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.
Motion planning is a fundamental research area in robotics. Sampling-based methods offer an efficient solution for what is otherwise a rather challenging dilemma of path planning. Consequently, these methods have been extended further away from basic robot planning into further difficult scenarios and diverse applications. A comprehensive survey of the growing body of work in sampling-based planning is given here. Simulations are executed to evaluate some of the proposed planners and highlight some of the implementation details that are often left unspecified. An emphasis is placed on contemporary research directions in this field. We address planners that tackle current issues in robotics. For instance, real-life kinodynamic planning, optimal planning, replanning in dynamic environments, and planning under uncertainty are discussed. The aim of this paper is to survey the state of the art in motion planning and to assess selected planners, examine implementation details and above all shed a light on the current challenges in motion planning and the promising approaches that will potentially overcome those problems.
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.
Key generation from the randomness of wireless channels is a promising alternative to public key cryptography for the establishment of cryptographic keys between any two users. This paper reviews the current techniques for wireless key generation. The principles, performance metrics and key generation procedure are comprehensively surveyed. Methods for optimizing the performance of key generation are also discussed. Key generation applications in various environments are then introduced along with the challenges of applying the approach in each scenario. The paper concludes with some suggestions for future studies.
The main vision of the Internet of Things (IoT) is to equip real-life physical objects with computing and communication power so that they can interact with each other for the social good. As one of the key members of IoT, Internet of Vehicles (IoV) has seen steep advancement in communication technologies. Now, vehicles can easily exchange safety, efficiency, infotainment, and comfort-related information with other vehicles and infrastructures using vehicular ad hoc networks (VANETs). We leverage on the cloud-based VANETs theme to propose cyber-physical architecture for the Social IoV (SIoV). SIoV is a vehicular instance of the Social IoT (SIoT), where vehicles are the key social entities in the machine-to-machine vehicular social networks. We have identified the social structures of SIoV components, their relationships, and the interaction types. We have mapped VANETs components into IoT-A architecture reference model to offer better integration of SIoV with other IoT domains. We also present a communication message structure based on automotive ontologies, the SAE J2735 message set, and the advanced traveler information system events schema that corresponds to the social graph. Finally, we provide the implementation details and the experimental analysis to demonstrate the efficacy of the proposed system as well as include different application scenarios for various user groups.
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.
The Big Data revolution promises to transform how we live, work, and think by enabling process optimization, empowering insight discovery and improving decision making. The realization of this grand potential relies on the ability to extract value from such massive data through data analytics; machine learning is at its core because of its ability to learn from data and provide data driven insights, decisions, and predictions. However, traditional machine learning approaches were developed in a different era, and thus are based upon multiple assumptions, such as the data set fitting entirely into memory, what unfortunately no longer holds true in this new context. These broken assumptions, together with the Big Data characteristics, are creating obstacles for the traditional techniques. Consequently, this paper compiles, summarizes, and organizes machine learning challenges with Big Data. In contrast to other research that discusses challenges, this work highlights the cause-effect relationship by organizing challenges according to Big Data Vs or dimensions that instigated the issue: volume, velocity, variety, or veracity. Moreover, emerging machine learning approaches and techniques are discussed in terms of how they are capable of handling the various challenges with the ultimate objective of helping practitioners select appropriate solutions for their use cases. Finally, a matrix relating the challenges and approaches is presented. Through this process, this paper provides a perspective on the domain, identifies research gaps and opportunities, and provides a strong foundation and encouragement for further research in the field of machine learning with Big Data.
Index modulation has become a promising technique in the context of orthogonal frequency division multiplexing (OFDM), whereby the specific activation of the frequency domain subcarriers is used for implicitly conveying extra information, hence improving the achievable throughput at a given bit error ratio (BER) performance. In this paper, a dual-mode OFDM technique (DM-OFDM) is proposed, which is combined with index modulation and enhances the attainable throughput of conventional index-modulation-based OFDM. In particular, the subcarriers are divided into several subblocks, and in each subblock, all the subcarriers are partitioned into two groups, modulated by a pair of distinguishable modem-mode constellations, respectively. Hence, the information bits are conveyed not only by the classic constellation symbols, but also implicitly by the specific activated subcarrier indices, representing the subcarriers' constellation mode. At the receiver, a maximum likelihood (ML) detector and a reduced-complexity near optimal log-likelihood ratio-based detector are invoked for demodulation. The minimum distance between the different legitimate realizations of the OFDM subblocks is calculated for characterizing the performance of DM-OFDM. Then, the associated theoretical analysis based on the pairwise error probability is carried out for estimating the BER of DM-OFDM. Furthermore, the simulation results confirm that at a given throughput, DM-OFDM achieves a considerably better BER performance than other OFDM systems using index modulation, while imposing the same or lower computational complexity. The results also demonstrate that the performance of the proposed low-complexity detector is indistinguishable from that of the ML detector, provided that the system's signal to noise ratio is sufficiently high.
Fog computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, to date, have been considered standing-alone. However, because of their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the IoE, Smart City, Industry 4.0, and Big Data Streaming, while introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE and then we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterward, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only device-to-device links to establish inter-thing "ad hoc" communication. Last, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects.
This paper presents the latest progress on cloud RAN (C-RAN) in the areas of centralization and virtualization. A C-RAN system centralizes the baseband processing resources into a pool and virtualizes soft base-band units on demand. The major challenges for C-RAN including front-haul and virtualization are analyzed with potential solutions proposed. Extensive field trials verify the viability of various front-haul solutions, including common public radio interface compression, single fiber bidirection and wavelength-division multiplexing. In addition, C-RANs facilitation of coordinated multipoint (CoMP) implementation is demonstrated with 50%-100% uplink CoMP gain observed in field trials. Finally, a test bed is established based on general purpose platform with assisted accelerators. It is demonstrated that this test bed can support multi-RAT, i.e., Time-Division Duplexing Long Term Evolution, Frequency-Division Duplexing Long Term Evolution, and Global System for Mobile Communications efficiently and presents similar performance to traditional systems.
With the accelerated development of Internet-of-Things (IoT), wireless sensor networks (WSNs) are gaining importance in the continued advancement of information and communication technologies, and have been connected and integrated with the Internet in vast industrial applications. However, given the fact that most wireless sensor devices are resource constrained and operate on batteries, the communication overhead and power consumption are therefore important issues for WSNs design. In order to efficiently manage these wireless sensor devices in a unified manner, the industrial authorities should be able to provide a network infrastructure supporting various WSN applications and services that facilitate the management of sensor-equipped real-world entities. This paper presents an overview of industrial ecosystem, technical architecture, industrial device management standards, and our latest research activity in developing a WSN management system. The key approach to enable efficient and reliable management of WSN within such an infrastructure is a cross-layer design of lightweight and cloud-based RESTful Web service.
Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.
A hybrid antenna is proposed for future 4G/5G multiple input multiple output (MIMO) applications. The proposed antenna is composed of two antenna modules, namely, 4G antenna module and 5G antenna module. The 4G antenna module is a two-antenna array capable of covering the GSM850/900/1800/1900, UMTS2100, and LTE2300/2500 operating bands, while the 5G antenna module is an eight-antenna array operating in the 3.5-GHz band capable of covering the C-band (3400-3600 MHz), which could meet the demand of future 5G application. Compared with ideal uncorrelated antennas in an 8 × 8 MIMO system, the 5G antenna module has shown good ergodic channel capacity of ~40 b/s/Hz, which is only 6 b/s/Hz lower than ideal case. This multi-mode hybrid antenna is fabricated, and typically, experimental results such as S-parameter, antenna efficiency, radiation pattern, and envelope correlation coefficient are presented.
A feature of the Internet of Things (IoT) is that some users in the system need to be served quickly for small packet transmission. To address this requirement, a new multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) scheme is designed in this paper, where one user is served with its quality of service requirement strictly met, and the other user is served opportunistically by using the NOMA concept. The novelty of this new scheme is that it confronts the challenge that the existing MIMO-NOMA schemes rely on the assumption that users' channel conditions are different, a strong assumption which may not be valid in practice. The developed precoding and detection strategies can effectively create a significant difference between the users' effective channel gains, and therefore, the potential of NOMA can be realized even if the users' original channel conditions are similar. Analytical and numerical results are provided to demonstrate the performance of the proposed MIMO-NOMA scheme.
Wireless sensor networks (WSNs) will be integrated into the future Internet as one of the components of the Internet of Things, and will become globally addressable by any entity connected to the Internet. Despite the great potential of this integration, it also brings new threats, such as the exposure of sensor nodes to attacks originating from the Internet. In this context, lightweight authentication and key agreement protocols must be in place to enable end-to-end secure communication. Recently, Amin et al. proposed a three-factor mutual authentication protocol for WSNs. However, we identified several flaws in their protocol. We found that their protocol suffers from smart card loss attack where the user identity and password can be guessed using offline brute force techniques. Moreover, the protocol suffers from known session-specific temporary information attack, which leads to the disclosure of session keys in other sessions. Furthermore, the protocol is vulnerable to tracking attack and fails to fulfill user untraceability. To address these deficiencies, we present a lightweight and secure user authentication protocol based on the Rabin cryptosystem, which has the characteristic of computational asymmetry. We conduct a formal verification of our proposed protocol using ProVerif in order to demonstrate that our scheme fulfills the required security properties. We also present a comprehensive heuristic security analysis to show that our protocol is secure against all the possible attacks and provides the desired security features. The results we obtained show that our new protocol is a secure and lightweight solution for authentication and key agreement for Internet-integrated WSNs.
Industrial systems always prefer to reduce their operational expenses. To support such reductions, they need solutions that are capable of providing stability, fault tolerance, and flexibility. One such solution for industrial systems is cyber physical system (CPS) integration with the Internet of Things (IoT) utilizing cloud computing services. These CPSs can be considered as smart industrial systems, with their most prevalent applications in smart transportation, smart grids, smart medical and eHealthcare systems, and many more. These industrial CPSs mostly utilize supervisory control and data acquisition (SCADA) systems to control and monitor their critical infrastructure (CI). For example, WebSCADA is an application used for smart medical technologies, making improved patient monitoring and more timely decisions possible. The focus of the study presented in this paper is to highlight the security challenges that the industrial SCADA systems face in an IoT-cloud environment. Classical SCADA systems are already lacking in proper security measures; however, with the integration of complex new architectures for the future Internet based on the concepts of IoT, cloud computing, mobile wireless sensor networks, and so on, there are large issues at stakes in the security and deployment of these classical systems. Therefore, the integration of these future Internet concepts needs more research effort. This paper, along with highlighting the security challenges of these CI's, also provides the existing best practices and recommendations for improving and maintaining security. Finally, this paper briefly describes future research directions to secure these critical CPSs and help the research community in identifying the research gaps in this regard.
To meet the fast-growing energy demand and, at the same time, tackle environmental concerns resulting from conventional energy sources, renewable energy sources are getting integrated in power networks to ensure reliable and affordable energy for the public and industrial sectors. However, the integration of renewable energy in the ageing electrical grids can result in new risks/challenges, such as security of supply, base load energy capacity, seasonal effects, and so on. Recent research and development in microgrids have proved that microgrids, which are fueled by renewable energy sources and managed by smart grids (use of smart sensors and smart energy management system), can offer higher reliability and more efficient energy systems in a cost-effective manner. Further improvement in the reliability and efficiency of electrical grids can be achieved by utilizing dc distribution in microgrid systems. DC microgrid is an attractive technology in the modern electrical grid system because of its natural interface with renewable energy sources, electric loads, and energy storage systems. In the recent past, an increase in research work has been observed in the area of dc microgrid, which brings this technology closer to practical implementation. This paper presents the state-of-the-art dc microgrid technology that covers ac interfaces, architectures, possible grounding schemes, power quality issues, and communication systems. The advantages of dc grids can be harvested in many applications to improve their reliability and efficiency. This paper also discusses benefits and challenges of using dc grid systems in several applications. This paper highlights the urgent need of standardizations for dc microgrid technology and presents recent updates in this area.
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.