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Robots, as the creation of human, became an irreplaceable component in human society. With the advancement of technologies, robots 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 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 make a collaboration 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.

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.

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.

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.

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.

Massive devices connected through fifth-generation (5G) networks constitute a ubiquitous Internet of Things (IoT), providing diverse service applications in a smart city. A robust network topology structure against cyber-attacks is the foundation of highly reliable service quality, especially in next-generation networks or beyond 5G (B5G) networks. Existing methods apply neural networks with deep reinforcement learning methods to advance the network topology. However, the reduction of unique hardware resource constraints and the application of edge intelligent networking capability of terminal nodes are emerging challenges for robustness optimization of IoT with 5G and B5G networks. To address these problems, we design a distributed learning framework utilizing edge intelligence, improving smart terminal nodes' networking capability, which is deployed on ordinary computers instead of specialized hardware such as GPUs. The proposed framework leveraging multi-core CPU and intelligent edge methods decreases the training time and economic cost and takes full advantage of computer resources. Furthermore, the best performing framework considers the distributed communication model of edge computing and optimizes the network topology by taking advantage of smart terminal nodes' contributions. We show that the framework succeeds in various topologies and outperforms compared with other state-of-art algorithms in improving the robustness for IoT topology in smart cities.

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.

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.

In recent times, there have been attempts to leverage artificial intelligence (AI) techniques in a broad range of cyber security applications. Therefore, this paper surveys the existing literature (comprising 54 papers mainly published between 2016 and 2020) on the applications of AI in user access authentication, network situation awareness, dangerous behavior monitoring, and abnormal traffic identification. This paper also identifies a number of limitations and challenges, and based on the findings, a conceptual human-in-the-loop intelligence cyber security model is presented.

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.

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 development of sensing and intelligent technologies, ambient sensor-based activity recognition is attracting more attention for a wide range of applications. One of the technology challenges is the recognition of the activity performer in a multi-occupancy scenario. This paper proposes a multi-label Markov Logic Network classification method to recognize resident types based on their activity habits and preference. The activity preference mainly includes time sequence preference, duration and period preference, and the location preference of a basic entity or action events. According to the resident type (gender, age bracket, job), the further reasoning work is the family role (mother, father, daughter and so on.) recognition. We have designed simple and combined preferences to test and evaluate our proposed method. Initial experiments have produced good performance in many cases proving this solution is an efficient and feasible method for resident type recognition which could be applied to real-world scenarios.

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.

The overwhelming increase of ubiquitous data, connections, and services brings serious challenges, in particular facing the demanding requirements of the Internet of Things (IoT). In order to seek better solutions and achieve more efficient information retrieval, artificial intelligence (AI) serves as a strong technical earthquake and contributes a lot to data analysis and decision making. It plays a compelling role in prompting digital and intelligent services. In this article, we focus on and emphasize the great significance pertaining to the convergence of IoT and AI. We first elaborate two typical forms of AI, namely knowledge-enabled AI and data-driven AI, with a comparison between respective advantages and disadvantages. Then we survey recent progress relating to the convergence of AI throughout the IoT architecture, from the sensing layer through the network layer to the application layer. In addition, a case study of a smart city is presented illustrating the convergence between IoT and AI. Furthermore, we point out open issues worth further research. The convergence of IoT and AI marries the merits of both and enables strong capability of resolving a broad range of problems.

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.

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.

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.

Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times.

With the continuous development of Internet of Things (IoT) and the overwhelming explosion of Big Data, edge computing serves as an efficient computing mode for time stringent data processing, which can bypass the constraints of network bandwidth and delay, and has been one of the foundation of interconnected applications. Although edge computing has gradually become one of bridges between cloud computing centers and mobile terminals, the literature still lacks a thorough review on the recent advances in edge computing platforms. In this paper, we firstly introduce the definition of edge computing and advantages of edge computing platform. And then, we summarize the key technologies of constructing an edge computing platform, and propose a general framework for edge computing platform. The role of distributed storage management systems in building edge computing platform is elaborated in detail. Furthermore, we give some applications to illustrate how to use third-party edge computing platforms to build specific applications. Finally, we briefly outline current open issues of edge computing platform based on our literature survey.

The extensive use of simulation-based serious games (SSGs) has made a revolution in educational techniques. As a potentially significant feature for SSG design and evaluation, the term fidelity (the similarity between an SSG and its real reference) emerges and attracts increasing attention. The study of fidelity not only benefits the design, development, and analysis of an SSG with the consideration of improving the learning effect but also contributes to the investment reduction of an SSG. However, the term fidelity is used inconsistently in the current literature. The introduction of new technologies (e.g., virtual reality) and the blend of multiform SSGs also facilitate the extension of fidelity with new connotations. All lead to confusing concepts and vague measure metrics. Besides, the relationship between fidelity and learning effect is still uncertain. A new vision and a comprehensive conceptual framework of fidelity for more general applications are in need. In this paper, further exploration and discussion of these issues in relation to fidelity of SSGs are presented through a systematic review. A general conceptual framework considering both aspects of the SSG system itself and the learners is developed and applied to analyze fidelity in SSGs. Based on that, a discussion on fidelity-related issues of SSG design and development is presented.

The emergence of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship between humans, computers and the physical environment. In this paper, we survey the advancement of CPSS through cyber-physical systems (CPS), cyber-social ​systems (CSS) and CPSS, as well as related techniques. CPSS are still at their infancy, most recent studies are application-specific and lack of systematic design methodology. To exploit the design methodology for CPSS, we review the existing system-level design methodologies in multiple application domains and further compare their performance characteristics and applicability for CPSS. Finally, we introduce our latest research advancement on system-level design methodology for CPSS and summarize future challenges for designing CPSS.

Physical unclonable function (PUF) is hardware-specific security primitive for providing cryptographic functionalities that are applicable for secure communication among the embedded devices. The physical structure of PUF is considered to be easy to manufacture but hard or impossible to replicate due to variations in its manufacturing process. However, a large community of analytics believes hardware-based PUF has paved the way for its realisation in providing dependable security. In this study, the authors have thoroughly explored the architecture, applications, requirements, and challenges of PUF that provide security solutions. For presenting the literature, they have designed a taxonomy where PUFs are divided under two main categories, including non-silicon and silicon-based PUF. Currently, there is no comprehensive survey that highlights the comparison and usability of memory-based and analogue/mixed-signal based PUF that are considered to be suitable as compared to counterparts. In a similar vein, they have presented the network-specific application scenarios in wireless sensor network, wireless body area network and Internet of Things and then identified the strong, weak and controlled PUF in a categorical manner. Moreover, they have presented a number of prospective limitations that are identified in PUF structures and then identified the open research challenges to meet the desired security levels.

In evolutionary dynamic optimization (EDO), most of the existing studies have assumed that dynamic optimization problems are black boxes. However, for many real-world problems, the dynamic parameters that cause the problems to change are observable. However, determining the utility of these parameters in improving optimization performance has not yet been well studied. In this paper, we propose and compare three strategies for this task: rote learning, fitting data with a feedforward neural network and an ensemble strategy. The main idea of these strategies is to learn the relation between the observable parameters and the optimal solutions and then predict new optima once the environment changes. We also propose a set of test cases representing different kinds of characteristics of real-world problems. In the experiments, the proposed strategies are compared with existing methods that do not use observable parameters, and the results validate our proposed strategies.

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., We review the existing research on Serious Games for Dementia Care (SGDC) from two perspectives: (1) the development of SGDC; (2) the different symptoms in different dementia stages. We also propose and discuss a comprehensive and professional assessment model of the therapeutic effectiveness of SGDC to compensate for the simplicity of existing assessment methods.

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.

Internet of Things (IoT) is expanding rapidly and so is the number of devices, sensors and actuators joining this world. IoT devices are an important part of the data collection process in Big Data systems, so by protecting them we support and improve the security of the whole system. ZigBee is a secure communication system for the underlying Internet of Things (IoT) infrastructure. Even though ZigBee has a strong security stack built on a variety of secret keys, ZigBee devices are vulnerable to the side-channel and key extraction attacks. Due to the low cost and limited resources, most ZigBee devices store their secret keys in plaintext. In this paper, we focus on protecting the storage of ZigBee secret keys and show how Physical Unclonable Functions (PUFs) can help the ZigBee devices to be robust tamper-resistant against the physical attacks. The proposed schemes include PUF-based key storage protection and key generation. The experiments in this paper were done using SRAM-PUF. Furthermore, two algorithms were proposed to overcome the defects in the randomness of keys generated using SRAM-PUF and, at the same time, to increase the reliability of these keys. We were able to significantly improve the hardware security of ZEDs by protecting their keying materials using costless, high secure, random, stable and volatile PUF-based secret keys.

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.

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.

With the popularization of the Internet of Things(IoT), the data are exploding. Data analysis is foundation of IoT based applications, and clustering is an important tool for data analysis. In clustering, determining the number of clusters is an important issue, which can be either designated artificially or determined automatically. The artificial methods have many disadvantages. And the automatic methods have distinct advantages, whose critical task is to design an appropriate clusters number updating algorithm. Although many researches have been made, most of them are not effective or cannot guarantee the unique clustering results and the good clustering accuracy rate. Meanwhile, considering that IoT based applications always involved both numerical data and nonnumeric data, and treating all the nonnumeric data in the same way is unpractical, we try to further classify the nonnumeric attributes according to their natures and explore the corresponding similarity metrics respectively. Based on it, an algorithm for determining the initial clustering centers is put forward by the dissimilarities and the densities of data objects. And then, an improved clustering algorithm is designed on a revised inter-cluster entropy for mixed data. The experiments on the 3 datasets in University of California at Irvine(UCI) show that the improved clustering algorithm is a deterministic clustering algorithm with good performance.

A novel approach is used to miniaturise the array size and broaden the bandwidth (BW) of a multilayer antenna-filter antenna- based frequency selective surface (FSS). Although such FSSs have broadly been investigated for higher frequency selectivity and less thickness, they still own high periodicity and lower BW. The proposed fractal FSS with centre square slots consists of three metallic layers, separated by two dielectric substrates and periodicity of the unit cell is sub-wavelength order (0.2 λ ₒ). Central square slots are added in the external Minkowski fractal patches to increase the capacitive effect whereas fractal slots are etched on the ground placed in the middle for enhanced coupling. It is demonstrated through simulations that proposed design can effectively minimise the element size and enhance the BW. Owing to minuscule array size and symmetric fractal geometry, the presented structure is insensitive to higher incidence angles (up to 30°) for both polarisations. A prototype of the proposed FSS is also fabricated and tested in a microwave anechoic chamber. An equivalent circuit model, full wave simulation and measurement results agree well and demonstrate stable response with 3 dB fractional BW approaching 87.4 and 92%, respectively, at a normal angle of incidence.

Web Ontology Language (OWL) designed to represent the class and relationships between classes is bringing a technological revolution in the Semantic Web. More and more knowledge is stored in the form of OWL. OWL repository refers to the systems that can store, query and infer OWL data. In OWL repository, information that users are forbidden to access (sensitive triples) can be inferred from information that the user have access to (non-sensitive triples) through inference rules, which brings about a security problem that some users’ sensitive information may be leaked. To protect sensitive triples, some researches have been done on the security problem, which mainly focus on removing some non-sensitive triples with low access frequency to achieve inference control. However, access frequency of a triple is unstable and it can not fully represent the importance of semantic information. In this paper, an inference control algorithm based on Semantic Value and access frequency is proposed. We introduced the concept of Degree of triples and Semantic Value, which can fully represent the importance of the semantic information represented by the triple. Moreover, we did experiments from the perspective of protecting sensitive triples of every user. Experimental results show that this algorithm can effectively prevent illegal inference and minimize the loss of semantic information.

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.

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.

With the rapid increase in number of sensing devices for healthcare, researchers are getting growing interest due to wide application support and new challenging scenarios. Internet of Things (IoT) comprises of huge number of connected devices with a variety of sensing support especially in medical health parameters sensing. In these scenarios, secure data collection and transmission to centralized servers is quite challenging to protect against several attacks for illegal data access. Existing solutions suffer from storage, communication and energy overheads. To resolve these issues, this paper presents a FoG assisted scheme for healthcare data aggregation in a secure and efficient manner.. We have involved the peer-to-peer communication between healthcare sensing devices and wearables to share secret data with an aggregating node that can share data with FoG server. In this scenario, an aggregator may be away from FoG server and cannot transmit data directly. However, it can share the encrypted data with the neighboring aggregator to transmit data to FoG server by appending in its current aggregated data. FoG server can extract the required values from the data and can save in the local repository that can be further updated later in cloud repositories. For these functionalities, we have presented two algorithms for message receiving at aggregator and message extraction at FoG server. Moreover, compression mechanism is also presented to further reduce the communication costs. We have performed simulations using TCL and C files in NS2.35 to generate trace files and then executed AWK scripts to extract results. Results prove the supremacy of proposed scheme over existing schemes in terms of storage, communication, transmission ratio, energy consumption and resilience.

In recent years, researchers have proposed some privacy-preserving public auditing schemes to remotely check the integrity of the outsourced data in cloud storage based on the homomorphic authenticators, random blocks sampling and random masking techniques. However, users need to maintain a table related to the block index in almost all of these schemes. It is difficult for users to maintain the table, especially when the outsourced data is frequently updated. To deal with it, a user stateless privacy-preserving public auditing scheme is put forward on rank-based authenticated skip list, which can support dynamical operations completely and makes it unnecessary for users to maintain the relevant table. When the server receives the data originally stored by the user or the user performs dynamic operations on its outsourced data, the server provide users with a time-stamped receipt signed by the both parties. The receipt can not only protect the cloud server from being extorted by the dishonest user, but also ensure that the cloud server update the user's data as required. A formal security proof for data integrity guarantee and an analysis for privacy-preserving property are made in the auditing phase to show the correctness of the scheme. The performance analysis indicates that our interactive audit protocol is with high efficiency.

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.

Recently, Human Recognition Activity (HAR) has received more and more attention. At present, Recurrent neural networks (RNN), long short-term memory in particular, are main approaches in HAR. However, RNN suffers from the fact that it cannot process sequences in parallel and longer sequences cannot be remembered well. Therefore, this paper applies the attention mechanism to explore the relevant time context, and proposes a new model, named DeepConvAttn. DeepConvAttn is based on a well-known deep learning model, DeepConvLSTM. These two models are compared in experiments, and the results show DeepConvAttn is better than DeepConvLSTM on two popular HAR benchmark datasets.

Different from current coherent-detection-based long-haul transmission systems, inter- and intradatacenter transmissions require a simpler transmitter and receiver. A promising way to significantly meet the demands of datacenter transmission is polarization division multiplexing intensity modulation with direct detection (PDM-IM-DD) using a Stokes vector receiver (SVR). However, for different SVR architectures, the corresponding demultiplexing matrix is required to recover the Stokes vectors from the detected signals, which are combined with an arbitrary state of polarization (SOP), will change the effect of noise dynamically and significantly influence the system performance. In this study, PDM-IM signals using four SVRs, i.e., a 90° optical hybrid with 2 balanced photodetectors (BPDs) and 2 photodetectors (PDs), a 90° optical hybrid with 4 PDs, a Stokes analyzer and a 3 × 3 coupler with 4 PDs, are studied theoretically and numerically. Theoretical system models using the four SVRs are developed, and the noise power variations are analyzed quantitatively based on these models. Moreover, the performance of the systems is also investigated for 224 Gbit/s polarization division multiplexing pulse amplitude modulation 4 level with direct detection (PDM-PAM4-DD) transmission in a simulation. The simulation results show that the bit error rate (BER) performance of the systems is consistent with the theoretical noise power variation curves. The theoretical analysis scheme is helpful for the practical design of SVR-based systems.

Due to the large number of students in a university classroom and crowded seat-ing, most features of student posture are typically obscured, making it difficult to balance accuracy in identifying students' postures with computational speed. To solve these problems, a classroom student posture recognition method is pro-posed in this article. First, because we must recognize the poses of multiple peo-ple 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. Then, to improve the accuracy of the pose estimation network, we use the squeeze-and-excitation network (SENet) structure that is embedded in the residual structure of high-resolution networks (HRNet). Finally, using the improved HRNet algorithm output of key human body points, we design a pose classification algorithm based on a support vector machine (SVM), which is used to classify human poses in the classroom. Experiments show that the improved HRNet multi-person pose estimation algorithm yields the best mean average precision (mAP) performance of 73.76 points in the common objects in context (COCO) validation dataset. We tested the proposed posture classification algorithm using a custom dataset collected in a classroom and were able to achieve a high recognition rate (90.1%) and robustness. Thus, the pro-posed method can effectively measure student postures.

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?

Internet of Things (IoT) is composed of a large number of devices that are connected to the Internet and can exchange information regarding different applications including healthcare, transportation, and agriculture. In IoT, the information and communication technology refers to the involvement of computing to reduce the communication costs for information sharing along with managing the storage capabilities. It becomes more interesting and challenging by involving the concept of caching for optimization that can also be perfectly mapped to improve sensing capabilities. This paper presents the impact of Computing, Communication, and Caching (CCC) over the smart sensing capabilities to highlight the possible opportunities for this convergence in recent enabling technologies including 5G, cloud, fog and edge computing based networks. In this regard, different technologies have been identified that help solving bottlenecks during smart sensing operations by a large number of physical devices in IoT. We have investigated that how the convergence of CCC can improve the smart IoT sensing. A number of requirements for smart IoT based sensing are identified. Moreover, new challenges are identified that cause limited services for IoT applications due to sensing limitation of current and future convergence in IoT enabling technologies.

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.

Kalman filter based algorithms such as unscented Kalman filter (UKF), and the unbiased conversion measurement Kalman filter (UCMKF) are the most popular nonlinear filters which used in tracking, navigation, estimation and information fusion. Applying those filters directly in East-North-Up (ENU) coordinates with motion models of sensors causes the degradation or even divergence of filter performance. To address this issue, we first analyzed and discussed the motion model consistency of moving sensor with a constant velocity (CV). Next, we proposed to extend the application of common filter algorithms to Earth Centered Earth Fixed (ECEF) coordinates to filter random errors. We verified the validity of our proposed method by filtering random errors in a constant velocity motion model of radar. The theoretical analysis and simulation results show that the extended algorithms provide better efficiency and compatibility in moving sensors.

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

It is well known that traditional microstrip (MS) transmission lines suffer from high transmission losses at microwave and millimeter wave frequencies. To solve the issue, a novel periodic subwavel...

Optimal power flow (OPF) refers to the problem of optimizing the operating decisions such as electric power generation in power systems, which are always subjected to dynamic factors like bus loads. Conventionally, OPF in dynamic environments has been solved by static-oriented optimization methods based on the prediction of the dynamic factors. However, as the dynamics of modern power systems become more and more complex and difficult to predict, research interest of intelligent methods that track the optimal decisions of OPF has been grown recently. Devoted to this objective, a learning enhanced differential evolution (LEDE) is proposed in this paper. LEDE incorporates the idea of nearest-neighbor rule from the field of machine learning, with which decisions of the previous environments are retrieved continually to replace the newly generated individuals of differential evolution. A so-called elitism stochastic ranking strategy is also proposed, used in LEDE to handle constraints of OPF. Experiments are conducted on the dynamic IEEE 30-bus system and IEEE 118-bus system, and the results show the efficiency of LEDE in comparison with other algorithms.

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.

In the Internet of Things (IoT), identification and resolution of physical object is crucial for authenticating object’s identity, controlling service access, and establishing trust between object and cloud service. With the development of computer vision and pattern recognition technologies, face has been used as a high-security identification and identity authentication method which has been deployed in various applications. Face identification can ensure the consistency between individual in physical-space and his/her identity in cyber-space during the physical–cyber space mapping. However, face is a non-code and unstructured identifier. With the increase of applications in current big data environment, the characteristic of face identification will result in the growing demands for computation power and storage capacity. In this paper, we propose a face identification and resolution scheme based on cloud computing to solve the above problem. The face identification and resolution system model is presented to introduce the processes of face identifier generation and matching. Then, parallel matching mechanism and cloud computing-based resolution framework are proposed to efficiently resolve face image, control personal data access and acquire individual’s identity information. It makes full use of the advantages of cloud computing to effectively improve computation power and storage capacity. The experimental result of prototype system indicates that the proposed scheme is practically feasible and can provide efficient face identification and resolution service.