The Internet of Things (IoT) is a promising technology which tends to revolutionize and connect the global world via heterogeneous smart devices through seamless connectivity. The current demand for machine-type communications (MTC) has resulted in a variety of communication technologies with diverse service requirements to achieve the modern IoT vision. More recent cellular standards like long-term evolution (LTE) have been introduced for mobile devices but are not well suited for low-power and low data rate devices such as the IoT devices. To address this, there is a number of emerging IoT standards. Fifth generation (5G) mobile network, in particular, aims to address the limitations of previous cellular standards and be a potential key enabler for future IoT. In this paper, the state-of-the-art of the IoT application requirements along with their associated communication technologies are surveyed. In addition, the third generation partnership project cellular-based low-power wide area solutions to support and enable the new service requirements for Massive to Critical IoT use cases are discussed in detail, including extended coverage global system for mobile communications for the Internet of Things, enhanced machine-type communications, and narrowband-Internet of Things. Furthermore, 5G new radio enhancements for new service requirements and enabling technologies for the IoT are introduced. This paper presents a comprehensive review related to emerging and enabling technologies with main focus on 5G mobile networks that is envisaged to support the exponential traffic growth for enabling the IoT. The challenges and open research directions pertinent to the deployment of massive to critical IoT applications are also presented in coming up with an efficient context-aware congestion control mechanism.
The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network "edge," near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.
Battery technology is the bottleneck of the electric vehicles (EVs). It is important, both in theory and practical application, to do research on the modeling and state estimation of batteries, which is essential to optimizing energy management, extending the life cycle, reducing cost, and safeguarding the safe application of batteries in EVs. However, the batteries, with strong time-variables and nonlinear characteristics, are further influenced by such random factors such as driving loads, operational conditions, in the application of EVs. The real-time, accurate estimation of their state is challenging. The classification of the estimation methodologies for estimating state-of-charge (SoC) of battery focusing with the estimation method/algorithm, advantages, drawbacks, and estimation error are systematically and separately discussed. Especially for the battery packs existing of the inevitable inconsistency in cell capacity, resistance and voltage, the advanced characterizing monomer selection, and bias correction-based method has been described and discussed. The review also presents the key feedback factors that are indispensable for accurate estimation of battery SoC, it will be helpful for ensuring the SoC estimation accuracy. It will be very helpful for choosing an appropriate method to develop a reliable and safe battery management system and energy management strategy of the EVs. Finally, the paper also highlights a number of key factors and challenges, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.
The paradigm of Internet of Things (IoT) is paving the way for a world, where many of our daily objects will be interconnected and will interact with their environment in order to collect information and automate certain tasks. Such a vision requires, among other things, seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Such features can be brought by blockchain, a technology born with a cryptocurrency called Bitcoin. In this paper, a thorough review on how to adapt blockchain to the specific needs of IoT in order to develop Blockchain-based IoT (BIoT) applications is presented. After describing the basics of blockchain, the most relevant BIoT applications are described with the objective of emphasizing how blockchain can impact traditional cloud-centered IoT applications. Then, the current challenges and possible optimizations are detailed regarding many aspects that affect the design, development, and deployment of a BIoT application. Finally, some recommendations are enumerated with the aim of guiding future BIoT researchers and developers on some of the issues that will have to be tackled before deploying the next generation of BIoT applications.
The recent expansion of the Internet of Things (IoT) and the consequent explosion in the volume of data produced by smart devices have led to the outsourcing of data to designated data centers. However, to manage these huge data stores, centralized data centers, such as cloud storage cannot afford auspicious way. There are many challenges that must be addressed in the traditional network architecture due to the rapid growth in the diversity and number of devices connected to the internet, which is not designed to provide high availability, real-time data delivery, scalability, security, resilience, and low latency. To address these issues, this paper proposes a novel blockchain-based distributed cloud architecture with a software defined networking (SDN) enable controller fog nodes at the edge of the network to meet the required design principles. The proposed model is a distributed cloud architecture based on blockchain technology, which provides low-cost, secure, and on-demand access to the most competitive computing infrastructures in an IoT network. By creating a distributed cloud infrastructure, the proposed model enables cost-effective high-performance computing. Furthermore, to bring computing resources to the edge of the IoT network and allow low latency access to large amounts of data in a secure manner, we provide a secure distributed fog node architecture that uses SDN and blockchain techniques. Fog nodes are distributed fog computing entities that allow the deployment of fog services, and are formed by multiple computing resources at the edge of the IoT network. We evaluated the performance of our proposed architecture and compared it with the existing models using various performance measures. The results of our evaluation show that performance is improved by reducing the induced delay, reducing the response time, increasing throughput, and the ability to detect real-time attacks in the IoT network with low performance overheads.
In this paper, we investigate the dual hesitant bipolar fuzzy multiple attribute decision making problems in which there exists a prioritization relationship over attributes. Then, motivated by the idea of Hamacher operations and prioritized aggregation operators, we have developed some Hamacher prioritized aggregation operators for aggregating dual hesitant bipolar fuzzy information: dual hesitant bipolar fuzzy Hamacher prioritized average operator, dual hesitant bipolar fuzzy Hamacher prioritized geometric operator, dual hesitant bipolar fuzzy Hamacher prioritized weighted average operator, dual hesitant bipolar fuzzy Hamacher prioritized weighted geometric operator. Then, we have utilized these operators to develop some approaches to solve the dual hesitant bipolar fuzzy multiple attribute decision making problems. Finally, a real-world example is then analyzed to illustrate the relevance and effectiveness of the proposed methodology.
Identity-based cryptosystems mean that public keys can be directly derived from user identifiers, such as telephone numbers, email addresses, and social insurance number, and so on. So they can simplify key management procedures of certificate-based public key infrastructures and can be used to realize authentication in blockchain. Linearly homomorphic signature schemes allow to perform linear computations on authenticated data. And the correctness of the computation can be publicly verified. Although a series of homomorphic signature schemes have been designed recently, there are few homomorphic signature schemes designed in identity-based cryptography. In this paper, we construct a new ID-based linear homomorphic signature scheme, which avoids the shortcomings of the use of public-key certificates. The scheme is proved secure against existential forgery on adaptively chosen message and ID attack under the random oracle model. The ID-based linearly homomorphic signature schemes can be applied in e-business and cloud computing. Finally, we show how to apply it to realize authentication in blockchain.
Due to the significant advancement of the Internet of Things (IoT) in the healthcare sector, the security, and the integrity of the medical data became big challenges for healthcare services applications. This paper proposes a hybrid security model for securing the diagnostic text data in medical images. The proposed model is developed through integrating either 2-D discrete wavelet transform 1 level (2D-DWT-1L) or 2-D discrete wavelet transform 2 level (2D-DWT-2L) steganography technique with a proposed hybrid encryption scheme. The proposed hybrid encryption schema is built using a combination of Advanced Encryption Standard, and Rivest, Shamir, and Adleman algorithms. The proposed model starts by encrypting the secret data; then it hides the result in a cover image using 2D-DWT-1L or 2D-DWT-2L. Both color and gray-scale images are used as cover images to conceal different text sizes. The performance of the proposed system was evaluated based on six statistical parameters; the peak signal-to-noise ratio (PSNR), mean square error (MSE), bit error rate (BER), structural similarity (SSIM), structural content (SC), and correlation. The PSNR values were relatively varied from 50.59 to 57.44 in case of color images and from 50.52 to 56.09 with the gray scale images. The MSE values varied from 0.12 to 0.57 for the color images and from 0.14 to 0.57 for the gray scale images. The BER values were zero for both images, while SSIM, SC, and correlation values were ones for both images. Compared with the state-of-the-art methods, the proposed model proved its ability to hide the confidential patient's data into a transmitted cover image with high imperceptibility, capacity, and minimal deterioration in the received stego-image.
In recent years, the environmental problems in China have become more and more serious, for example, resource depletion, environmental pollution, ecological imbalance, and so on. At the same time, the laws and regulations about the environmental protection in the world are constantly introduced, the academic research about the environmental issues is also increasing, more and more consumers show concern for the green products, and the restrictions of the "green trade barrier" in the international to the export products in China enterprises must pay attention to the influence of their behavior on the environment in pursuit of economic efficiency. In this context, the green supply chain management (GSCM) emerges. GSCM theory first appeared in some foreign countries, and started later in China. Compared with some developed countries, the GSCM theory in China is still not mature. From the source speaking, there is lack of a complete GSCM system. In this paper, we combine the generalized weighted Bonferroni mean (GWBM) operator, generalized weighted geometric Bonferroni mean (GWGBM) operator, dual GWBM operator, and dual GWGBM operator with Pythagorean 2-tuple linguistic numbers to propose the generalized Pythagorean 2-tuple linguistic WBM (GP2TLWBM) operator, generalized Pythagorean 2-tuple linguistic WGBM (GP2TLWGBM) operator, dual GP2TLWBM (DGP2TLWBM) operator, and dual GP2TLWGBM (DGP2TLWGBM) operator, and then, the multiple attribute decision-making (MADM) methods are developed based on DGP2TLWBM and DGP2TLWGBM operators. Finally, we use an example for green supplier selection to prove the MADM process of the proposed algorithms.
Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.
Motor bearing is subjected to the joint effects of much more loads, transmissions, and shocks that cause bearing fault and machinery breakdown. A vibration signal analysis method is the most popular technique that is used to monitor and diagnose the fault of motor bearing. However, the application of the vibration signal analysis method for motor bearing is very limited in engineering practice. In this paper, on the basis of comparing fault feature extraction by using empirical wavelet transform (EWT) and Hilbert transform with the theoretical calculation, a new motor bearing fault diagnosis method based on integrating EWT, fuzzy entropy, and support vector machine (SVM) called EWTFSFD is proposed. In the proposed method, a novel signal processing method called EWT is used to decompose vibration signal into multiple components in order to extract a series of amplitude modulated-frequency modulated (AM-FM) components with supporting Fourier spectrum under an orthogonal basis. Then, fuzzy entropy is utilized to measure the complexity of vibration signal, reflect the complexity changes of intrinsic oscillation, and compute the fuzzy entropy values of AM-FM components, which are regarded as the inputs of the SVM model to train and construct an SVM classifier for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by using the simulated signal and real motor bearing vibration signals. The experiment results show that the EWT outperforms empirical mode decomposition for decomposing the signal into multiple components, and the proposed EWTFSFD method can accurately and effectively achieve the fault diagnosis of motor bearing.
With the purpose of identifying cyber threats and possible incidents, intrusion detection systems (IDSs) are widely deployed in various computer networks. In order to enhance the detection capability of a single IDS, collaborative intrusion detection networks (or collaborative IDSs) have been developed, which allow IDS nodes to exchange data with each other. However, data and trust management still remain two challenges for current detection architectures, which may degrade the effectiveness of such detection systems. In recent years, blockchain technology has shown its adaptability in many fields, such as supply chain management, international payment, interbanking, and so on. As blockchain can protect the integrity of data storage and ensure process transparency, it has a potential to be applied to intrusion detection domain. Motivated by this, this paper provides a review regarding the intersection of IDSs and blockchains. In particular, we introduce the background of intrusion detection and blockchain, discuss the applicability of blockchain to intrusion detection, and identify open challenges in this direction.
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the field. The advantage of machine learning in an era of medical big data is that significant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classification, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
For this paper, to consider both Bonferroni mean (BM) operator and two-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs), we combine the weighted BM (WBM) operator, the generalized WBM (GWBM) operator, and the dual GWBM operator with 2TLPFNs to propose the two-tuple linguistic Pythagorean WBM (2TLPFWBM) operator, the 2TLPFWGBM operator, the generalized 2TLPFWBM (G2TLPFWBM) operator, the generalized 2TLPFWGBM operator, the dual G2TLPFWBM operator, and the dual G2TLPFWGBM operator. Then, some MADM procedures are developed based on these operators. At last, an applicable example for a safety assessment of construction project is given.
Fully convolutional neural networks (FCNs) have been shown to achieve the state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Our proposed models significantly enhance the performance of fully convolutional networks with a nominal increase in model size and require minimal preprocessing of the data set. The proposed long short term memory fully convolutional network (LSTM-FCN) achieves the state-of-the-art performance compared with others. We also explore the usage of attention mechanism to improve time series classification with the attention long short term memory fully convolutional network (ALSTM-FCN). The attention mechanism allows one to visualize the decision process of the LSTM cell. Furthermore, we propose refinement as a method to enhance the performance of trained models. An overall analysis of the performance of our model is provided and compared with other techniques.
Dempster-Shafer evidence theory is efficient to deal with uncertain information. One assumption of evidence theory is that the source of information should be independent when combined by Dempster's rule for evidence combination. However, the assumption does not coincide with the reality. A lot of works are done to solve the problem about the independence. The existing method based on the statistical parameter Pearson correlation coefficient discount is one of the feasible methods. However, the Pearson correlation coefficient is only used to characterize the linear correlation between the attributes of the normal distribution. In this paper, a new method is proposed, the Pearson correlation coefficient and Shearman correlation coefficient to generate the discounting factor. Taking the parametric statistic and nonparametric statistic into consideration, the proposed method is more efficient. The experiments on wine data set are illustrated to show the efficiency of our proposed method.
This paper studies the application of cooperative techniques for non-orthogonal multiple access (NOMA). More particularly, the fixed gain amplify-and-forward (AF) relaying with NOMA is investigated over Nakagami-m fading channels. Two scenarios are considered insightfully: 1) the first scenario is that the base station (BS) intends to communicate with multiple users through the assistance of AF relaying, where the direct links are existent between the BS and users and 2) the second scenario is that the AF relaying is inexistent between the BS and users. To characterize the performance of the considered scenarios, new closed-form expressions for both exact and asymptomatic outage probabilities are derived. Based on the analytical results, the diversity orders achieved by the users are obtained. For the first and second scenarios, the diversity order for the nth user are μ(n + 1) and μn, respectively. Simulation results unveil that NOMA is capable of outperforming orthogonal multiple access (OMA) in terms of outage probability and system throughput. It is also worth noting that NOMA can provide better fairness compared with conventional OMA. By comparing the two scenarios, cooperative NOMA scenario can provide better outage performance relative to the second scenario.
The Internet of Things (IOT) is considered to be the third wave of the world information industry after the computer and the Internet. In order to realize the ubiquitous access to the IOT, the cellular network can only be fused with the wireless sensor network physical social systems. Based on this, the purpose of this paper is to extract the general functions that necessary but unrelated with the specific application in the cellular network and wireless sensor fusion technology. These general functions, including terminal registration and serial number assignment, terminal authentication management, connection management, terminal fault management, terminal state management and monitoring, congestion control, terminal dormancy and activation, and so on. Aiming at these general functions, the author gives an exploratory solution to provide a referable shared function module for the fusion of cellular network and wireless sensor networks physical social systems. It is of great significance to improve the development progress of the IOT, reduce the difficulty and cost of development.
Covert channels represent unforeseen communication methods that exploit authorized overt communication as the carrier medium for covert messages. Covert channels can be a secure and effective means of transmitting confidential information hidden in overt traffic. For covert timing channel, the covert message is usually modulated into inter-packet delays (IPDs) of legitimate traffic, which is not suitable for voice over LTE (VoLTE) since the IPDs of VoLTE traffic are fixed to lose the possibility of being modulated. For this reason, we propose a covert channel via adjusting silence periods, which modulates covert message by the postponing or extending silence periods in VoLTE traffic. To keep the robustness, we employ the Gray code to encode the covert message to reduce the impact of packet loss. Moreover, the proposed covert channel enables the tradeoff between the robustness and voice quality which is an important performance indicator for VoLTE. The experiment results show that the proposed covert channel is undetectable by statistical tests and outperforms the other covert channels based on IPDs in terms of robustness.
In this paper, we investigate the multiple attribute decision-making (MADM) problems for risk evaluation of enterprise human capital investment with interval-valued bipolar 2-tuple linguistic numbers (IVB2TLNs). Then, we propose some arithmetic and geometric aggregation operators with IVB2TLNs and analyze the characteristics of these operators. Furthermore, we present methods that apply these operators to solve the MADM problems with IVB2TLNs. Finally, a real-world example for the risk evaluation of enterprise human capital investment is used to illustrate the effectiveness of the proposed methodology.