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Fifth-Generation (5G) mobile cellular networks provide a promising platform for new, innovative and diverse IoT applications, such as ultra-reliable and low latency communication, real-time and dynamic data processing, intensive computation, and massive device connectivity. End-to-End (E2E) network slicing candidates present a promising approach to resource allocation and distribution that permit operators to flexibly provide scalable virtualized and dedicated logical networks over common physical infrastructure. Though network slicing promises the provision of services on demand, many of its use cases, such as self-driving cars and Google's Stadia, would require the integration of a Multi-Access Edge Computing (MEC) platform in 5G networks. Edge Computing is envisioned as one of the key drivers for 5G and Sixth-Generation (6G) mobile cellular networks, but its role in network slicing remains to be fully explored. We investigate MEC and network slicing for the provision of 5G service focused use cases. Recently, changes to the cloud-native 5G core are a focus with MEC use cases providing network scalability, elasticity, flexibility, and automation. A cloud-native microservices architecture, along with its potential use cases for 5G network slicing, is envisioned. This paper also elaborates on the recent advances made in enabling E2E network slicing, its enabling technologies, solutions, and current standardization efforts. Finally, this paper identifies open research issues and challenges and provides possible solutions and recommendations.

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

Multimedia sensors have recently become a significant data source in the Internet of Things (IoT), giving rise to the Internet of Media Things (IoMT). Since multimedia applications are usually latency-sensitive, data processing in the cloud is not always suitable. A strategy to minimize delay is processing multimedia streams closer to data sources, exploiting resources at the network edge. Moreover, virtualization is widely used to reduce complexity arising from heterogeneity in IoT environments. In this paper, we propose an edge-based architecture and platform to manage virtual multimedia sensors (VMS), enabling IoMT applications to be easily deployed. Our proposal encompasses V-PRISM, a software architecture tailored for IoMT, and ALFA, a distributed implementation of the architecture. V-PRISM components were designed to be deployed and executed in multiple edge nodes. VMSs are in charge of processing multimedia streams and provide an abstraction layer between IoMT applications and physical devices that produce those streams. This paper describes the proposal and the results of experiments showing that the proposed approach can successfully perform multimedia stream processing for applications that requires low-latency.

The epidemic increase in online reviews’ growth made the sentiment classification a fascinating domain in academic and industrial research. The reviews assist several domains, which is complicated to gather annotated training data. Several sentiment classification methodologies are devised for performing the sentiment analysis, but retrieval of information is not accurately performed, less effective, and less convergence speed. In this paper, we propose a sentiment paper proposes a sentiment classification model, namely Spider Monkey Crow Optimization algorithm (SMCA), for training the deep recurrent neural network (DeepRNN). In this method, the telecom review is employed to remove stop words and stemming to eliminate inappropriate data to minimize user’s seeking time. Meanwhile, the feature extraction is performed using SentiWordNet to derive the sentiments from the reviews. The extracted SentiWordNet features and other features, like elongated words, punctuation, hashtag, and numerical values, are employed in the DeepRNN for classifying sentiments. To retrieve the required review, the Fuzzy K-Nearest neighbor (Fuzzy-KNN) is employed to retrieve the review based on a distance measure. With rigorous assessments and experimentation, it is observed that the proposed SMCA-based DeepRNN performs better in terms of accuracy of 97.7%, precision of 95.5%, recall of 94.6%, and F1-score 96.7%, respectively.

As Blockchain innovation picks up popularity in many areas, it is frequently hailed as a sound innovation. Because of the decentralization and encryption, many imagine that data put away in a Blockchain is and will consistently be protected. Among various abstraction layers of Blockchain architecture, the consensus layer is the core component behind the performance and security measures of the Blockchain network. Consensus mechanisms are a critical component of a Blockchain system’s long-term stability. Consensus forms the core of blockchain technology. Therefore, a range of consensus protocols has been introduced to maximize Blockchain systems’ efficiency and meet application domains’ individual needs. This research paper describes the layered architecture of Blockchain. A comprehensive review of mainstream consensus protocols mainly Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Activity (PoA) is presented in the paper. These mainstream consensus protocols have been explained and detailed performance analysis of these consensus protocols has been done. We have proposed a performance matrix of these consensus protocols based on different parameters like Degree of decentralization, Latency, Fault Tolerance Rate, Scalability, etc. Consensus protocols being the core of a strong fault-tolerant secured blockchain system, the proposed work intends to help inappropriate protocol selection and further research on strengthening trust and ownership in the technology. Depending upon different parameters like decentralization which is low in POA compared to other protocols, whereas POW is non-scalable, so depending on the priority of a particular performance parameter, the paper will help in the selection of a specific protocol.

The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-to-end behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.

The problem of finding the route with maximum end-to-end spectral efficiency in a multihop wireless network has been subject to considerable interest in the recent literature. It has been established that the problem is polynomially solvable in networks that employ time division multiple access (TDMA) or frequency division multiple access (FDMA). The main characteristic of TDMA and FDMA is being interference-free. Motivated by the advances in full-duplex (FD) communications and self-interference (SI) cancelation, this paper considers the problem for interference-limited wireless networks, in which nodes can send and receive at the same time and using the same frequency, but all links may interfere with each other. The contribution of this paper is three-fold. We formulate the problem as a mixed integer non-linear programming problem, and provide two equivalent re-formulations that provide more insight into the problem structure. Moreover, to the best of our knowledge for the first time, we provide a formal proof that the problem is NP-complete in the interference-limited case. Our proof uses reduction from a more general link scheduling problem subject to signal-to-interference-plus-noise-ratio (SINR) constraints. Finally, using an algorithm based on searching over paths while pruning the allowed number of hops, we provide a detailed numerical study that illustrates the tradeoffs between interference-limited FD relaying and TDMA half-duplex (HD) relaying. Our results indicate that sub-optimal interference-limited FD relaying leads to up to 2.69 times higher spectral efficiencies as compared to optimal TDMA HD relaying. Moreover, the lower the signal-to-noise ratio (SNR) regime, the larger the network size and/or the higher the SI capabilities, the higher the spectral efficiency gain due to FD (over HD) relaying.

In this paper, a novel reversible data hiding method for encrypted images (RDHEI) is proposed. An efficient coding scheme based on cyclic binary Golay (23, 12) code is designed to embed additional data into the least significant bits (LSBs) of the encrypted image. The most significant bits (MSBs) are used to ensure the reversibility of the embedding process. The proposed scheme is lossless, and based on the receiver’s privileges, allows recovery of marked data, original data and embedded data. Furthermore, the scheme can be used with any type of data, however it is best suited to 16-bit DICOM images of monochrome photometric interpretation. A modification to the standard DICOM network model was also introduced, to point out an example application of the proposed RDHEI method, i.e. an anonymized data storage outsourcing. A computer-based analysis has been carried out and simulation results are shown at the end of this paper.

Near Field Communication (NFC) is a prominent short-range, contact-less communication technology, which is rapidly getting popular in modern smart devices. For communication between two active devices via NFC, applications generally choose the peer-to-peer operation mode. In this paper, we exploit the possibility of using the NFC read/write mode, designed primarily for unidirectional data transfer from an active NFC reader to a passive NFC tag, for bi-directional half-duplex communication between two active NFC devices. The advantages of using the NFC read/write mode include low protocol overhead and permitting different data formats. However, the challenges are avoiding the reader collision problem, maintaining a secure session, and completing all transactions in an acceptable time frame. In this paper, we address the above challenges and propose a methodology for efficient communication between active NFC devices using NFC read/write mode. To evaluate the scheme, we design a secure Multi-Factor Authentication (MFA) system that requires bi-directional communication for mutually authenticating two NFC devices. The proposed methodology is experimentally verified using NFC-enabled Android smartphones and a Kerberos server as the third-party authenticator.

Identifying k ey frames is the first and necessary step before solving the variety of other B haratanatyam problems. The paper aims to partition the momentarily stationary frames (key frame s) from this dance video’s motion frames. The proposed key frame s (KFs) localization is novel, simple, and effective compared to the existing dance video analysis methods. It is distinctive from standard KFs detection algorithms as used in other human motion videos. In the dance’s basic structure, the occurrence of KFs during performances is often not completely stationary and varies with the dance form and the performer. Hence, it is not easy to decide a global threshold (on the quantum of motion) to work across dancers and performances. The earlier approaches try to compute the threshold iteratively. However, the novelty of the paper is: (a) formulating an adaptive threshold, (b) adopting Machine Learning (ML) approach and, (c) generating the effective feature by combining three frame differencing and bit-plane technique for the KF detection. In ML, we use Support Vector Machine (SVM) and Convolutional Neural Network (CNN) as the classifiers. The proposed approaches are also compared and analyzed with the earlier approaches. Finally, the proposed ML techniques emerge as a winner with around 90% accuracy.

This paper presents an overview of device identification techniques and the Manufacturer Usage Description (MUD) standard used for the Internet of things to reduce the IoT attack surface. The ongoing diversity and the sheer increase in the number of connected IoT devices have crumpled security efforts. There is a need to reconsider and redesign the underlying concept of developing security systems to resolve IoT security challenges. In this backdrop, device profiling and identification have emerged as an exciting technique that helps to reduce IoT device attack surface. One of the known approaches for device identification is to fingerprint a device. There are many ways to fingerprint the device, mostly using device network flows or device local attributes. The device identification ensures the authenticity of the device attached to the network, like user authentication. Since IoT devices mostly work using machine-to-machine (M2M) communication, this requires identifying each device properly. But there is no unified approach for device identification for the ever-growing world of IoT devices and applications. One of the major steps forward in this direction is the development of the Manufacturer Usage Description (MUD) standard that defines the role of a device within the network. It limits the device to execute the primary task only, which will help to reduce the attack surface. Since the inception of MUD, many security frameworks use this standard for IoT security. However, there is a need to scrutinize the security frameworks based on the MUD, to find out the claimed effectiveness of the standard in IoT security. This paper initially identifies and classifies the potential vulnerabilities in IoT devices. Then, the study provides an overview of the research that focuses on device identification techniques and analyzes their role in IoT security. Finally, the research presents an overview of MUD technology, its implementation scenarios, the limitation of the latest MUD standard, and its applications in the industry. The prime aim of this work is to examine the MUD benefits in IoT security along with the weaknesses and challenges while implementing this standard along with future directions.

Cryptographic algorithms are widely used in information security fields such as network protocol authentication and commercial encryption software. Password recovery based on the hash algorithm is an important means of electronic forensics, encrypted information restoration, illegal information filtering, and network security maintenance. The traditional password recovery system is based mainly on the CPU and GPU and has a low energy efficiency ratio and cracking efficiency and cannot meet high-performance computing requirements. To further improve the computational efficiency and application flexibility of password recovery algorithms, this paper proposes a reconfigurable computing kernel design method based on a hybrid reconfigurable computing array (HRCA). Through in-depth analysis of the hash algorithm, the basic computing kernel set is extracted, and the combination design is carried out from the unit kernel, interconnection and storage structure to reconstruct the hash algorithm to match the application with the appropriate structure. Second, combined with the pipeline technology, the full pipeline hash and high-speed password attack algorithms are optimized and implemented to meet the needs of high-performance computing. Finally, an advanced computing kernel library is established, and the combination of a computing kernel map from the control and communication levels to achieve multidimensional reconfigurable computing and an overall placement strategy is used to make full use of the chip resources to improve computational efficiency. The experimental results and analysis show that compared with traditional CPU and GPU methods, the password recovery algorithm designed in this paper has the highest cracking speeds at 78.22 times and 2.65 times that of the CPU and GPU, respectively, and the highest energy efficiency ratio is 25.88 times and 3.16 times that of the CPU and GPU, respectively. Furthermore, the recovery efficiency has been significantly improved and meets the requirements of high-performance password recovery computing.

In this paper, we are proposing and validating “mobTHE” protocol that is designed to fill the gap in handling seamless mobile communication for on-body nodes. Therefore, “mobTHE” protocol tackles the dis-connectivity problem resulted from the sensor nodes' mobility while keeping an eye on the network's sustainability in terms of maximum packet throughput, prolonged node lifetime, and low temperature. To provide such a balanced trade-off, we utilize two Coordinator Nodes (CNs), namely, CN1 and CN2. Both of these CNs are working as Serving CN (SCN) to its closest nodes and as Neighbour CN (NCN) to the sensor nodes located far from these CNs, which enable collecting data from different sensor nodes continuously. The challenge here is the synchronization between the two CNs in such a way to optimize network resources via minimizing packet transmission duplication and packet drop rate, which is the main contribution of this paper. We present a novel Handover (HO) mechanism during the sensor node mobility and changing between the two CNs. That HO mechanism is capable of minimizing the redundancy of packets and leading to minimization in the packet drop rate. Considering the worst-case scenario where the sensor node is out of the two CNs range, we exploit two-hops data transmission. Parent Node (PN) is selected “on demand” to carry only the data of the moving node which moves away from both CNs rather than carrying the data of all sensor nodes which conserves the energy of the PN nodes as well as the moving node. The simulation analysis depicted that “mobTHE” protocol outperforms the conventional “THE” and “iM-SIMPLE” protocols in terms of temperature control, WBAN lifetime, throughput, and overall residual energy.

A distributed denial of service (DDoS) attack represents a major threat to service providers. More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by overwhelming the target with a massive number of malicious requests. A cyberattack of this kind is likely to result in tremendous economic losses for businesses and service providers due to increasing both operating and financial costs. In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks. Indeed, many defense systems have been transformed into smart and intelligent systems through the use of ML techniques, which allow them to defeat DDoS attacks. This paper analyzes recent studies concerning DDoS detection methods that have adapted single and hybrid ML approaches in modern networking environments. Additionally, the paper discusses different DDoS defense systems based on ML techniques that make use of a virtualized environment, including cloud computing, software-defined network, and network functions virtualization environments. As the development of the Internet of Things (IoT) has been the subject of significant research attention in recent years, the paper also discusses ML approaches as security solutions against DDoS attacks in IoT environments. Furthermore, the paper recommends a number of directions for future research. This paper is intended to assist the research community with the design and development of effective defense systems capable of overcoming different types of DDoS attacks.

Cloud computing has become a widely exploited research area in academia and industry. Cloud computing benefits both cloud services providers (CSPs) and consumers. The security challenges associated with cloud computing have been widely studied in the literature. This systematic literature review (SLR) is aimed to review the existing research studies on cloud computing security, threats, and challenges. This SLR examined the research studies published between 2010 and 2020 within the popular digital libraries. We selected 80 papers after a meticulous screening of published works to answer the proposed research questions. The outcomes of this SLR reported seven major security threats to cloud computing services. The results showed that data tampering and leakage were among the highly discussed topics in the chosen literature. Other identified security risks were associated with the data intrusion and data storage in the cloud computing environment. This SLR’s results also indicated that consumers’ data outsourcing remains a challenge for both CSPs and cloud users. Our survey paper identified the blockchain as a partnering technology to alleviate security concerns. The SLR findings reveal some suggestions to be carried out in future works to bring data confidentiality, data integrity, and availability.

A detection model of Internet of Things encrypted traffic based on edge intelligence is proposed in the paper, which can reduce the communication times of distributed Internet of Things gateways in the process of edge intelligence as well as the encrypted traffic detection model establishment time, in order to solve the problems that it is difficult to carry out efficient classification and accurate identification of the encrypted traffic of Internet of Things. In this paper, four new classification and identification methods for encrypted traffic are put forward, namely time-sequence behavior analysis, dynamic behavior analysis, key behavior analysis and two-round filtering analysis. The experimental results show that when the sample size is 1600, the encrypted traffic detection model establishment time is less than 100 seconds, and the accuracy of all the four new traffic classification methods is more than 92% and the recall rates of them are more than 83%.

Enhancement of low-light images is a challenging task due to the impact of low brightness, low contrast, and high noise. The inability to collect natural labeled data intensifies this problem further. Many researchers have attempted to solve this problem using learning-based approaches; however, most models ignore the impact of noise in low-lit images. In this paper, an encoder-decoder architecture, made up of separable convolution layers that solve the issues encountered in low-light image enhancement, is proposed. The architecture is trained end-to-end on a custom low-light image dataset (LID), comprising both clean and noisy images. We introduce a unique multi-context feature extraction module (MC-FEM) where the input first passes through a feature pyramid of dilated separable convolutions for hierarchical-context feature extraction followed by separable convolutions for feature compression. The model is optimized using a novel three-part loss function that focuses on high-level contextual features, structural similarity, and patch-wise local information. We conducted several ablation studies to determine the optimal model for low-light image enhancement under noisy and noiseless conditions. We have used performance metrics like peak-signal-to-noise ratio, structural similarity index matrix, visual information fidelity, and average brightness to demonstrate the superiority of the proposed work against the state-of-the-art algorithms. Qualitative results presented in this paper prove the strength and suitability of our model for real-time applications.

How to extract discriminative features from redundant video information is a key issue for video pedestrian re-identification. Factors such as occlusion, perspective, and posture changes in complex environments pose severe challenges to pedestrian re-identification based on local methods. In this paper, a posture-guided multi-scale structural relationship learning pedestrian re-identification method is proposed. The purpose is to analyze the video sequence of pedestrians based on the reference pose and the pose alignment model, and extract the sample frame with the highest image quality and the most complete spatial information in the reference pose. The method based on posture guidance can more accurately eliminate the interference of background, occlusion and perspective factors. To further explore the potential relationship between local regions, this paper calculates the relationship matrix between the local regions based on the relationship model to further calculate the relationship weight, and the graph convolutional network based on the relationship weight learns the structural relationship feature of multi-scale regions. The input of the graph convolutional network is a local region divided by a multi-scale method, and the output is a pose-guided multi-scale structural relationship feature. The experimental results on three public datasets show that the proposed method performs favorably against state-of-the-art methods.

Internet Exchange Points (IXPs) interconnect heterogeneous networks and transfer substantial traffic volumes. In the past decade, the number of IXPs has seen tremendous growth, with more operators connecting to these IXPs even though these IXPs faced various inter-domain routing limitations. Routers based on Border Gateway Protocol (BGP) forwards packets only based on destination IP prefix and selects only routes learned from their neighbors. IXPs designed using Software-Defined Network (SDN), called SDX, offer solutions for existing inter-domain routing problems. This paper presents the existing scalability limitations of inter-domain routing at IXP and how traditional IXP structural design can be transformed into a highly scalable SDX design by exploiting the SDN platform’s functionalities in different use cases of SDX. The paper then reviewed how the SDX improved various IXP operators’ scalability by reviewing and analyzing the latest SDX models and approaches, which provide enhanced policies to enhance providers’ management operations and offer good quality of services (QoS) to the various participating members. Finally, we discussed the open issues and challenges in this area that need further study and a solution to tackle them.

Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks.

In recent years, deep learning has been increasingly used in the field of multimodal emotion recognition in conjunction with electroencephalogram. Considering the complexity of recording electroencephalogram signals, some researchers have applied deep learning to find new features for emotion recognition. In previous studies, convolutional neural network model was used to automatically extract features and complete emotion recognition, and certain results were obtained. However, the extraction of hierarchical features with convolutional neural network for multimodal emotion recognition remains unexplored. Therefore, this paper proposes a hierarchical fusion convolutional neural network model to mine the potential information in the data by constructing different network hierarchical structures, extracting multiscale features, and using feature-level fusion to fuse the global features formed by combining weights with manually extracted statistical features to form the final feature vector. This paper conducts binary classification experiments on the valence and arousal dimensions of the DEAP and MAHNOB-HCI data sets to evaluate the performance of the proposed model. The results show that the model proposed in this paper can achieve accuracies of 84.71% and 89.00% on the two corresponding data sets, indicating that the model proposed in this paper is superior to other deep learning emotion classification models in feature extraction and fusion.

Machine learning (ML) is becoming an integral part of networks security arsenal, where Internet of Things (IoT) structures play an increasingly important role. However, IoT networks have many specific requirements, mostly due to limited energy availability and stringent computing resources. This results in limitations for traditional ML approaches to security, in particular for anomaly detection. Consequently, new focuses for solutions that range from architectural to data processing ones are necessary. Therefore, appropriate lightweight ML algorithms have to be designed and deployed in appropriate architectural settings, which is the main contribution of this paper. In addition, insights into ML functioning are needed to better understand the observed anomalies. To enable these insights (and support a wider applicability of ML based approaches), the results have to be as explainable as possible. The research presented in this paper addresses this problem through the functional and data transparency of ML applications, tailored to the specifics of anomaly detection in IoT networks. To tackle accordingly also the architectural issues, the presented approach builds on the well-established layering principle from computer communications reference models. This principle not only supports flexibility but also increases security in these new environments of growing importance.

The popularity and usage of Cloud computing is increasing rapidly. Several companies are investing in this field either for their own use or to provide it as a service for others. One of the results of Cloud development is the emergence of various security problems for both industry and consumer. One of the ways to secure Cloud is by using Machine Learning (ML). ML techniques have been used in various ways to prevent or detect attacks and security gaps on the Cloud. In this paper, we provide a Systematic Literature Review (SLR) of ML and Cloud security methodologies and techniques. We analyzed 63 relevant studies and the results of the SLR are categorized into three main research areas: (i) the different types of Cloud security threats, (ii) ML techniques used, and (iii) the performance outcomes. We have defined 11 Cloud security areas. Moreover, distributed denial-of-service (DDoS) and data privacy are the most common Cloud security areas, with a 16% level of use and 14%respectively. On the other hand, we found 30 ML techniques used, some used hybrid and others as standalone. The most popular ML used is SVM in both hybrid and standalone models. Furthermore, 60% of the papers compared their models with other models to prove the efficiency of their proposed model. Moreover, 13 different evaluation metrics were enumerated. The most applied metric is true positive rate and least used is training time. Lastly, from 20 datasets found, KDD and KDD CUP'99 are the most used among relevant studies.

The new “status quo” created by the emerging integration of Multi-Access Edge Computing (MEC) and Radio Access Network (RAN) infrastructures, combined with the openness of the 5G service market, have set new standards on how the multitude of 5G service domains should be incentivized and coordinated to comply with the performance requirements set on a per session basis. In this paper, we revisit the predominant offline contract-driven mobile data access model enabling users to gain access to the infrastructure of only a few network operators and propose a new blockchain-backed mobile data access model where the key 5G stakeholders can trade, share and consume mobile edge network assets (access to the internet, spectrum, processing, storage, local content etc.) in a fully decentralized, anonymous and highly-robust fashion. Blockchain-backed mobile data access should address critical practical challenges of blockchain decentralization, scalability and security in the context of 5G and Beyond networks. Accordingly, we provide a meticulous survey of existing solutions in the aforementioned three areas and present a comprehensive holistic framework of protocols to address the key challenges identified, including a delegated Proof-of-Stake (DPoS) for distributed blockchain consensus over 5G and Beyond mobile data networks, a future-proof smart-contract enabled payment relay service enabling instant off-chain payments in a highly robust fashion as well as a hybrid mixing protocol that enables payment relays to act as anonymous payment hubs while addressing the unique challenges opposed by the joint blockchain and network level interaction of the 5G and Beyond service components. A wide range of practical implications and mitigation measures for dishonest operation of the blockchain nodes are investigated and sophisticated yet highly-robust incentive engineering mechanisms are derived. Detailed numerical results also accompany the paper, bringing to light the unique performance trade-offs and valuable design guidelines towards blockchain-backed mobile data access for 5G and Beyond mobile data networks.

Blockchain technology is becoming increasingly attractive to the next generation, as it is uniquely suited to the information era. Blockchain technology can also be applied to the Internet of Things (IoT). The advancement of IoT technology in various domains has led to substantial progress in distributed systems. Blockchain concept requires a decentralized data management system for storing and sharing the data and transactions in the network. This paper discusses the blockchain concept and relevant factors that provide a detailed analysis of potential security attacks and presents existing solutions that can be deployed as countermeasures to such attacks. This paper also includes blockchain security enhancement solutions by summarizing key points that can be exploited to develop various blockchain systems and security tools that counter security vulnerabilities. Finally, the paper discusses open issues relating to and future research directions of blockchain-IoT systems.

This paper presents an in-depth study and exploration of the health IoT architecture and related implementation technologies from both theoretical and practical aspects, with important theoretical significance and practical application value. The research includes cloud fusion health IoT architecture, multimodal information acquisition in health IoT perception layer, multi-level service quality assurance of health IoT based on human LAN, and emotional perception and emotional interaction in health IoT. In terms of health IoT architecture, the cloud convergence health IoT architecture is proposed to deeply integrate the health cloud platform and perception layer by integrating multiple communication technologies to optimize the user experience and make health IoT applications more closely connected with people. This paper describes the basic concepts and main components of multimodal sensing information collection, the design and implementation of a health monitoring cloud robotics platform, robotics-based multimodal data sensing and aggregation, and high comfort sustainable physiological signal collection based on smart clothes. The feasibility and performance of the QoS framework proposed in this paper are verified by computer simulations. In this paper, migration learning is used to implement emotion data labeling, continuous conditional random fields to identify emotions based on data collected from smartphones and smart clothes, respectively, and finally decision layer fusion for emotion classification prediction.

With the deepening of the global economic community, various emergencies emerge in endlessly, and the risks gradually increase. People’s lives and property are threatened, which also causes a great burden on the social economy. Hitherto unknown novel coronavirus events occurred in China after the outbreak of the new coronavirus in 2019. The emergency management system is not perfect, so we start to study and improve the deficiencies of the emergency management system, but it is still difficult to effectively prevent and deal with all kinds of sudden and frequent social problems. Therefore, this paper puts forward the research of intelligent evaluation system of government emergency management based on BP neural network. In this paper, an intelligent evaluation system of government emergency management based on Internet of things environment is established, and then the system is deepened by BP neural network algorithm to avoid the interference of human factors. An objective intelligent evaluation system of government emergency management is constructed and verified by an example. We applied the system in a province, and proved that the system has strong executive ability, outstanding big data computing ability, and can objectively evaluate and analyze the government emergency management. The operability and accuracy of the intelligent evaluation system are verified. The effective evaluation content provides a new idea and method for government emergency management. And then continuously improve the emergency management measures to achieve the effect of dealing with things smoothly without panic.

With the increasing and diversified Internet of Things (IoT) devices, more IoT heterogeneous wireless networks have emerged, providing more network services for IoT devices, especially mobile phones and other roaming devices. However, there are also some malicious users who use different means to attack the security of the network, so that more users begin to pay attention to the identity authentication and privacy protection of the Internet of Things. This paper designs an IoT node roaming authentication model, which is used to enhance the security authentication capability of the Internet of Things to roaming devices. In order to effectively prevent malicious nodes from connecting to the network, this paper proposes a roaming authentication protocol based on heterogeneous fusion mechanism (HFM-IoT). The authentication protocol uses the remote authentication server in the local and remote areas to perform interactive authentication on the roaming device, which increases the difficulty of attacking or infecting multiple network areas by malicious nodes. According to the security analysis, the protocol can protect against multiple network attacks, and it can be seen from the experimental simulation results that the protocol has lower energy burden and authentication delay.

The UAV is emerging as one of the greatest technology developments for rapid network coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific area according to a desired quality of service requirement and to enable various entities in the network to have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard, by considering a multiple-UAV network where each UAV is associated to its own controlling operator, this paper addresses two major challenges: the selection of the UAV for the desired quality of network coverage and the development of a distributed and autonomous real-time monitoring framework for the enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based auction mechanism to model the interaction between the business agent who is interested in outsourcing the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of theoretical analysis and efficacy of the proposed model.

Rapidly evolving, the Internet of Things imposes new challenges for the developers of wireless networks. Various critical infrastructure monitoring scenarios require fast and reliable alert delivery. In such systems, multiple sensors are entrusted to react to the same emergency event. Thus, it is enough to receive an alert message from any of these sensors. However, such a message shall be reliably delivered as soon as possible. The recently published Wi-Fi HaLow standard defines the Restricted Access Window (RAW) mechanism that coordinates transmissions of numerous devices. Thus, it can improve reliability and reduce delays. The paper is the first to study the usage of RAW in a scenario of emergency alerts, where the alert shall be received from at least one sensor. The paper presents an easy-to-calculate mathematical model of alert delivery with RAW. The model allows dynamic online reconfiguration of RAW parameters to select such parameters that minimize consumed channel timeshare while providing satisfactory reliability and delivery delay for an alert. Intensive performance evaluation shows that the RAW is fruitful for mission-critical data delivery in the considered scenario.

Understanding the peak time of popularity evolution can provide insights on recommendation systems and online advertising campaigns. Although popularity evolution has been largely studied, the problem of how to predict its peak time remains unexplored. Taking Twitter hashtags as case study, the goal of this study is to predict when popularity reaches the peak for Twitter hashtags, from the perspective of an online social network application, in the context of the Twitter social network. On the whole, this paper includes three research aspects. Firstly, this paper investigates how early popularity reaches its peaks. Then, it is found that popularity tends to peak in the early stage of its evolution. Secondly, this paper discusses when a peak time prediction should be triggered. Thirdly, this paper designs a multi-modal based deep learning method, where the state-of-art deep learning techniques, such as multi-modal embedding and attention mechanisms, are adopted. We find that in the early stage of popularity evolution, no matter which factor is used as the input, the prediction effect is poor. By contrast, the hashtag string factor has the weakest contribution to popularity prediction in the middle and late stages of popularity evolution. The overall performance of our proposed method is evaluated in terms of the minimum, quartiles, and maximum values of absolute errors. From the experimental results, the prediction method we designed is superior.

The Internet of Things is made of diverse networked things (i.e., smart, intelligent devices) that are consistently interconnected, producing meaningful data across the network without human interaction. Nowadays, the Healthcare system is widely interconnected with IoT environments to facilitate the best possible patient monitoring, efficient diagnosis, and timely operate with existing diseases towards the patients. Concerning the security and privacy of the patient's information. This paper is focused on Secure surveillance mechanism for a medical healthcare system with enabled internet of Things (sensors) by intelligently recorded video summary into the server and keyframes image encryption. This paper is twofold. Firstly, a well-organized keyframe extraction mechanism is called to extract meaningful image frames (detected normal/abnormal activities keyframe) by the visual sensor with an alert sent to the concerned authority in the healthcare system. Secondly, the final decision about the happened activity with extracted keyframes to keep highly secure from any adversary, and we propose an efficient probabilistic and lightweight encryption algorithm. Our proposed mechanism verifies effectiveness through producing results, robustness in nature, minimum execution time, and comparatively secure than other images (keyframes) encryption algorithms. Additionally, this mechanism can reduce storage, bandwidth, required transmission cost, and timely analysis of happened activity from any adversary with protecting the privacy of the patient's information in the IoT enabled healthcare system.

The blooming of cloud computing leads to the rapid expansion of data center networks (DCN). Conventional link state routing algorithms like OSPF are widely adopted in data center networks, however, those routing algorithms bring great control overheads and long convergence time. Recently, topology-aware routing methods are considered to be efficient especially in data center networks with regular topologies. Lots of topology-aware routing methods (e.g., Fat-Tree and BCube) have been proposed for specific data center network topologies. This paper first proposes a formalized method to describe regular topologies and a regular Topology Description Language (TPDL) based on this method. TPDL is well designed to accurately define regular network topologies in a clear way leveraging their regularities. Based on the Software-Defined Networking (SDN) technology, this paper also proposes a novel topology-aware routing scheme: cRetor (controller-side REgular TOpology Routing scheme). Different from other topology-aware routing methods, cRetor is a TPDL-based general routing method, which means it is expected to work on different kinds of regular topologies. In this scheme, TDPL files are used as a priori knowledge to build an initial topology in the SDN controllers, which eliminates the process of topology discovery via Link Layer Discovery Protocol (LLDP) and hence relieves the bandwidth and processing burdens on controllers. Besides, we also apply the A-star algorithm to SDN controllers to speed up the routing selection, where TPDL's distance formulas act as the heuristic function. The experimental results show that cRetor outperforms LLDP-based SDN, OSPF and DCell in routing calculation performance, convergence speed, routing overheads and fault tolerance.

Power electronic matrix converters are converters carrying out just one solitary transitional stage. AC-AC conversion automatism in matrix converters is attained through the exploitation of bidirectional controllable power switches associated with a structural matrix. The indirect matrix converter is an alternating preference to the direct matrix converter embodied by AC-DC converter and DC-AC inverter. In this paper, an assemblage of Pulse Width Modulation categories is used to control the dual stages of IMC. AC-DC converter is modulated through the well-known space vector algorithm, while DC-AC inverter is modulated through a novel algorithmic conception referred to as the digital scalar algorithm. Comparative analysis between the conventional DSVPWM modulation and the novel proposed modulation technique is escorted demonstrating that the latter is facilely comprehended and simply put into real-time practice. This paper comes forward with digital signal processor real-time enforcement, conceived for indirect matrix converter. Uniting both original approaches into one topological system entrained a significant decrease of 38% in real-time computation assesses. Throughout the paper, it is well proven that the proposed solution results in high-quality input/output waveforms, and averts from servicing supplementary devices that trigger silicon devices' gates. Numerous experimental enquires within the delivered research reveal advantageous aspects of the contemporary approach from overall simplicity to entire effectiveness.

With the maturity of technologies such as Internet of Things (IoT) and Cloud Computing, more and more medical institutions share patient's medical data in the IoT-connected healthcare system, whereas it also brings hidden dangers to data security and privacy protection. The application of Hierarchical Attribute-Based Encryption (HABE) in IoT-connected healthcare system helps to solve the problem of data sharing with a large number of users. Delegation is a function of HABE, which can realize the transfer of hierarchical user access rights, effectively reducing the workload of the trusted authority. However, when “delegation” is used to generate decryption keys for users who do not comply with the access structure, the “key-delegation abuse” problem arises, which can seriously damage the privacy of patient's data. Nevertheless, it has not attracted much attention in previous research. This paper proposes the problem of key-delegation abuse in Ciphertext-Policy Hierarchical Attribute-Based Encryption (CP-HABE). After the cryptanalysis of typical schemes, we conclude two reasons for key-delegation abuse in CP-HABE, which are randomizing the original key elements or keeping parts of them in a new decryption key. This paper proposes a new mechanism specifically by using directed graph and construct a CP-HABE scheme against key-delegation abuse (CP-HABE-AKDA). Aiming at the scenario of key leaking, we further present the traceable CP-HABE-AKDA solution for the IoT-connected healthcare system, which has the additional function of tracking and verifying the identity of key leaker.

The goal of video super-resolution technique is to address the problem of effectively restoring high-resolution (HR) videos from low-resolution (LR) ones. Previous methods commonly used optical flow to perform frame alignment and designed a framework from the perspective of space and time. However, inaccurate optical flow estimation may occur easily which leads to inferior restoration effects. In addition, how to effectively fuse the features of various video frames remains a challenging problem. In this paper, we propose a Local-Global Fusion Network (LGFN) to solve the above issues from a novel viewpoint. As an alternative to optical flow, deformable convolutions (DCs) with decreased multi-dilation convolution units (DMDCUs) are applied for efficient implicit alignment. Moreover, a structure with two branches, consisting of a Local Fusion Module (LFM) and a Global Fusion Module (GFM), is proposed to combine information from two different aspects. Specifically, LFM focuses on the relationship between adjacent frames and maintains the temporal consistency while GFM attempts to take advantage of all related features globally with a video shuffle strategy. Benefiting from our advanced network, experimental results on several datasets demonstrate that our LGFN can not only achieve comparative performance with state-of-the-art methods but also possess reliable ability on restoring a variety of video frames. The results on benchmark datasets of our LGFN are presented on https://github.com/BIOINSu/LGFN and the source code will be released as soon as the paper is accepted.

How much information can be transported, i.e., the transmission rate, is a subject of great interest in hybrid wireless networks. The main focus of this paper is the effect of channel fading in hybrid wireless network, in which a wired network of base stations is deployed to support long-range communications between wireless nodes. Two types of transmission mode in hybrid wireless network, i.e., intra-cell mode and infrastructure mode, are considered. To effectively overcome fading impairment, optimal multiple access technique is applied, allowing opportunistic sources to transmit concurrently with the scheduled source. Those different sources, much like in wideband CDMA system, share the entire bandwidth. A successive interference cancellation (SIC) strategy is then introduced at receiver side to limit the intra-cell interference and achieve the maximum capacity. Meanwhile, frequency reuse scheme is employed to minimize the inter-cell interference. Since the outage capacity over different fading channels will exhibit different asymptotic behaviors, in this paper we examine the Rayleigh, Rician and Nakagami-m models, which are the most commonly used fading models. Close-form solutions for outage throughput capacity at high signal-to-noise-plus-interference ratio (SNIR) are derived. It is showed that, with opportunistic sources, the intra-cell mode effectively combats fading as wireless nodes increases; however, the infrastructure mode is bottlenecked by the downlink transmission since base station is the only transmitter in the cell during the downlink phase. The theoretical bounds obtained and proofs are instrumental to the future network modeling and design.

The rapid development of information technology has led to the development of medical informatization in the direction of intelligence. Medical health big data provides a basic data resource guarantee for medical service intelligence and smart healthcare. The classification of medical health big data is of great significance for the intelligentization of medical information. Due to the simplicity of KNN (K-Nearest Neighbor) classification algorithm, it has been widely used in many fields. However, when the sample size is large and the feature attributes are large, the efficiency of the KNN algorithm classification will be greatly reduced. This paper proposes an improved KNN algorithm and compares it with the traditional KNN algorithm. The classification is performed in the query instance neighborhood of the conventional KNN classifier, and weights are assigned to each class. The algorithm considers the class distribution around the query instance to ensure that the assigned weight does not adversely affect the outliers. Aiming at the shortcomings of traditional KNN algorithm in processing large data sets, this paper proposes an improved KNN algorithm based on cluster denoising and density cropping. The algorithm performs denoising processing by clustering, and improves the classification efficiency of KNN algorithm by speeding up the search speed of K-nearest neighbors, while maintaining the classification accuracy of KNN algorithm. The experimental results show that the proposed algorithm can effectively improve the classification efficiency of KNN algorithm in processing large data sets, and maintain the classification accuracy of KNN algorithm well, and has good classification performance.

The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc. have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird’s eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.

Nature-inspired optimization is a modern technique in the past decades. Researchers report their successful applications in various fields such as manufacturing, biomedical, and environmental engineering, while other researchers doubt its applicability. In this paper, we collect newly emerging nature-inspired optimization algorithms proposed after 2008, present them in a unified way, implement them, and evaluate them on benchmark functions. Moreover, we optimize the behavioural parameters for these algorithms. Since it is impossible to cover all interesting topics regarding nature-inspired optimization, this paper only focuses on the continuous encoding algorithms for single objective global problems, which is fundamental for other related topics.

Recently, radar high-resolution range profile (HRRP) recognition based on convolutional neural networks (CNNs) has received considerable attention due to its robustness to translation and amplitude changes. Most of the existing methods require that sufficient labeled data with complete aspect angles be used as training data, which is a difficult task in practice. In addition, HRRP signals have a high sensitivity to the aspect angle. Therefore, the representative and discriminative powers of the features extracted from typical CNN models are reduced due to incomplete aspect angles in the training data, which significantly limit the recognition performance. This paper first considers the problem of HRRP recognition with incomplete aspect angle training data and addresses the problem by a deep transfer learning framework. Specifically, the two proposed methods enhance the recognition performance by exploring the discriminative power and the intraclass consistency with auxiliary data, which have HRRP signals with complete aspect angles. This paper generates a simulated HRRP dataset from public data to validate the proposed work. The comparisons of the recognition results demonstrate that the proposed framework outperforms the latest CNN-based models.

Traditional image classification technology has become increasingly unable to meet the changing needs of the era of big data. With the open source use of a large number of marked databases and the development and promotion of computers with high performance, deep learning has moved from theory to practice and has been widely used in image classification. This paper takes big data image classification as the research object, selects distributed deep learning tools based on Spark cluster platform, and studies the image classification algorithm based on distributed deep learning. Aiming at the problems that the Labeled Structural Deep Network Embedding (LSDNE) model is applied to the attribute network and generates a large number of hyperparameters and the model complexity is too high, inspired by the Locally Linear Embedding (LLE) algorithm, this paper proposes a semi-supervised network based on the neighbor structure learning model. This model will add the neighbor information of the node at the same time when learning the network representation. Through the node vector reconstruction, the node itself and the neighbor node together constitute the next layer of representation. On the basis of Structural Labeled Locally Deep Nonlinear Embedding (SLLDNE), the node attribute is further added to propose Structural Informed Locally Distributed Deep Nonlinear Embedding (SILDDNE), and how the model combines the structural characteristics of the node with the attribute characteristics is explained in detail. The SVM classifier classifies the known labels, and SILDDNE fuses the network structure, labels, and node attributes into the deep neural network. The experimental results on the CIFAR-10 and CIFAR-100 datasets for image classification standard recognition tasks show that the proposed network achieves good classification performance and has a high generalization ability. Experiments on the CIFAR-10 data set show that the 34-layer SLLDNE pruned 40-layer Dense Net compresses about 50% of the parameter amount, increases the computational complexity efficiency by about 8 times, and reduces the classification error rate by 30%. Experiments on the CIFAR-100 data set show that the 34-layer SLLDNE parameter volume is compressed by about 16 times compared to the 19-layer VGG parameter volume, the computational complexity efficiency is increased by about 6 times, and the classification error rate is reduced by 14%.

Under the explosive growth of information available on the Web, recommender systems have been used as an effective technology to filter useless information and attempt to recommend the most useful items. The proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices has gradually driven many novel emerging services which are latency-sensitive and computation-intensive with a higher quality-of-service. Under such circumstances, the data sources contain four key characteristics (i.e., sparsity, heterogeneity, mobility, volatility). The conventional recommender systems based on cloud computing are incapable of digging the information of user demands. Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service. This paper comprehensively reviews the state of the art literature on the convergence of recommender systems and edge computing, and identify the future directions along this dimension. This paper can provide an array of new perspectives on the convergence for researchers, practitioners, and tap into the richness of this interdisciplinary research area.

In view of the shortcomings of traditional fault diagnosis methods based on time domain vibration analysis, which require complicated calculations of feature vectors, and are over-dependent on a prior diagnosis knowledge, effort for the design of the feature extraction algorithms, and have limited ability to extract the complex relationships in fault signals, in this paper we propose a convolutional neural network (CNN) framework for machine health monitoring based on the encoding of one-dimension (1-D) time series to two-dimension (2-D) images. This paper defines a new Gram matrix and through the Python programming environment, we emulate the new Gram matrix in 2-D images, thus feature extraction and recognition can be performed by CNNs. The proposed method which is tested on two datasets, including multi-stage centrifugal fan dataset for our lab, motor bearing dataset for Case Western Reserve University, has achieved prediction average accuracy of 94.07% and 96.28%, respectively. The results have been compared with other deep learning and traditional methods, including Recurrent neural network (RNN), Support Vector Machines (SVM), Multi-Genetic algorithm, shallow CNN and BP neural network. The results show that the method can improve fault diagnosis accuracy in an effective way and stability than other advanced techniques.

Recently, to reduce the increasing energy consumption along with the explosive growth of smart terminals and network data in Internet of Things (IoT), green IoT is attracting more and more attention from both academia and industry. Towards green IoT, this paper devotes attention to Device-to-Device (D2D) communication and social network which are two essential components in green IoT and first reviews the latest developments about them. Further, this paper proposes a social interaction assisted resource sharing scheme for D2D communication, to improve the utilization of spectrum resources in green IoT. Specifically, the proposed scheme abstracts the D2D communication system into social layer and physical layer, by combining social network with D2D communication. Extensive simulations with real social interaction data are conducted and they show that the proposed scheme can achieve improvements in terms of the transmission success rate for green IoT.

The Web of Things (WoT) standard recently promoted by the W3C constitutes a promising approach to devise interoperable IoT systems able to cope with the heterogeneity of software platforms and devices. The WoT architecture envisages interconnected IoT scenarios characterized by a multitude of Web Things (WTs) that interact according to well-defined software interfaces; at the same time, it assumes static allocations of WTs to hosting devices, and it does not cope with the intrinsic dynamicity of IoT environments in terms of time-varying network and computational loads. In this paper, we extend the WoT paradigm for cloud-edge continuum deployments, hence supporting dynamic orchestration and mobility of WTs among the available computational resources. Differently from state-of-art Mobile Edge Computing (MEC) approaches, we heavily exploit the W3C WoT, and specifically its capability to standardize the software interfaces of the WTs, in order to propose the concept of a Migratable WoT (M-WoT), in which WTs are seamlessly allocated to hosts according to their dynamic interactions. Three main contributions are proposed in this paper. First, we describe the architecture of the M-WoT framework, by focusing on the stateful migration of WTs and on the management of the WT handoff process. Second, we rigorously formulate the WT allocation as a multi-objective optimization problem, and propose a graph-based heuristic. Third, we describe a container-based implementation of M-WoT and a twofold evaluation, through which we assess the performance of the proposed migration policy in a distributed edge computing setup and in a real-world IoT monitoring scenario.

In this paper, we examine the performance of four authoritative DNS server implementations (BIND, NSD, Knot DNS, and YADIFA). In our tests, we apply the measurement procedure defined in Section 9 of RFC 8219. Our aim is threefold: to provide DNS operators with ready to use measurement results to support their selection of the best fitting authoritative DNS server implementation for their needs, to assist researchers and DNS64 server developers in finding a suitable authoritative DNS server implementation for their DNS64 benchmarking measurements, and to advance the theory and practice of benchmarking DNS servers. We examine how the different conditions such as the number of active CPU cores, the size of the zone file, the applied timeout, and the type of the processor influence the performance of the tested authoritative DNS server implementations. The performance of all four tested DNS servers scales up more or less well with the number of CPU cores, except for YADIFA. The increase of the size of the zone file causes significant degradation only in the performance of BIND, which shows different anomalies described in the paper. The change of the timeout from 250ms (required by RFC 8219) to 100ms usually causes only a small performance degradation. We point out that NSD and Knot DNS can achieve an order of magnitude higher performance than BIND and YADIFA.

As the Software Define Network (SDN) adopts centralized control logic, it is vulnerable to various types of Distributed Denial of Service (DDoS) attacks. At present, almost all the research work focuses on high-rate DDoS attack against the SDN control layer. Moreover, most of the existing detection methods are effective for high-rate DDoS attack detection of the control layer, while a low-rate DDoS attack against the SDN data layer is highly concealed, and the detection accuracy against this kind of attack is low. In order to improve the detection accuracy of the low-rate DDoS attack against the SDN data layer, this paper studies the mechanism of such attacks, and then proposes a multi-feature DDoS attack detection method based on Factorization Machine (FM). The features extracted from the flow rules are used to detect low-rate DDoS attacks, and the detection of low-rate DDoS attacks based on FM machine learning algorithms is implemented. The experimental results show that the method can effectively detect the low-rate DDoS attack against the SDN data layer, and the detection accuracy reaches 95.80 percent. Because FM algorithm can achieve fine-grained detection for low-rate DDoS attack, which provides a reliable condition for defending against such attacks. Finally, this paper proposes a defense method based on dynamic deletion of flow rules, and carries out experimental simulation and analysis to prove the effectiveness of the defense method, and the success rate of forwarding normal packets reached 97.85 percent.

The developments in wireless technology and applications in recent years have increased the interest in downlink scheduling and resource allocations among researchers. Moreover, fair scheduling and balanced Quality of Service (QoS) delivery for various forms of traffic are needed for Long-Term Evolution (LTE) wireless systems. This paper proposes hybrid QoS-aware downlink scheduling approaches that aim to address different traffic classes and balance the QoS delivery with improvements to the overall system performance under channel and bandwidth constraints. Moreover, this research introduces a taxonomy that classifies the scheduling algorithms into four main classes: delay aware, queue aware, target bit-rate aware and hybrid aware. The latter class is the scheduling class that is proposed in this paper; it considers channel, queue and delay parameters in its scheduling metric. Using simulations, we compare and analyze different downlink scheduling rules for their network-centric performance metrics, e.g., average packet loss ratio, average throughput, average packet delay, system fairness, and system spectral efficiency. The simulation results show that the queue-aware and delay-aware scheduling rules deliver the best QoS performance for video traffic classes, whereas our proposed hybrid scheduling rules deliver balanced QoS for various types of traffic classes. Employing QoS balancing scheduling rules in an LTE downlink is suggested to provide high QoS delivery for different traffic classes.

Knowledge-based authentication approaches such as the use of passwords and personal identification numbers (PINs) are the most common ways of authenticating users. The main problem with such approach is relying on simple authentication login credentials at the login stage, and assuming the user is still the same between access sessions makes applications and networks vulnerable to access by unauthorized users. Application-level access patterns on smartphone and tablet devices can be utilized to provide an approach for continuously authenticating and identifying users. This paper presents a user authentication and identification method based on mobile application access patterns, and throughout the paper we use a smart home environment as a motivating scenario. To enhance the classification process, many features have been extracted and utilized which considerably improved differentiating between users and eliminating similarities in the access usage patterns. The proposed model has been evaluated using two datasets, and the results show an ability to authenticate users with high accuracy in terms of low false positive, false negative, and equal error rates. Overall, the statistical analysis of the extracted multi-features and the results show that the feasibility of decision-making based on app interactions can lead to high accuracy.