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The Metaverse depicts a parallel digitalized world where virtuality and reality are fused. It has economic and social systems like those in the real world and provides intelligent services and applications. In this paper, we introduce the Metaverse from a new technology perspective, including its essence, corresponding technical framework, and potential technical challenges. Specifically, we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time, space, and contents of the Metaverse by citing Maslow's Hierarchy of Needs. Subsequently, we conclude four pillars of the Metaverse, named ubiquitous connections, space convergence, virtuality and reality interaction, and human-centered communication, and establish a corresponding technical framework. Additionally, we envision open issues and challenges of the Metaverse in the technical aspect. The work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.© 2022 Published by Elsevier Ltd.

Abstract As the core of ontology integration, the task of ontology mapping is to find the semantic relationship between ontologies. Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregarding semantic nuances and necessitating substantial manual intervention. Therefore, this paper introduces an ontology mapping method. Based on the traditional ontology mapping method, the process employs a deep learning model to mine the semantic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the similarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similarity matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. However, since the number of mapped pairs is much larger than the number of non-mapped pairs, the number of positive samples in the data set is much smaller than the number of negative samples. Therefore, based on the traditional SVM, the paper adopts cost-sensitive strategy to deal with the class imbalance problem. In comparative evaluations against contemporary ontology mapping techniques, our method demonstrates a noteworthy 5.0% enhancement in recall and a 3.0% improvement in F1 score when tested on both public benchmark datasets and domain-specific datasets.

Abstract The concept of the Human Digital Twin (HDT) has recently emerged as a new research area within the domain of digital twin technology. HDT refers to the replica of a physical-world human in the digital world. Currently, research on HDT is still in its early stages, with a lack of comprehensive and in-depth analysis from the perspectives of universal frameworks, core technologies, and applications. Therefore, this paper conducts an extensive literature review on HDT research, analyzing the underlying technologies and establishing typical frameworks in which the core HDT functions or components are organized. Based on the findings from the aforementioned work, the paper proposes a generic architecture for the HDT system and describes the core function blocks and corresponding technologies. Subsequently, the paper presents the state of the art of HDT technologies and their applications in the healthcare, industry, and daily life domains. Finally, the paper discusses various issues related to the development of HDT and points out the trends and challenges of future HDT research and development.

As the Industrial Internet of Things rapidly evolves, enterprises are increasingly using a wide variety of web containers. However, this growing usage also magnifies the complexities associated with managing these assets, leading to a rise in network security risks and vulnerabilities. This trend not only highlights a shift in industrial practices but also underscores the urgent need for proactive measures to strengthen network security. In response to this complex challenge, this paper presents a cutting-edge approach for detecting and identifying web containers through proactive probing. During the detection phase, we meticulously construct web container probes, laying the groundwork for their central role in the identification process. During this stage, the probe leverages a payload-based identification method for web containers, achieving an exceptional level of accuracy by implementing advanced protocol decoding techniques. The integration of these innovative techniques not only enhances the precision of web container identification but also demonstrates our commitment to providing a comprehensive solution to the challenges posed by web container management. This pioneering methodology reflects the ongoing evolution of detection strategies and our dedication to pushing technological boundaries for an improved understanding of web containers. By seamlessly integrating these two advanced identification methods, we have successfully identified four different types of web containers. Importantly, we have made significant progress in precision and accurately differentiating between various versions of these web containers, with a total of ten versions correctly identified. This groundbreaking method is not only crucial for addressing the current complexities in web container asset management but also provides enterprises with a more effective means of network security defense. Through the use of this advanced proactive probing method, enterprises can better understand and manage their web container assets, reducing network security risks and ensuring system stability and security. Our contribution plays a pivotal role in the ongoing development of the Industrial Internet of Things and lays a strong foundation for future research and applications in network security. The innovative approach presented here serves as a catalyst for the positive advancement of the current Industrial Internet of Things landscape and lays a solid groundwork for future exploration and application in network security.

Effective user authentication is key to ensuring equipment security, data privacy, and personalized services in Internet of Things (IoT) systems. However, conventional mode-based authentication methods (e.g., passwords and smart cards) may be vulnerable to a broad range of attacks (e.g., eavesdropping and side-channel attacks). Hence, there have been attempts to design biometric-based authentication solutions, which rely on physiological and behavioral characteristics. Behavioral characteristics need continuous monitoring and specific environmental settings, which can be challenging to implement in practice. However, we can also leverage Artificial Intelligence (AI) in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication. Thus, we review the literature on the use of AI in physiological characteristics recognition published after 2015. We use the three-layer architecture of the IoT (i.e., sensing layer, feature layer, and algorithm layer) to guide the discussion of existing approaches and their limitations. We also identify a number of future research opportunities, which will hopefully guide the design of next generation solutions.

Abstract Prolonged and over-excessive interaction with cyberspace poses a threat to people’s health and leads to the occurrence of Cyber-Syndrome, which covers not only physiological but also psychological disorders. This paper aims to create a tree-shaped gold-standard corpus that annotates the Cyber-Syndrome, clinical manifestations, and acupoints that can alleviate their symptoms or signs, designating this corpus as CS-A. In the CS-A corpus, this paper defines six entities and relations subject to annotation. There are 448 texts to annotate in total manually. After three rounds of updating the annotation guidelines, the inter-annotator agreement (IAA) improved significantly, resulting in a higher IAA score of 86.05%. The purpose of constructing CS-A corpus is to increase the popularity of Cyber-Syndrome and draw attention to its subtle impact on people’s health. Meanwhile, annotated corpus promotes the development of natural language processing technology. Some model experiments can be implemented based on this corpus, such as optimizing and improving models for discontinuous entity recognition, nested entity recognition, etc. The CS-A corpus has been uploaded to figshare.

Internet of Vehicles (IoV) offers many services aiming to enhance the safety and comfort of drivers and passengers such as accident alarms, congestion avoidance, and multimedia, entertainment applications. Cooperatively sharing and retrieving large-scale files in IoV is a challenging task due extremely volatile nature of IoV. Therefore, it is paramount to develop a vehicle-to-vehicle (V2V) content delivery mechanism that can adapt to IoV communication requirements. One of the challenging tasks in this regard is to locate content pieces and gather information about peers. Previous proposed systems broadcast beacon messages to the whole network to locate certain content pieces, which consume the bandwidth and limit the network resources. To overcome these issues, in this paper, we propose BusCache, a traffic-aware content delivery system for IoV. BusCache uses buses as trackers for the content distribution on the overlay network, due to their availability and unique characteristics (predefined trajectories and time). Instead of flooding the entire network, a request to the bus is enough to determine the piece’s location. We first propose a clustering strategy to control the data followed between peers without relying on infrastructure, which reduces the end-to-end delay network between the receiving peer and the tracked node. Then we introduce a peer selection mechanism that selects the most appropriate peers that will deliver the desired content by taking into account their velocity, destination, and other traffic-related factors. Finally, we proposed a policy-based content selection algorithm that identifies and prioritizes the scarce content that is present in just a few vehicles. Simulation results show that BusCache can reduce content lookup time and content dissemination compared to state-of-the-art approaches.

IoT has recently witnessed a boom in AI deployment at the edge as a result of the newly developed small size Machine Learning (ML) models and integrated hardware accelerators. Although it brings huge benefits such as privacy-preserving and low-latency applications, it still suffers from typical resource limitations of edge devices. A new approach aims to deploy multiple inference models varying in size and accuracy onboard the edge device which could alleviate some of these limitations. This dynamic system can be leveraged to provide real-time energy efficient application by smartly allocating inference tasks to inference local models or offload to edge servers based on current constraints. In this work, we tackle the problem of efficiently allocating inference models for a given set of inference tasks between local inference models and edge server models in parallel under given time and energy constraints. This problem is considered strongly NP-hard and therefore we propose LITOSS, a 2-stage framework in which we use a lightweight Genetic Algorithm-based schemer for task scheduling along with a Reinforcement Learning (RL) agent for improving edge server selection. We perform experiments using a raspberry pi with a set of edge servers. Results show that our framework performed relatively faster compared to other meta-heuristic schemes such as LGSTO, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) while providing higher average accuracy. We also show that using an RL agent to select the best subset of available edge servers increased, or maintained in worst cases, the average accuracy while reducing the average scheduling times.

The Social Internet of Things (SIoT) is presented as a new paradigm of the Internet of Things that solves the problems of network navigability and provides enhanced service discovery and composition. It aims to socialize the IoT devices and allow them to interact just like humans by creating multiple social relationships. In SIoT scenarios, a device can offer multiple services, and different devices can offer the same services with different parameters and factors of interest, which leads to data sparsity and sheer volume of services. However, this sheer volume of available services makes it difficult for devices to navigate and select the ones that best fit their needs or preferences. On the other hand, the heterogeneous nature and dynamic connectivity of SIoT networks raise the cold start problem in service recommendations. Few works explored the integration of trust-aware approaches with latent feature mining in the SIoT recommendation systems. To address these challenges, we proposed a hybrid latent feature mining and trust-aware model to provide a tailored service recommendation in the SIoT environment. Experimental results conducted on a public dataset reveal the increase of service recommendation accuracy and highlight the proposed framework’s effectiveness in meeting recommendation needs within the scope of SIoT environment.

The brain–machine interface has been an integral component of the metaverse since the inception of the latter, in his classic science fiction novel “True Names,” Vernor Vinge, the American mathematician and computer science Professor, describes a virtual world that can be accessed and experienced via a brain–machine interface. Following the introduction of this idea, the science fiction novel “Avalanche” formally proposed the concept of a metaverse, where a virtual world constructed by humans using digital technology can be mapped onto and interact with the real world. Large companies such as Meta, Apple, Sony, Microsoft, and Samsung have launched new metaverse-related hardware and software products. Domestic giants such as Tencent, Alibaba, and Baidu have also integrated themselves into the metaverse, confirming its future development and commercial value. Goldman Sachs estimates that trillions of dollars will be invested in the development of the metaverse over the next few years. As the focus of metaverse research shifts toward content exchange and social interaction, the issue of addressing the current bottlenecks in audiovisual media interaction has become an urgent matter, and the brain–computer interface is one of its solutions. Brain–computer interfaces are becoming increasingly complex. As a physiological signal acquisition tool, it has demonstrated indispensable application potential in numerous fields of the metaverse. A non-invasive brain–computer interface possesses the advantages of being easy to obtain and having good performance and accuracy. It is the preferred method for detecting brain signals in brain–computer interfaces. The Electroencephalogram is a unique physiological signal conducive to reflecting people's psychological state. By reading and categorizing the relevant papers in the paper database, including Web of Science, CNKI, IEL, and ACM Digital Library; investigating the products and functional parameters of Neuralink, Synchron, OpenBCI, and Emotiv; studying three application scenarios, namely, the generative art in the metaverse art, the serious game of medicine and healthcare in the medical metaverse, and the application status of the brain–machine interface in virtual human expression synthesis in the social metaverse; and by investigating the existing commercial products and patents (MindWave Mobile, GVS, Galea), this paper discusses the challenges and potential problems that brain–computer interfaces may face with their widespread use by drawing parallels with the development process of network and neural security and bioethics. Furthermore, the possibility of in-depth and diverse applications of brain–computer interfaces in the future is explored, for instance, the use of sensory simulation technology to simulate olfactory sensation, gustatory sense, and tactile sensation, and the use of motor imagery to assist disabled people in participating in the metaverse.

Pedestrian trajectory prediction aims to use observed human historical trajectories and surrounding environmental information to predict the future position of the target pedestrian, which has important application value in reducing collision risks for autonomous vehicles in social interactions.However, traditional model-driven pedestrian trajectory prediction methods are difficult to predict pedestrian trajectories in complex and highly dynamic scenes.In contrast, datadriven pedestrian trajectory prediction methods rely on large-scale datasets and can better capture and model more complex pedestrian interaction relationships, thereby achieving more accurate pedestrian trajectory prediction results, and have become a research hotspot in fields such as autonomous driving, robot navigation and video surveillance.In order to macroscopically grasp the research status and key issues of pedestrian trajectory prediction methods, We started with the classification of pedestrian trajectory prediction technology and methods.First, the research progress of existing pedestrian trajectory prediction methods were elaborated and the current key issues and challenges were summarized.Second, according to the modeling differences of pedestrian trajectory prediction models, existing methods were divided into model-driven and data-driven pedestrian trajectory prediction methods, and the advantages, disadvantages and applicable scenarios of different methods were summarized.Then, the mainstream datasets used in pedestrian trajectory prediction tasks were summarized and the performance indicators of different algoriths were compared.Finally, the future development direction of pedestrian trajectory prediction was prospected.

Abstract Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard to understanding complex brain processing mechanisms. This study aims to comprehensively review and discuss the applications of Transformers in brain sciences. First, we present a brief introduction of the critical architecture of Transformers. Then, we overview and analyze their most relevant applications in brain sciences, including brain disease diagnosis, brain age prediction, brain anomaly detection, semantic segmentation, multi‐modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, and multi‐task collaboration. We organize the model details and open sources for reference and replication. In addition, we discuss the quantitative assessments, model complexity, and optimization of Transformers, which are topics of great concern in the field. Finally, we explore possible future challenges and opportunities, exploiting some concrete and recent cases to provoke discussion and innovation. We hope that this review will stimulate interest in further research on Transformers in the context of brain sciences.

Robots, as the creation of humans, became an irreplaceable component in human society. With the advancement of technologies, robots have become more and more intelligent and have been widely used in many fields, such as disease diagnosis, customer services, healthcare for the older people, and so on. As robots made our lives much more convenient than ever before, they also brought many potential risks and challenges in technology, security, and ethic. To better understand the development of robots, we proposed a concept of a robot’s living space and analyzed the role of robots in our society. In this paper, we focus on setting a theoretical framework of the robot’s living space to further understand the human-robot relationship. The research in this paper contains three central aspects. First, we interpret the concept of the robot’s living space and the functions of each space. Second, we analyze and summarize the relative technologies which support robots living well in each space. Finally, we provide advice and improvement measures based on a discussion of potential problems caused by the developments of robots. With the trend of robots humanization and human-robot society integration, we should seriously consider how to collaborate with intelligent robots to achieve hybrid intelligence. To build a harmonious human-robot integrated society, studying the robot’s living space and its relationship with humans is the pre-requisite and roadmap.

In the Internet of Things (IoT), security and privacy issues of physical objects are crucial to the related applications. In order to clarify the complicated security and privacy issues, the life cycle of a physical object is divided into three stages of pre-working, in-working, and post-working. On this basis, a physical object-based security architecture for the IoT is put forward. According to the security architecture, security and privacy requirements and related protecting technologies for physical objects in different working stages are analyzed in detail. Considering the development of IoT technologies, potential security and privacy challenges that IoT objects may face in the pervasive computing environment are summarized. At the same time, possible directions for dealing with these challenges are also pointed out.

Abstract Due to the large number of students in a typical classroom and crowded seating, most features of student posture are often obscured, making it difficult to balance the accuracy in identifying student postures with computational efficiency. To solve this issue, a novel classroom student posture recognition method is proposed. First, to recognize the poses of multiple students in the classroom, we use the you-only-look-once (YOLOv3) algorithm for object detection and retrain it to detect human objects that are hunching on a table, creating the pose estimation network. Next, to improve the accuracy of the pose estimation network, we use the squeeze-and-excitation network structure that is embedded in the residual structure of high-resolution networks (HRNet). Finally, with the improved HRNet algorithm’s outputs of key human body points, we design a pose classification algorithm based on a support vector machine, to classify human poses in the classroom. Experiments show that the improved HRNet multi-person pose estimation algorithm yields the best mean average precision performance of 73.76% on the common objects in context (COCO) validation dataset. We further test the proposed algorithm on a customer dataset collected in a classroom and achieved a high recognition rate of 90.1% and good robustness.

Over the past few years, Radio Frequency IDentification (RFID) has stridden significantly due to its adoption in many business and daily life activities. RFID empowers end-to-end and item-by-item visibility, letting the customers or decision-makers effectively trail the personnel and resources for improved management. Significant research and development in this area have transformed the inexpensive, miniaturized technology of RFIDs without chip inside. Chipless Radio Frequency IDentification (CRFID) is a unique wireless technology based on radar principle to sense, identify, and track the objects. CRFIDs are simplified and miniaturized tags of traditional RFID, which can widely be used in small and cheap objects/applications in our daily life. This paper reviews the definition, principle, and most recent development in CRFIDs. Based on the reported researches, the ElectroMagnetic Spectrum (EMS) method is typical, which has been concerned by many RFID researchers. Therefore, this paper reviews different types of EMS CRFID tag and classifies these tags based on structure design emphasizing their significant geometries in comparison with performance parameters. Antenna and coding methods of the EMS CRFID are also elaborated, contrasting the tag capacity and coding performance. Finally, we briefly summarize the challenges and limitations in this emerging field and look forward to the prospects of future CRFID.

Internet of Things (IoTs) is a big world of connected objects, including the small and low-resources devices, like sensors, as well as the full-functional computing devices, such as servers and routers in the core network. With the emerging of new IoT-based applications, such as smart transportation, smart agriculture, healthcare, and others, there is a need for making great efforts to achieve a balance in using the IoT resources, including Computing, Communication, and Caching. This paper provides an overview of the convergence of Computing, Communication, and Caching (CCC) by covering the IoT technology trends. At first, we give a snapshot of technology trends in communication, computing, and caching. As well, we describe the convergence in sensors, devices, and gateways. Addressing the aspect of convergence, we discuss the relationship between CCC technologies in collecting, indexing, processing, and storing data in IoT. Also, we introduce the three dimensions of the IoTs based on CCC. We explore different existing technologies that help to solve bottlenecks caused by a large number of physical devices in IoT. Finally, we propose future research directions and open problems in the convergence of communication, computing, and cashing with sensing and actuating devices.

The data explosion caused by the rapid and widespread use of IoT devices is placing tremendous pressure on current communication, computing and storage resources. In an ambient ubiquitous computing environment, taking advantage of the context of the application scenario can significantly improve the system performance of IoT networks. Therefore, in this paper, we propose CONTESS, a context-aware edge computing framework with selective sensing that leverages the context information of the sensed environment to improve its applicability to smart IoT systems. CONTESS is composed of two parts: context management, where context acquisition, modeling and reasoning happens; and selective sensing, where data selection happens. We demonstrate the capabilities of CONTESS in the scenario of a parking management system for a smart city environment. We implement CONTESS using linked data and semantic web technologies. We start by designing an OWL-based ontology and then simulating the proposed scenario using the OMNET++ network simulator along with the Veins framework and SUMO traffic simulator. The results show an improvement compared to traditional sensing methods in both communication overhead and the application results.

The network of objects/things embedded with electronics, software, sensors, and network connectivity, Internet of Things (IoT), creates many exciting applications (e.g., smart grids, smart homes, and smart cities) by enabling objects/things to collect and exchange data so that they can be sensed and controlled. To fulfill IoT, one essential step is to connect various objects/things (e.g., mobile phones, cars, and buildings) so that they can “talk” to each other (i.e., collect and exchange data). However, substantial case studies show that simply connecting them without further collaboration among the objects/things when “talking” to each other leads to unnecessary energy consumption, uncertain security, unstable performance, etc., for IoT. Therefore, collaboration for IoT is very important. Specifically, there are a lot of critical issues to consider in terms of how to achieve robust collaboration among the objects/things for IoT. For instance, how to conduct collaboration among the objects/things so that more energy-efficient communication can be achieved for IoT? How to conduct collaboration among the objects/things so that computing with higher performance can be achieved for IoT? How to improve the security of IoT with collaboration among the objects/things? How to enhance the Quality of Service of IoT with collaboration among the objects/things? How to minimize the overhead costs when objects/things are collaborating in IoT?

Dementia is a severe disease threatening ageing societies, which not only causes great harm to patients both physically and psychologically but also places a heavy burden on patients' families. Medications have been used for the treatment of dementia but with little success. However, serious games, as a new form of dementia therapy, stand out from various therapeutic methods and pave the way for dementia treatment. In the field of serious games for dementia care (SGDC) in ageing societies, there exists abundant research related to this topic. While, a detailed review of the development route and a category framework for characteristics of dementia are still needed. Besides, due to the large number of games, it is difficult to select out effective ones. Yet, there is no unified and comprehensive assessment methods for SGDC. So a reliable assessment model is worth studying. In this paper, we review these existing research work on SGDC from two perspectives: (1) the development of SGDC; (2) the different symptoms in different dementia stages. We also propose a comprehensive and professional assessment model of the therapeutic effectiveness of SGDC to compensate for the simplicity of existing assessment methods. Finally, a discussion related to SGDC is presented.

Abstract With the rapid development and large-scale uptake of the Internet of Things, smart home is evolving from a vision towards a realistically viable solution for assisted living. Activity recognition is one of the fundamental tasks in order to provide accurate and timely assistance and service. As daily living scenarios are full of similar activities, missing data, and noise, inferring complex activities using knowledge-driven reasoning algorithms suffers from several drawbacks, e.g., real-time raw sensor data segmentation, poor generalization, higher computational complexity, and scalability. To address these problems, this paper proposes a hybrid approach to complex daily activity recognition by merging the first-order logic and probability graphic modeling. Specifically, we develop a novel “Markov logic network” combining data-driven multi-feature and simplified rule-based modeling and inference, thus enabling and supporting the applicability and robustness of daily activity recognition. To evaluate the approach and associated methods, we design a testing scenario with a number of similar activity groups, missing data, or disturbance test datasets in a multi-modeling sensor scene. Initial results show our approach outperforms the traditional approach with a better accuracy in the situations of similar activities with missing data and noise disturbance. Experiments are also conducted to compare the Gibbs sampling and MC-SAT sampling algorithms for Markov logic network, and the results show that the Gibbs is better in our experimental settings.

Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure. However, compromised cybersecurity of the smart grid not only affects the security of existing energy systems but also directly impacts national security. The increasing number of cyberattacks against the smart grid urgently necessitates more robust security protection technologies to maintain the security of the grid system and its operations. The purpose of this review paper is to provide a thorough understanding of the incumbent cyberattacks’ influence on the entire smart grid ecosystem. In this paper, we review the various threats in the smart grid, which have two core domains: the intrinsic vulnerability of the system and the external cyberattacks. Similarly, we analyze the vulnerabilities of all components of the smart grid (hardware, software, and data communication), data management, services and applications, running environment, and evolving and complex smart grids. A structured smart grid architecture and global smart grid cyberattacks with their impact from 2010 to July 2022 are presented. Then, we investigated the the thematic taxonomy of cyberattacks on smart grids to highlight the attack strategies, consequences, and related studies analyzed. In addition, potential cybersecurity solutions to smart grids are explained in the context of the implementation of blockchain and Artificial Intelligence (AI) techniques. Finally, technical future directions based on the analysis are provided against cyberattacks on SGs.

Access control is a critical aspect for improving the privacy and security of IoT systems. A consortium is a public or private association or a group of two or more institutes, businesses, and companies that collaborate to achieve common goals or form a resource pool to enable the sharing economy aspect. However, most access control methods are based on centralized solutions, which may lead to problems like data leakage and single-point failure. Blockchain technology has its intrinsic feature of distribution, which can be used to tackle the centralized problem of traditional access control schemes. Nevertheless, blockchain itself comes with certain limitations like the lack of scalability and poor performance. To bridge the gap of these problems, here we present a decentralized capability-based access control architecture designed for IoT consortium networks named IoT-CCAC. A blockchain-based database is utilized in our solution for better performance since it exhibits favorable features of both blockchain and conventional databases. The performance of IoT-CCAC is evaluated to demonstrate the superiority of our proposed architecture. IoT-CCAC is a secure, salable, effective solution that meets the enterprise and business’s needs and adaptable for different IoT interoperability scenarios.

Short texts play an important role in our daily communication. It has been applied in many fields. In this paper, we propose a novel short text similarity measurement algorithm-based long short-term memory (LSTM) encoder. It contains preprocessing, training, and evaluating stages. Our preprocessing algorithm can avoid gradient vanishing problems in the process of backward propagation faster after normalization. The training stage fully leverages the inception module to extract the features of different dimensions and improves the LSTM network to process the relationships of word sequences. The evaluating stage employs cosine distance to calculate the semantic similarity of two short texts. We do experiments on two short text dataset of different lengths and analyze the experiment result. The experiment result shows that our algorithm can fully employ semantic information and sequence information of short texts and have a higher accuracy and recall compared to other short text similarity measurement algorithms.

With the rapid growth in connected vehicles, FoG-assisted vehicular ad hoc network (VANET) is an emerging and novel field of research. For information sharing, a number of messages are exchanged in various applications, including traffic monitoring and area-specific live weather and social aspects monitoring. It is quite challenging where vehicles’ speed, direction, and density of neighbors on the move are not consistent. In this scenario, congestion avoidance is also quite challenging to avoid communication loss during busy hours or in emergency cases. This paper presents emergency message dissemination schemes that are based on congestion avoidance scenario in VANET and vehicular FoG computing. In the similar vein, FoG-assisted VANET architecture is explored that can efficiently manage the message congestion scenarios. We present a taxonomy of schemes that address message congestion avoidance. Next, we have included a discussion about comparison of congestion avoidance schemes to highlight the strengths and weaknesses. We have also identified that FoG servers help to reduce the accessibility delays and congestion as compared to directly approaching cloud for all requests in linkage with big data repositories. For the dependable applicability of FoG in VANET, we have identified a number of open research challenges.

The Internet of Things (IoT) aims at connecting things to the Internet in a peer-to-peer paradigm for data collecting and data sharing in our daily life. A blockchain is an immutable append-only ledger maintained by a peer-to-peer network, where the whole network needs to reach a consensus on the transactional data stored on the ledger. With the decentralization nature, the design of IoT and blockchain aligns with each other well. Blockchain has been integrated with the IoT to solve the existing IoT problems. Our research focuses on analyzing the solutions proposed in academia and the methodologies used to integrate blockchain with the IoT. Through conducting a systematic literature review (SLR) on peer-reviewed, published articles on blockchain-based solutions for IoT, we gather the knowledge on current technical approaches implemented to integrate blockchain into the IoT. Majority of the research in this space is either at a conceptual level or at a very early stage. However, we only found 35 published papers with the real implementation of blockchain in the IoT platforms. We elicit the challenges of the IoT that were being addressed, and the detailed design of the blockchain-based solutions from two perspectives, namely data management and thing management. The evaluation methods and metrics used by those works are also being recorded and analyzed. In addition to the analysis of the literature, we provide our insights on improving the existing solutions and research methodology based on our expertise and experience on the blockchain.

This paper presents a review of recent investigational studies on exciting Surface Plasmon Polaritons (SPPs) in MicroWave (MW) and TeraHertZ (THz) regimes by using subwavelength corrugated patterns on conductive or metal surfaces. This article also describes SPP Microstrip (MS) structures at microwave and terahertz frequencies, and compares their significance with that of conventional MS Transmission Lines (TL), in order to tackle the key challenges of high gain, bandwidth size, compactness, TL losses, and signal integrity in high-end electronic devices. Because they have subwavelength properties, surface plasmon polaritons are gaining attention for their improved performance and ability for miniaturization in high-speed dense circuits. They possess comparably minuscule wavelength compared to incident light (photons). Consequently, they can demonstrate stronger spatial confinement and higher local field intensity at optical frequencies. In addition to engineering spoof SPP waveguides, which are created by engraving grooves and slits on metal surfaces to allow operation on at low frequencies (microwave and terahertz), semiconductors with smaller permittivity values and thus lower free charge carrier concentration have been demonstrated as a potential candidate in plasmonic devices. If necessary, further tuning of semiconductor-based SPP structures is aided by controlling the charge carrier concentration through doping, or by external stimuli such as optical illumination or thermal excitation of charge carriers from valence to conduction bands. This article conclusively covers previously elucidated perspectives on manipulating SSPPs in the MW and THz ranges, and emphasizes how these could steer next-generation plasmonic devices. Keywords: Surface Plasmons, Subwavelength, Metamaterials, Plasmonics, Terahertz, Microwave

Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain–computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and recognize brain activities. With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic resonance imaging (fMRI). In this paper, we reviewed the brain activity decoding models based on machine learning and deep learning algorithms. Specifically, we focused on current brain activity decoding models with high attention: variational auto-encoder (VAE), generative confrontation network (GAN), and the graph convolutional network (GCN). Furthermore, brain neural-activity-decoding-enabled fMRI-based BCI applications in mental and psychological disease treatment are presented to illustrate the positive correlation between brain decoding and BCI. Finally, existing challenges and future research directions are addressed.

The particle swarm optimization (PSO) algorithm has been widely used in various optimization problems. Although PSO has been successful in many fields, solving optimization problems in big data applications often requires processing of massive amounts of data, which cannot be handled by traditional PSO on a single machine. There have been several parallel PSO based on Spark, however they are almost proposed for solving numerical optimization problems, and few for big data optimization problems. In this paper, we propose a new Spark-based parallel PSO algorithm to predict the co-authorship of academic papers, which we formulate as an optimization problem from massive academic data. Experimental results show that the proposed parallel PSO can achieve good prediction accuracy.

With the inclusion of mobile devices and ubiquitous connectivity of smart devices in Internet of Things, secure key management is mandatory to ensure privacy for information exchange. In this regard, the multiparty key establishment schemes achieve better security strength by taking shared parameters from neighboring member nodes to calculate the key. The similar multiparty mechanism can be adopted among other hierarchical nodes, including head node, server and gateway node. Moreover, session keys can also be set up in a similar manner. The main problem in multiparty password-based authentication schemes is the computation of extensively hard problem that limits it to three parties and N-party is quite more complex or infeasible. This paper presents a novel distributed multiparty keying scheme where chaotic maps are used to provide one-way hashing and Chebyshev polynomial are used for establishing a common multiparty key. In this paper, Phase-I covers keying among trusted server and group heads and Phase-II elaborates the key establishment among smart devices and their group heads. The scheme is verified through the formal specification and security analysis using Rubin Logic for inter-group key establishment scenario. We have validated the intra-group and inter-group key establishment by doing extensive simulations in NS 2.35. Moreover, a test bed is setup for group head to server level authentication and key establishment. Results prove the supremacy of our scheme as compared with preliminaries in terms of computation cost, communication cost, and resilience.

To find the relative positional relationship accurately and efficiently without pre-installed infrastructure in emergent scenarios like firefighting, we proposed a rotating-anchor based indoor geolocation system using time of arrival (TOA) technique in this paper. In this system, the positioning problem is regarded as a template-matching problem to estimate direction of the target. Based on preliminary results, further measurements that the tester held the locator and turned around continuously without any stop. The TOA ranging error for continuous rotation measurement was analyzed and proved to follow non-Guassian with impulsive characteristic in this paper. According to such error characteristics, a direction estimation algorithm of maximum correntropy criterion-based matching algorithm was proposed in this paper. The performance of algorithm was validated by comparing with the least mean square and least mean p-norm with empirical measurements.

The revolutionary change in information and communication technology has made the people’s lives much convenient more than ever before. But it has affected the human’s physical and mental health as well as community’s social connectivity. Cybersyndrome is the physical, social, and mental disorders that affect the human being due to the excessive interaction with the cyberspace. Many previous works have discussed the role that the technology plays in the development of specific disorders, such as Internet addiction disorder or gaming addiction disorder. However, none of these works have explored the effects of excessive interaction with the cyberspace on the people’s lives as a whole and its impact on the social connectivity of the community. Therefore, in this paper, we have presented the formation stages, classification, recovery, and prevention methods of cybersyndrome. We have explored the impact of cybersyndrome in physical, social, and thinking spaces and its future implications and complications.

To address the problem of a serious Internet of Things (IoT) talent shortage, the Chinese government is encouraging universities and institutions to establish a major in IoT Engineering to train high-level IoT talent. Since 2011, 700 Chinese universities and institutions have established IoT Engineering undergraduate programs. We surveyed the history, status, curriculum, and problems of IoT undergraduate degree programs in China. Based on data collected from faculties, students, and graduates, we found that the IoT Engineering curriculum was an unsystematic patchwork and was deficient in practical platforms. We then provide suggestions for further development and exploration of IoT education by presenting a Technical Knowledge Map of IoT Engineering. This survey work on Chinese IoT education can prompt Chinese educators to reflect on its achievements and problems, which will contribute to IoT talent cultivation worldwide.

Scheduling different types of data packets, such as high or low priority data packets at the sender node, is important for reducing energy and capacity consumptions and end-to-end delay. Current scheduling schemes of wireless sensor networks use preemptive and non-preemptive scheduling algorithms, which incur relatively long end-to-end transmission delay and high processing overhead. Besides, they do not consider the path capacity, which represents the capacity of the network for transferring as much as sensory data to the sink node(s). Consequently, sensory data are routed to the sink node(s), whatever they are more or less, important for supporting domain applications. To remedy this issue, we propose a method, which differentiates between high and low priority when routing sensory data to the sink node(s). Specifically, the priority of sensory data is determined through a novel capacity assignment mechanism. When the network capacity, which depends on the capacity of routing paths, may not be sufficient for supporting the sensory data routing requirement, sensory data with a relatively high priority should be routed to the sink node(s), while that with a relatively low priority may be decreased or prohibited. Experimental evaluation has been conducted, and the result shows that congestion and packets dropping are reduced, when sensory data can be differentiated in their priority and the network traffic is relatively heavy.

The Internet of Things (IoT) technologies enable connections among things with wider ranges. The development of such technologies in cyberspace promotes the convergence of physical space and cyberspace. The research on social attributes (e.g., relationships and social existence) that currently exist and on interactive behavior of physical things from the convergence of those two spaces can help to efficiently address certain social problems such as food safety, medicine source tracking, and traffic adjustments. This research on social attributes can also help to improve the serviceability of the IoT. At present, research on social attributes in the IoT has not modeled the relations of things in the IoT. In this paper, we formulate social attributes of thing, analyze the role of relations that is one of important social attributes in the IoT, and use super network architecture to present the complex relations among physical things. Based on these relations and relation architecture, we use an ontology-based approach to model the relations of things. Finally, we use a case to interpret the concrete application process of relations in smart home.

Identity management is a fundamental feature of Internet of Things (IoT) ecosystem, particularly for IoT data access control. However, most of the actual works adopt centralized approaches, which could lead to a single point of failure and privacy issues that are tied to the use of a trusted third parties. A consortium blockchain is an emerging technology that provides a neutral and trustable computation and storage platform that is suitable for building identity management solutions for IoT. This paper proposes a lightweight architecture and the associated protocols for consortium blockchain-based identity management to address privacy, security, and scalability issues in a centralized system for IoT. Besides, we implement a proof-of-concept prototype and evaluate our approach. We evaluate our work by measuring the latency and throughput of the transactions while using different query actions and payload sizes, and we compared it to other similar works. The results show that the approach is suitable for business adoption.

Internet use has seriously affected individual health in potential and subtle ways. Past research on the subject mainly concentrated on the mental health caused by addictive use, e.g., impulse control disorders and obsessive-compulsive disorder. With the constant development of cyber technology, Internet has been an indispensable element of living and working, which can provide more convenient and efficient support and help. At the same time, the effects of Internet use more than Internet addiction on individual health cannot be ignored in living and working. This paper aims to identify the most common physical complaints associated with Internet use, and further investigate the association between the frequency of Internet use and individual physical health. Five hundred and thirteen participants completed the questionnaires by online or offline manner, which covers demographic questions and questions concerning Internet use and physical complaints. The most common complaints were involving dry eyes, decreased vision, and cervical pain. The positive pearson correlation coefficient were found between the level of physical complaints and the frequency of Internet use, place of residence and education. Especially, the higher amount of time for the Internet use is strongly associated with a higher level of physical complaints.

To apply the algorithms in Internet of Things for physical world objects, the relationship between physical objects is becoming more and more complicated. As we know, social relationship is widely used in human world and social Internet of Things to solve the multiple object problems. Thus, a way via combining social relationship with physical object to solve the problem with a huge number of objects or complicated interactions among objects has been analyzed. This article proposes a new concept of “Physical Objects’ Social Relationship” for describing, managing, and predicting the relationships between physical objects in Internet of Things. The classification method for physical objects’ social relationships is proposed using the spatial-temporal attribute of social relationships. Moreover, the logical expression method for physical objects’ social relationships is discussed.

Our world is rapidly changing, and swiftly evolving towards so-called “smart worlds”. The smart worlds starting with smart things such as the smart objects, smart city, smart manufacturing, and smart systems, will eventually encompass all aspects of the cyber, physical, social and mental world. A cyber-enabled completely new digital space featured with ubiquitous interconnections, integrations and interactions of physical, social, mental and other spaces will continuously bring out more and more changes. Cyberspace is actually the combinational outcome of various technologies including computers, communications, materials, intelligence and studies in perception, cognition, biology, sociology, etc., as well as advanced computing like the Internet/Web, pervasive networks, ubiquitous sensing and computing, the internet of things, the cloud, big data and so forth. These smart worlds are set to be the next important stage in human history. We have to be aware of the essential problems and crucial issues affecting in building truly smart worlds that benefit humanity, and simultaneously safeguard the natural environment for sustainable development and evolution. Therefore, this is the time to foresee future trends and identify what the grand challenges for smart worlds are. This Special Section in IEEE ACCESS has ten papers, and a brief summary about each paper is presented as follows.

Smart worlds begin with smart things, such as smart objects, smart cities, smart manufacturing, and smart systems, are overlaid with sensing and actuation, many embedded in things, and eventually encompass all aspects of the cyber, physical, social, and thinking hyperspace. In the future, human beings will live in a smart environment where both life and work are well addressed by technology, whereas humans will be responsible only for providing creativity. For this purpose, we have organized the first 2015 Smart World Congress, including five IEEE International Conferences. Specifically, a variety of challenges are presented in the field of smart world. Therefore, an open forum on the top ten challenges (Top10Cs) for smart worlds was held under the congress to identify the main challenges through collecting the intelligence existing particularly in crowd wisdom. Top10Cs and related works, as a crowdsourcing approach, discuss and analyze the top challenges for smart worlds based on the selective results of the crowd and experts via an online platform. Moreover, we summarize the experiences obtained in organizing the Top10Cs forum.

The development of informationization and intelligentization prompts Internet developing toward a new era. A deep fusion among cyber space, physical space, social space, and thinking space brings a quaternionic cyber-physical-social-thinking hyperspace, based on which an embryo of smart world is being established through heterogeneous spaces. The smart world is expected to be an attractive perspective involving ubiquitous sensing, computing, and communication to achieve comprehensive interconnections of physical perception, cyber interaction, social correlation, and cognitive thinking. In this paper, evolution of the smart world is briefly introduced, and physical-based coordination, social-inspired interactivity, brain-abstracted cooperativity, and cyber-enabled homogeneity are, respectively, discussed as the main characteristics of the smart world.

Following the two trends of computerization and informatization, another emerging trend is cyberization in which numerous and various cyber entities in cyberspace will exist in cyber-enabled worlds, including the cyber world and cyber-conjugated physical, social, and mental worlds. Computer science and information science, as holistic fields, have, respectively, played important roles in computerization and informatization. Similarly, it is necessary for there to be a corresponding field for cyberization. Cybermatics is proposed as such a holistic field for the systematic study of cyber entities in cyberspace and cyber world, and their properties, functions, and conjugations with entities in conventional spaces/worlds. This paper sets out to explain the necessity and rationale for, and significance of, the proposed field of Cybermatics, what it is and what it encompasses, and how it is related to other fields and areas.

Electronic healthcare (eHealth) identity management (IdM) is a pivotal feature in the eHealth system. Distributed ledger technology (DLT) is an emerging technology that can achieve agreements of transactional data states in a decentralized way. Building identity management systems using Blockchain can enable patients to fully control their own identity and provide increased confidence in data immutability and availability. This paper presents the state of the art of decentralized identity management using Blockchain and highlights the possible opportunities for adopting the decentralized identity management approaches for future health identity systems. First, we summarize eHealth identity management scenarios. Furthermore, we investigate the existing decentralized identity management solutions and present decentralized identity models. In addition, we discuss the current decentralized identity projects and identify new challenges based on the existing solutions and the limitations when applying it to healthcare as a particular use case.

With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

Sensing technology has been widely investigated and utilized for gas detection. Due to the different applicability and inherent limitations of different gas sensing technologies, researchers have been working on different scenarios with enhanced gas sensor calibration. This paper reviews the descriptions, evaluation, comparison and recent developments in existing gas sensing technologies. A classification of sensing technologies is given, based on the variation of electrical and other properties. Detailed introduction to sensing methods based on electrical variation is discussed through further classification according to sensing materials, including metal oxide semiconductors, polymers, carbon nanotubes, and moisture absorbing materials. Methods based on other kinds of variations such as optical, calorimetric, acoustic and gas-chromatographic, are presented in a general way. Several suggestions related to future development are also discussed. Furthermore, this paper focuses on sensitivity and selectivity for performance indicators to compare different sensing technologies, analyzes the factors that influence these two indicators, and lists several corresponding improved approaches.

Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems. Aiming at this issue, orthogonal frequency division multiplexing (OFDM) has been identified as one of the successful technologies, which works with a multi-carrier parallel radio transmission strategy. In this article, through the use of swarm intelligence paradigm, a solution approach is accordingly proposed by employing an efficient Jaya algorithm, called PA-Jaya, to deal with the power allocation problem in cognitive OFDM radio networks for IoT. Because of the algorithm-specific parameter-free feature in the proposed PA-Jaya algorithm, a satisfactory computational performance could be achieved in the handling of this problem. For this optimization problem with some constraints, the simulation results show that compared with some popular algorithms, the efficiency of spectrum utilization could be further improved by using PA-Jaya algorithm with faster convergence speed, while maximizing the total transmission rate.

The intent of this paper is to provide an overview of basic concepts, types, techniques, and experimental studies of the current state-of-the-art Frequency Selective Surfaces (FSSs). FSS is a periodic surface with identical two-dimensional arrays of elements arranged on a dielectric substrate. An incoming plane wave will either be transmitted (passband) or reflected back (stopband), completely or partially, depending on the nature of array element. This occurs when the frequency of electromagnetic (EM) wave matches with the resonant frequency of the FSS elements. Therefore, an FSS is capable of passing or blocking the EM waves of certain range of frequencies in the free space; consequently, identified as spatial filters. Nowadays, FSSs have been studied comprehensively and huge growth is perceived in the field of its designing and implementation for different practical applications at frequency ranges of microwave to optical. In this review article, we illustrate the recent researches on different categories of FSSs based on structure design, array element used, and applications. We also focus on theoretical breakthroughs with fabrication techniques, experimental verifications of design examples as well as prospects and challenges, especially in the microwave regime. We emphasize their significant performance parameters, particularly focusing on how advancement in this field could facilitate innovation in advanced electromagnetics.

With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by multi-modal sensors or devices show great differences in formats, domains and types, which poses challenges for machines to process and understand. Therefore, adding semantics to Internet of Things becomes an overwhelming tendency. This paper provides a systematic review of data semantization in IoT, including its backgrounds, processing flows, prevalent techniques, applications, existing challenges and open issues. It surveys development status of adding semantics to IoT data, mainly referring to sensor data and points out current issues and challenges that are worth further study.

Internet-of-Things (IoT) include a large number of devices that can communicate across different networks. Cyber-Physical Systems (CPS) also includes a number of devices connected to the internet where wearable devices are also included. Both systems enable researchers to develop healthcare systems with additional intelligence as well as prediction capabilities both for lifestyle and in hospitals. It offers as much persistence as a platform to ubiquitous healthcare by using wearable sensors to transfer the information over servers, smartphones, and other smart devices in the Telecare Medical Information System (TMIS). Security is a challenging issue in TMIS, and resourceful access to health care services requires user verification and confidentiality. Existing schemes lack in ensuring reliable prescription safety along with authentication. This research presents a Secure Authentication and Prescription Safety (SAPS) protocol to ensure secure communication between the patient, doctor/nurse, and the trusted server. The proposed procedure relies upon the efficient elliptic curve cryptosystem which can generate a symmetric secure key to ensure secure data exchange between patients and physicians after successful authentication of participants individually. A trusted server is involved for mutual authentication between parties and then generates a common key after completing the validation process. Moreover, the scheme is verified by doing formal modeling using Rubin Logic and validated using simulations in NS-2.35. We have analyzed the SAPS against security attacks, and then performance analysis is elucidated. Results prove the dominance of SAPS over preliminaries regarding mutual authentication, message integrity, freshness, and session key management and attack prevention.