Homophily based FRS is accurate when the common feature is a physical or social feature, such as age, race, location, job, or lifestyle. However, it is not the case with personality types. Having a given personality type does not necessarily mean that you are compatible with people that have the same personality type. Therefore, in this paper, we present and evaluate an FRS based on the big-five personality traits model and hybrid filtering, in which the friend recommended process is based on personality traits and users' harmony rating. To validate the proposed system's accuracy, a personality-based social network site that uses the proposed FRS named PersoNet is implemented. Users' rating results show that PersoNet performs better than collaborative filtering (CF)-based FRS in terms of precision and recall.

An industrial mobile network is crucial for industrial production in the Internet of Things. It guarantees the normal function of machines and the normalization of industrial production. However, this characteristic can be utilized by spammers to attack others and influence industrial production. Users who only share spams, such as links to viruses and advertisements, are called spammers. With the growth of mobile network membership, spammers have organized into groups for the purpose of benefit maximization, which has caused confusion and heavy losses to industrial production. It is difficult to distinguish spammers from normal users owing to the characteristics of multidimensional data. To address this problem, this paper proposes a spammer identification scheme based on Gaussian mixture model (SIGMM) that utilizes machine learning for industrial mobile networks. It provides intelligent identification of spammers without relying on flexible and unreliable relationships. SIGMM combines the presentation of data, where each user node is classified into one class in the construction process of the model. We validate the SIGMM by comparing it with the reality mining algorithm and hybrid fuzzy c-means (FCM) clustering algorithm using a mobile network dataset from a cloud server. Simulation results show that SIGMM outperforms these previous schemes in terms of recall, precision, and time complexity.

In order to meet low latency, service sensitive and location awareness requirements of digital manufacturing, fog computing is introduced to be an intermediate layer between industrial Internet of Things and cloud. The distributed, dynamic characteristics and the collaboration requirement make it face many new security and privacy issues that cannot be solved by the traditional public key or symmetric cryptosystem. For addressing them, a registered but anonymous attribute credential is designed to manage the network entities. Based on it, an attribute credential based public key cryptography (AC-PKC) is constructed to provide flexible key management by taking the advantage of the certificate-less public key cryptography and the combination property of the elliptic curve cryptography. Encryption, authentication, and access control with privacy preserving can be realized on the basic operations of AC-PKC, which can meet various security requirements of fog computing based digital manufacturing. The performance analyses and comparison with the existing public key schemes and attribute based encryption solutions show that the proposed scheme can work flexibly at a relatively low cost.

Cyberspace is the digital world created based on traditional physical, social, and thinking spaces (PST) but in turn makes a great difference on PST. The cyberization and the emergence of cyber-enabled spaces can be viewed as the bridge between cyberspace and PST, which reshaped the current definition of cyberspace and contributed to a novel concept general cyberspace (GC). Generally, GC is a unified description of conventional cyberspace (also shortly cyberspace in this paper) and cyber-enabled PST. It essentially emerges from cyberspace based on ubiquitous connections between things and the deep convergence of spaces. This paper proposes the definition of GC and investigates it from its three main aspects: 1) existence; 2) interactions; and 3) applications/services, respectively, in terms of philosophy, science, and technology outlook.

Face identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In the current Internet of Things (IoT) and big data situation, the increase of applications based on face identification and resolution raises the demands of computation, communication, and storage capabilities. Therefore, we have proposed the fog computing-based face identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing-based framework. In this paper, we propose a security and privacy preservation scheme to solve the above issues. We give an outline of the fog computing-based face identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of face identification and face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the confidentiality, integrity, and availability (CIA) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.

Cyberspace is a new basic space after the three traditional basic spaces-physical, social, and thinking spaces (PST spaces). It is a trend that entities (objects) and PST spaces they are living in to be cyberized. On the one hand, the rapidly developing of cyberspace has the increasingly significant influences to PST spaces. On the other hand, the cyberization of objects in PST spaces have been continuously deepening and strengthening. Cyberization leads to the convergence of the four basic spaces, which also called cyberspace and cyber-enabled physical-social-thinking spaces (CPST spaces). In recent years, the philosophy research on CPST spaces and objects (short for cyber philosophy) has been developing rapidly while some researchers try to figure cyber science and its fundamental issues. Up to now, the bridge, fundament logic from cyber philosophy to cyber science, has not yet formed. This paper proposes a new concept of “cyberlogic” for establishing a bridge from cyber philosophy to cyber science. The etymology, concept, contents, and methods of cyberlogic are presented, and the cyberlogic for the CPST spaces is shown. Moreover, main issues and methodologies for cyberlogic are discussed.

For large-scale Internet of Things (IoT), which located in the hostile environment where exists malicious nodes (MNs), the security of time synchronization is a critical and challenging issue. The malicious sensor nodes could decrease the accuracy of the whole network by broadcasting fake timestamp messages. In this paper, we propose a secure time synchronization model for large-scale IoT. In this model, a node utilizes its father node and grandfather node to detect the MN. By employing the model, a spanning tree topology which synchronizes to the reference nodes can be constructed hop by hop. Then a secure time synchronization protocol is developed to against fake timestamps, which adopts the secure model. We use NS2 as the simulation tool to evaluate our protocol, and compare the impact of fake timestamps in various circumstances with the pervious protocols TPSN and STETS. The experiment results show that our protocol is effective to prevent attacks from MNs.

The identification and resolution technology are the prerequisite for realizing identity consistency of physical-cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in identification applications. With the increase of face identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based face identification and resolution scheme. Face identifier is first generated by the identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individual's identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of face identification and resolution.

Space and time are crucial characteristics of the physical objects. Considering only the spatial dimension will lead to an ambiguity when objects are mapped from the physical world to cyber world, therefore the temporal and logical dimensions also should be addressed in the mapping process. In this context we propose STLF, a spatial-temporal-logical framework that observe the relations that holds among objects in the physical space to properly map them to the cyberspace, and furthermore, we discuss the methods to map the object's changing properties, we conclude by advocating Perdurance-based mapping.

Wireless sensor networks (WSNs) are integrated as a pillar of collaborative Internet of Things (IoT) technologies for the creation of pervasive smart environments. Generally, IoT end nodes (or WSN sensors) can be mobile or static. In this kind of hybrid WSNs, mobile sinks move to predetermined sink locations to gather data sensed by static sensors. Scheduling mobile sinks energy-efficiently while prolonging the network lifetime is a challenge. To remedy this issue, we propose a three-phase energy-balanced heuristic. Specifically, the network region is first divided into grid cells with the same geographical size. These grid cells are assigned to clusters through an algorithm inspired by the${{k}}$-dimensional tree algorithm, such that the energy consumption of each cluster is similar when gathering data. These clusters are adjusted by (de)allocating grid cells contained in these clusters, while considering the energy consumption of sink movement. Consequently, the energy to be consumed in each cluster is approximately balanced considering the energy consumption of both data gathering and sink movement. Experimental evaluation shows that this technique can generate an optimal grid cell division within a limited time of iterations and prolong the network lifetime.

Nowadays, green energy management is a fundamental perspective for supporting cyber-physical interactions and managing energy resources, and smart grid is emerging as the next generation energy management paradigm. Toward the green energy management and smart grid, there are several open issues to be explored. This special issue is to provide a platform for the last results in the related topics.

Cloud computing is an emerging data interactive paradigm to realize users’ data remotely stored in an online cloud server. Cloud services provide great conveniences for the users to enjoy the on-demand cloud applications without considering the local infrastructure limitations. During the data accessing, different users may be in a collaborative relationship, and thus data sharing becomes significant to achieve productive benefits. The existing security solutions mainly focus on the authentication to realize that a user’s privative data cannot be illegally accessed, but neglect a subtle privacy issue during a user challenging the cloud server to request other users for data sharing. The challenged access request itself may reveal the user’s privacy no matter whether or not it can obtain the data access permissions. In this paper, we propose a shared authority based privacy-preserving authentication protocol (SAPA) to address above privacy issue for cloud storage. In the SAPA, 1) shared access authority is achieved by anonymous access request matching mechanism with security and privacy considerations (e.g., authentication, data anonymity, user privacy, and forward security); 2) attribute based access control is adopted to realize that the user can only access its own data fields; 3) proxy re-encryption is applied to provide data sharing among the multiple users. Meanwhile, universal composability (UC) model is established to prove that the SAPA theoretically has the design correctness. It indicates that the proposed protocol is attractive for multi-user collaborative cloud applications.

The Internet of Things (IoT) is becoming an attractive system paradigm to realize interconnections through the physical, cyber, and social spaces. During the interactions among the ubiquitous things, security issues become noteworthy, and it is significant to establish enhanced solutions for security protection. In this work, we focus on an existing U2IoT architecture (i.e., unit IoT and ubiquitous IoT), to design an aggregated-proof based hierarchical authentication scheme (APHA) for the layered networks. Concretely, 1) the aggregated-proofs are established for multiple targets to achieve backward and forward anonymous data transmission; 2) the directed path descriptors, homomorphism functions, and Chebyshev chaotic maps are jointly applied for mutual authentication; 3) different access authorities are assigned to achieve hierarchical access control. Meanwhile, the BAN logic formal analysis is performed to prove that the proposed APHA has no obvious security defects, and it is potentially available for the U2IoT architecture and other IoT applications.

Vehicle-to-grid (V2G), involving both charging and discharging of battery vehicles (BVs), enhances the smart grid substantially to alleviate peaks in power consumption. In a V2G scenario, the communications between BVs and power grid may confront severe cyber security vulnerabilities. Traditionally, authentication mechanisms are solely designed for the BVs when they charge electricity as energy customers. In this paper, we first show that, when a BV interacts with the power grid, it may act in one of three roles: 1) energy demand (i.e., a customer); 2) energy storage; and 3) energy supply (i.e., a generator). In each role, we further demonstrate that the BV has dissimilar security and privacy concerns. Hence, the traditional approach that only considers BVs as energy customers is not universally applicable for the interactions in the smart grid. To address this new security challenge, we propose a role-dependent privacy preservation scheme (ROPS) to achieve secure interactions between a BV and power grid. In the ROPS, a set of interlinked subprotocols is proposed to incorporate different privacy considerations when a BV acts as a customer, storage, or a generator. We also outline both centralized and distributed discharging operations when a BV feeds energy back into the grid. Finally, security analysis is performed to indicate that the proposed ROPS owns required security and privacy properties and can be a highly potential security solution for V2G networks in the smart grid. The identified security challenge as well as the proposed ROPS scheme indicates that role-awareness is crucial for secure V2G networks.

Along with radio frequency identification (RFID) becoming ubiquitous, security issues have attracted extensive attentions. Most studies focus on the single-reader and single-tag case to provide security protection, which leads to certain limitations for diverse applications. This paper proposes a grouping-proofs-based authentication protocol (GUPA) to address the security issue for multiple readers and tags simultaneous identification in distributed RFID systems. In GUPA, distributed authentication mode with independent subgrouping proofs is adopted to enhance hierarchical protection; an asymmetric denial scheme is applied to grant fault-tolerance capabilities against an illegal reader or tag; and a sequence-based odd-even alternation group subscript is presented to define a function for secret updating. Meanwhile, GUPA is analyzed to be robust enough to resist major attacks such as replay, forgery, tracking, and denial of proof. Furthermore, performance analysis shows that compared with the known grouping-proof or yoking-proof-based protocols, GUPA has lower communication overhead and computation load. It indicates that GUPA realizing both secure and simultaneous identification is efficient for resource-constrained distributed RFID systems.

SUMMARY The open radio frequency identification (RFID) air interface may suffer from severe threats that make security problem become a critical issue for RFID systems and applications. This paper proposes a dual cryptography authentication protocol (DCAP) for RFID systems. DCAP partitions randomly the tag identifier into two partial identifiers that are used in the forward link and in the backward link, respectively. The protocol applies hash function and shared‐key encryption algorithm to safeguard both forward and backward links and provides a three‐round authentication mode on each tag and reader in a session. Then, authentication is carried out by the primary, secondary, and final verifications. For a formal analysis, a graphical method Colored Petri Nets is applied to model and analyze the correctness of DCAP. We prove that the protocol owns tag anonymity and forward security and has the capability to resist major attacks such as replay, reader forgery, and tag forgery. Finally, the performance in terms of storage, communication overhead, and computation load is evaluated to demonstrate that the protocol has modest complexity and high efficiency. Copyright © 2011 John Wiley & Sons, Ltd.

Radio frequency identification (RFID)-based sensor systems are emerging as a new generation of wireless sensor networks by inherently integrating identification, sensing, communications and computation capabilities. Security and privacy are critical issues in dealing with a large amount of sensed data. In the study, the authors propose a distributed key array authentication protocol (KAAP) that provides classified security protection. KAAP is synthetically analysed in three aspects: logic, security and performance. The logic analysis includes messages formalisation, initial assumptions and anticipant goals based on GNY Logic formal method to verify the design correctness of the protocol. The security analysis with respect to confidentiality, integrity, authentication, anonymity and availability is performed via the simulated attacks, which involves supposing the attacker's identity, simulating the attacker's authentication process and creating compromised conditions. Such analysis ensures that the protocol has an ability to resist both external attacks (spoofing, replay, tracking and Denial of Service) and internal forgery attacks. Additionally, the performance is evaluated and compared with other related protocols to show that KAAP can improve the reliability and efficiency of sensor systems with insignificantly increased complexity. The result indicates that the protocol is reliable and scalable in advanced RFID-based sensor systems.