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This paper presents the design and analysis of a paper-based chipless RFID sensor for wetness content measurement in plant leaves. The sensor is designed using a passive microwave resonator to measure the ambient physical parameter. The resonator is designed with a rectangular loop inductively coupled with an interdigital capacitor. The experimental analysis of the designed sensor is illustrated here to prove its efficacy. The resonator provides adequate backscattered signal for wireless measurement in the microwave frequency band. When interrogated with a couple of UWB antennas and a VNA, it yields accurate leaf wetness response of the plant. The commercial competence of this sensing device is validated through its experimental analysis on three different types of leaf samples. This sensor is suitable for plant health monitoring in any small-scale gardening applications such as nurseries and greenhouses.

This paper presents a thorough review of transmitarray devices particularly aiming antenna beamsteering, gathering some of the most relevant solutions published by the scientific community in the field. First, the background for realizing 1-D and 2-D antenna beamsteering with a transmitarray is introduced. Subsequently, several examples of unit-cells for transmitarray implementation and complete transmitarray designs presented in the literature are outlined. Each solution is analyzed in detail, identifying the nature of its layout, e.g., based on microstrip patches, frequency selective surfaces (FSS), or metamaterials (MMs), and the method employed to enable electronic reconfigurability, e.g., p-i-n diodes, varactor diodes, or microelectromechanical systems (MEMS). In addition, some models with the capability of controlling the wavefront polarization modes are also included herein since these are the base of hybrid transmitarrays, i.e., transmitarray with both electronic beamsteering and polarization control. Finally, all the models are compared against each other in order to highlight their benefits and limitations, summarizing their main characteristics, such as the frequency of operation and bandwidth, insertion loss, physical dimensions, and maximum beamsteering range, when available.

Recently, it has been shown that channel feedback helps to enhance the physical layer security (PLS) of communication systems. The current use of feedback focuses on generating secret keys protecting the transmitted messages, and this feedback scheme is proved to be optimal for some physically degraded channel models. In this paper, we revisit the model of the state-dependent wiretap channel with the noncausal state at the transmitter, which plays an important role in the PLS of the wireless communication systems. We propose a new feedback scheme for this model and show that the proposed new scheme performs better than the already existing secret key-based feedback scheme. The results of this paper are further explained via a Gaussian example.

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.

Nowadays, 5G is in its initial phase of commercialization. The 5G network will revolutionize the existing wireless network with its enhanced capabilities and novel features. 5G New Radio (5G NR), referred to as the global standardization of 5G, is presently under the $3^{\mathrm {rd}}$ Generation Partnership Project (3GPP) and can be operable over the wide range of frequency bands from less than 6GHz to mmWave (100GHz). 3GPP mainly focuses on the three major use cases of 5G NR that are comprised of Ultra-Reliable and Low Latency Communication (uRLLC), Massive Machine Type Communication (mMTC), Enhanced Mobile Broadband (eMBB). For meeting the targets of 5G NR, multiple features like scalable numerology, flexible spectrum, forward compatibility, and ultra-lean design are added as compared to the LTE systems. This paper presents a brief overview of the added features and key performance indicators of 5G NR. The issues related to the adaptation of higher modulation schemes and inter-RAT handover synchronization are well addressed in this paper. With the consideration of these challenges, a next-generation wireless communication architecture is proposed. The architecture acts as the platform for migration towards beyond 5G/6G networks. Along with this, various technologies and applications of 6G networks are also overviewed in this paper. 6G network will incorporate Artificial intelligence (AI) based services, edge computing, quantum computing, optical wireless communication, hybrid access, and tactile services. For enabling these diverse services, a virtualized network slicing based architecture of 6G is proposed. Various ongoing projects on 6G and its technologies are also listed in this paper.

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.

The proposed approach uses blockchain-based technology to strengthen the data security of wireless sensor networks (WSNs). This paper integrates blockchain-based technology with data transfer to establish an extremely secure WSNs structure. The present wireless network is built on the architecture of the Internet of Things (IoT) and employs a blockchain-based method to make the reliability of data transmission strong. In this proposed research, many small-area wireless sensor networks establish the entire WSNs structure, and every small-area wireless sensor network has a primary data collection node called a “mobile database.” The “mobile database” node of this study uses embedded microcontrollers with an operating system, such as Raspberry Pi and Arduino Yun. This block contains the sensor data collected by itself and the hash value of the previous block. Then the hash value of its own block, which is also part of the hash calculation of the next block, was calculated through the mining calculation program. Any block in the proposed method includes the encrypted hash-value of the previous block, the current timestamp, and the transaction data. In our research content, the transaction data is represented as wireless network sensing data. Basically, the system employs the hash function for calculation using the Merkel-tree algorithm. Such programming makes the block with blockchain-based technology difficult to tamper with content. This study approach revises the blockchain-based transaction ledger to become a sensor data record. Therefore, the proposed system gathers and analyzes sensor data for more reliability in the wireless sensing network structure. Furthermore, the innovative system with blockchain-based technology can treat a private cloud-end. This paper also carries on to visualize the uploaded sensing data by the sensors and draws corresponding charts based on big data analysis. The wireless network architecture proposed in this paper is built on embedded devices, making it easy for the system to build a web server. Using Python or JavaScript programming language in the web environment is relatively more convenient for data visualization and data analysis. Finally, this study uses traditional methods and innovative methods to compare data transmission. When the system uses innovative methods with blockchain-based technology, it is almost impossible for any operator to tamper with the data transmitted by the sensor.

The simple rules that govern the interactions between the different components of a complex system often lead to interesting behaviours that are unexpected. The experiments described in this paper involved creating a variation of a traditional Braitenberg Vehicle, by placing sensors on ten different possible locations on a simulated vehicle, and incorporating an obstacle avoidance behaviour using a subsumption-like architecture, which resulted in different unusual and unexpected behaviours being produced. The vehicle was allowed to explore a simulated environment which contained a single bright light in the centre with walls on the border. By using a novel combination of the Prediction by Partial Matching compression algorithm and ${k}$ -means clustering, interesting emergent behaviours were effectively discovered within a search space of over 10,000 simulations produced from a simple interaction of light and proximity sensors on a vehicle and a single light source. The clustering algorithm discovered five distinct behaviours: circling and spiralling behaviours; interesting behaviours creating intricate rose petal-like structures; behaviours that create simple rose petal-like structures; behaviours with large movements and low complexity; and behaviours with less movement. The novel algorithm demonstrated in this paper has useful potential in the science of complex systems and modelling to help expedite the systematic exploration of a substantial search space of simulations in order to discover interesting behaviours.

Timing synchronization has a vital role in swarm drones’ network (SDN) or a swarm of unmanned aerial vehicle (UAV) network. Current timing synchronization methods focus on enhancing single-hop skews which remarkably improve timing synchronization precision at this level. The improper clock of the drone system can cause interference, affect spectrum precision and interrupt the operation of the transceiver. In the drones’ network, master drones’ (MD) neighbor drone’s timing synchronization approaches like Reference Broadcast System (RBS) realize a good performance. However, the requirement of one super drone with a large number of broadcasts for RBS makes it unrealistic to use in some situations like SDN network situation. Appropriate study and adjustments are needed to have real timing synchronization by eliminating the clocks drift and enhancing the timing synchronization precision. Therefore, a new self-timing synchronization approach is proposed in this paper where several MD drones can autonomously generate swarm clusters. The cluster head (CH) instigates a timing synchronization procedure starting with intra-Swarm cluster timing synchronization. The intermediate drones (ID) are elected between two swarm clusters to synchronize all drones in line with the inter-swarm cluster timing synchronization approach. The proposed approach is distributed and flexible to achieve high timing synchronization precision. The paper proposes a novel self-timing synchronization approach for in large scale semi-flat SND network architecture. Self-timing synchronization is swarm cluster-based and applicable for a huge number of master drones in SDN. One is the intra-Swarm cluster where the timing synchronization procedure starts with the CH to synchronize all CM. Secondly, in the inter-swarm cluster timing synchronization, two clusters are synchronized via intermediate drone (ID). However, the simulations demonstrated that in many cases all CHs are synchronized by the synchronized CHs from intra-swarm cluster timing synchronizations; this increased the system throughput and synchronization delay to about 75% compared to what we planned to achieve. Moreover, the simulation results also proved that the achieved synchronization precision can be used for position estimation and prediction with high accuracy.

The Internet of Things is based on a communication network to automatically receive various information from the natural world. It uses intelligent objects with perception, communication and computers to automatically receive various information from nature. All independently managed physical objects are connected to each other to achieve integrated perception, reliable transmission and intelligent processing, and to establish intelligent information between people and objects, and objects and object service systems. This article mainly introduces the impact of industrial agglomeration on the regional economy in a simulated intelligent environment based on machine learning. This paper proposes a method to detect industrial agglomeration index to analyze industrial agglomeration. Through the establishment of the industrial agglomeration index system, the level of integration of the manufacturing industry in our city was objectively analyzed, and the impact of the integration of manufacturing industry on our city was tested empirically. Finally, the relationship between industrial integration and regional economic development was tested. The experimental results in this paper show that industrial integration in a smart environment based on simulation learning has a significant positive impact on regional economic development. Among them, the level of cooperation between the manufacturing service industry and the manufacturing industry increased by 1%, and the level of regional economic development increased by 0.025%.

PnP problem is well researched in many fields, such as computer vision. It is considered the fundamental method to solve the key problems of robot SLAM. However, in pedestrian visual localization, uncalibrated PnP (UPnP), specifically PnP with unknown focal length (PnPf) is more suitable for solving the problem. Recently, a few researchers proposed some methods to alleviate this problem. However, the localization accuracy of the existing methods is not satisfied when image pixel noise is larger. In other words, RANSAC should be running before solving the PnPf problem to get less noisy input, which means the localization delay increases inevitably. In this paper, we propose a more robust method for solving the PnPf problem without the help of RANSAC based on Gröbner basis and convex optimization. We build a Gröbner basis solver on the offline stage with one instance in the prime field. Then, we substitute the coefficients with real value and find multiple solutions on the online stage. Finally, we construct a convex optimization program to seek the final robust solution to PnPf problem. The other purpose of this paper is to provide a second-level localization experience for the end-user. The simulation result shows that our method can give a localization solution, which is more reliable than benchmark methods by both synthetic and real data verified.

In this paper, the secrecy performance of the physical layer security (PLS) of the hybrid free-space optical/radio frequency (FSO/RF) communication system is analyzed. The transmission protocol of the considered system is performed under the eavesdropper's attempt to overhear the RF link between the transmitter and legitimate receiver of the hybrid system. The FSO link is characterized by Málaga-M distribution while the RF links are modeled by Nakagami-m distribution. The two practical eavesdropping modes considered in this paper include: colluding and non-colluding. Exact closed-form expressions for the system's secrecy outage probability (SOP), the asymptotic of the SOP (SOP∞), the probability of strictly positive secrecy capacity (SPSC), the average secrecy capacity (ASC), and the asymptotic of the ASC (ASC∞), are specifically derived under the influence of both eavesdropper modes. Our derived analytical expressions present an efficient tool to investigate the impact of some channel parameters on the secrecy performance, namely the fading severity of the RF links, atmospheric turbulence severity, pointing error of the FSO link, number of eavesdroppers, and the power of the eavesdropper links. The results show that the increase of the eavesdroppers' number under both modes profoundly degrades the considered system secrecy performance. The accuracy of the numerical results obtained is validated by Monte-Carlo simulations.

Small cells (SCs) are expected to be ultra-densely deployed in or close to the traffic hot-spots in the fifth generation (5G) mobile networks to provide wireless capacity cost-effectively. Traffic hot-spots change over time, which means SCs cannot be deployed in a one-off manner as macrocells normally do, rather they should be constructed in a staged process. Hence, mathematical models that capture the time-varying staged-construction process, are urgently needed for operators to effectively predict the construction period, but are currently lacking. In this paper, inspired by the foam bursting process-a natural phenomenon that can be observed in daily life such as hand-washing, we first propose a novel model that can predict the time-varying expectation and logarithmic variance of SC coverage areas. Then, we verify the model by real network deployment cases. Additionally, in order to extract parameters from historical base station deployment data, a parameter estimation algorithm is designed and verified. The findings of the paper reveal that mobile operators should construct ultra-dense SC networks in a staged manner like how larger foams split into smaller ones.

Video monitoring is an important means to ensure production safety in coal mine. However, the currently intelligent video surveillance is difficult to respond in real-time due to the latency of cloud computing. In this paper, a cloud-edge cooperation framework is proposed, which integrates cloud computing and edge computing in a coordinated manner. The cloud computing is used to process non-real-time and global tasks, while the edge computing is responsible for handling local monitoring video in real-time. In order to realize cloud-edge data interaction and online optimization for edge models, the heterogeneous converged network is built. In addition, an object detection model FL-YOLO composed of depthwise separable convolution and down-sampling inverted residual block is proposed, which realizes real-time video analysis at the edge. Finally, this paper discusses the complexity of FL-YOLO by its computational cost and model size. The experiment results show that the model size of FL-YOLO is 16.1MB, which is very light, and it achieves 36.7 FPS on NVIDIA Jetson TX1 and an AP of 76.7% on Multi-scene pedestrian dataset. Comparing with mainstream object detection models, FL-YOLO completes faster detection speed and higher accuracy, and it has lower calculation complexity and smaller model scale. Furthermore, the AP on Single-scene pedestrian dataset of FL-YOLO is improved to 80.7% by cloud-edge cooperation. K-Fold method is also used to further compared the performance of FL-YOLO and other models. Moreover, system test is implemented on coal mine, which validates the actual engineering effect of the proposed cloud-edge cooperation framework.

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.

In this paper, we consider the problem of accurate force control using actuated winches, intended for use in real-time hybrid hydrodynamic model testing. The paper is also relevant to other cable-driven parallel robot applications that use force control in an inner control loop. For this problem, conventional strategies typically use actuated winches with torque-controlled servomotors directly connected to the cabled drum. In contrast, we propose using actuated winches with position-controlled servomotors that connect to the cabled drum via a clockspring. The servomotors are position controlled at drive-level and are rapid, accurate, robust, and simple to install. We show how this, combined with an accurate estimate of the clockspring deflection and stiffness, can yield fast and precise force tracking on moving objects. This includes proposing associated feedforward force-controllers that compensates for damping, angle-dependent force variations, delays, and non-constant clockspring characteristics. Extensive experimental testing on a 1 degree of freedom actuated mass-spring system supports the work. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Photo Response Non-Uniformity (PRNU) is considered the most effective trace for the image source attribution task. Its uniqueness ensures that the sensor pattern noises extracted from different cameras are strongly uncorrelated, even when they belong to the same camera model. However, with the advent of computational photography, most recent devices heavily process the acquired pixels, possibly introducing non-unique artifacts that may reduce PRNU noise's distinctiveness, especially when several exemplars of the same device model are involved in the analysis. Considering that PRNU is an image forensic technology that finds actual and wide use by law enforcement agencies worldwide, it is essential to keep validating such technology on recent devices as they appear. In this paper, we perform an extensive testing campaign on over 33.000 Flickr images belonging to 45 smartphone and 25 DSLR camera models released recently to determine how widespread the issue is and which is the plausible cause. Experiments highlight that most brands, like Samsung, Huawei, Canon, Nikon, Fujifilm, Sigma, and Leica, are strongly affected by this issue. We show that the primary cause of high false alarm rates cannot be directly related to specific camera models, firmware, nor image contents. It is evident that the effectiveness of \prnu based source identification on the most recent devices must be reconsidered in light of these results. Therefore, this paper is intended as a call to action for the scientific community rather than a complete treatment of the subject. Moreover, we believe publishing these data is important to raise awareness about a possible issue with PRNU reliability in the law enforcement world., Comment: Final paper in : https://ieeexplore.ieee.org/document/9393356

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.

This paper considers the problem of estimating the motion parameters of a low-observable high-speed maneuvering target with high-order motions, where the target’s complex motions will result in the range migration (RM) and Doppler frequency migration (DFM) effects within the observation time interval, which will significantly affect the radar performance. Based on the Hough transform (HT) and the polynomial Chirplet transform (PCT), a novel method for the motion parameter estimation, which we coin as HPCT, is proposed in this paper. It first employs the trajectory feature extracted from the image of range profiles by the HT to correct the first-order RM and resolve the Doppler ambiguity. Then, it compensates the residual range curvature and the DFM with the motion parameters estimated from the time-frequency ridge, which is extracted from the highly concentrated time-frequency representation (TFR) generated by the PCT. After that, the energy of radar returns can be coherently integrated along the target’s moving trajectory. In addition, we evaluate the parameter estimation performance, integration performance and computational cost of the proposed method via theoretical analysis or numerical experiments. The results show that in terms of the anti-noise ability, cross-term interference avoidance, measurement accuracy and computational complexity, the HPCT is superior to some common methods for the motion parameter estimation. Finally, we extend it to the scenario of multiple targets with different motion orders.

Due to the rapid development of wireless communication technologies, the number of wireless users are radically increasing. Currently, $\sim 23$ billion wireless devices are connected to the internet, and these numbers are expected to increase manifolds in the years to come. The technology growth of the fifth-generation (5G) wireless systems will be needed to meet this high demand of the network. 5G wireless systems offer data-rates of up to 10Gbps, 1-ms latency, and reduced power consumption. It is a known fact that 5G wireless systems will be exploiting beyond the presently used 3 GHz microwave and millimetre-wave (mm-wave) frequency bands. This is the primary driver in the development of the 5G wireless system. Multi-beam Phased array antenna (PAA) systems are typically used in the deployment of 5G systems for high-gain and directionality. In current 5G and future Beyond 5G (B5G) antenna array systems, beamforming networks (BFNs) such as the Butler Matrix (BM) will play a key role in achieving multi-beam characteristics. So, this paper presents an extensive review of the BM based BFNs, and discusses which type of BM will be suitable for the phased array antenna (PAA) systems in the upcoming 5G and next-generation of B5G wireless systems. Moreover, this paper also summarizes the different types of BM designs based on the number of layers. The BMs are classified into the bi-layer, tri-layer, and four-layer structures. It includes different techniques that have been used to solve the problem of crossing, narrow bandwidth, and size reduction of the BM. From the previous studies, it is found that most of the past research work was performed using the bi-layer BM system, whereas the difficult geometries like tri- and four-layer BM are avoided due to their complex fabrication process. It is also found in this paper that the metamaterial (MTM) based bi-layer BM achieves low insertion-loss and phase-error, excellent bandwidth and compact size, and good S-parameter performance, which makes them an ideal BFN candidate for the upcoming 5G and next-generation B5G systems.

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.

By caching multiple copies of the sensed data and suppressing repeated content deliveries from the sensor nodes, the in-network caching exhibits strong potentials to reduce energy consumption and avoid service loss in the Internet of Things (IoT). In this paper, we review the fundamentals of the in-network caching in IoT, especially the deployment principles of the cache-enabled brokers. Since in-network caching is not the optimal choice in some cases, we propose the dynamic in-network caching scheme to decide whether the sensed data should be cached in brokers or not, where the location and delay requirement of the clients, traffic load of the brokers as well as the energy level of the sensor nodes are encapsulated in the proposed scheme. Next, we give a practical case study to demonstrate the effectiveness of the proposed in-network caching scheme in the IoT scenario. It shows that the proposed caching scheme is superior to the traditional sensor-to-client scheme in terms of energy saving. Finally, we conclude this paper, and summarize the future research directions to achieve the in-network caching for implementing the green IoT.

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.

Massive multiple-input multiple-output (M-MIMO) is a significant pillar in fifth generation (5G) networks where a large number of antennas is deployed. It provides massive advantages to modern communication systems in data rate, spectral efficiency, number of users serviced simultaneously, energy efficiency, and quality of service (QoS). However, it requires advanced signal processing for data detection. The growing MIMO size leads to complicated scenarios, which makes the detector design a knotty problem. The problem is also becoming more complicated when high-order modulation schemes are exploited and more users are multiplexed. Therefore, it is not practical to employ the maximum likelihood (ML) detector despite the excellent performance. Linear detectors are alternative solutions and relatively simple. Unfortunately, they still need an exact matrix inversion computation, which bears to a significant high complexity. Therefore, several iterative methods are utilized to approximate or evade the matrix inversion rather than computing it. This paper studies the pros and cons of iterative matrix inversion methods where the number of computations and bit-error-rate (BER) are considered to compare between the methods. The comparison is conducted in several scenarios such as different ratio between the number of base station (BS) antennas and user terminal (UT) antennas ( $\beta $ ), the number of iterations ( ${n}$ ), and the relaxation parameter ( $\omega $ ). This paper also studies the impact of $\omega $ in the performance of Richardson (RI) and the successive over-relaxation (SOR) methods. Numerical results show that the conjugate gradient (CG) and optimized coordinate descent (OCD) methods exhibit the lowest complexity with an acceptable performance. In addition, the Gauss-Seidel (GS) method outperforms all other detectors with a trivial complexity increment. It is also noticed that the performance is not improved with every iteration. It is also shown that $\omega $ has a great impact and a significant role in achieving a satisfactory performance in both RI and SOR based detectors. From implementation point of view, detectors based on RI, OCD, and CG methods have achieved the highest hardware efficiency (HE) while Jacobi (JA) based detector has obtained the lowest HE. Recent research advances of detection methods are also presented in the open research direction with a potential impact of linear detection methods in initialization and pre-processing.

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.

The outboard corner reflectors are widely used as a countermeasure to radar tracking and detection. Therefore, it is essential to determine the composite time-domain scattering characteristics of the corner reflector and ship against the sea background and the jamming effectiveness of the corner reflector array. In this paper, the composite electromagnetic scattering characteristics of ship target, corner reflector, and the sea surface are studied based on the time-domain bouncing ray method (TDSBR). The radar echo and broadband radar cross section (RCS) results for the time domain pulse incident are also obtained and the high-resolution range profile (HRRP) is calculated. We then devise an evaluation system based on centroid jamming intensity and range profile similarity to determine the jamming characteristics of passive jamming. We further propose an array composing of three sizes of corner reflectors. Finally, the centroid jamming effectiveness is analyzed by comparing the RCS of the corner reflector array and the ship, and the deception jamming effectiveness is measured by calculating the range profile similarity based on the cosine similarity method and the range profile peak envelope overlap. Simulation results show that the reflector array can achieve a good performance both for centroid and range profile deception. The simulation results given and the evaluation method proposed in this paper provides significant practical insights into the design and analysis of the passive jamming systems.

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.

As the mobile networks become even denser and the traffic demand is increasing drastically, it is becoming more heterogeneous. Recently, it is intensive to investigate the Cell-Free Massive multiple-input multiple-output Massive (MIMO) system, where a large number of access points (APs) simultaneously serve a much smaller number of users, and the APs and users are clustered to provide good service for all users. This paper designs a mobile base station (MBS)-assisted Cell-Free Massive MIMO system: Utilizing an MBS deployed on the unmanned aerial vehicle (UAV) to form an air-ground heterogeneous system with the Cell-Free Massive MIMO system and offload part of traffic to the MBS to further improve the system performance. To provide better service to all users, this paper design an MBS flight trajectory optimal method to improve the wireless coverage performance and user experience, and proposed a joint power allocation algorithm based on the consideration of the fairness of the service quality. The simulation results indicate that the performance of the system designed in this paper has a significant improvement compared with the normal Cell-Free Massive MIMO system.

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.

In this paper, we investigate a multitone simultaneous wireless information and power transfer (SWIPT) based modulation scheme for battery-less Internet of Things (IoT) nodes that works in the ultra high frequency (UHF) region. The conventional SWIPT system is assumed to have power-consuming communication modules. Having such modules on to the IoT nodes whose power is harvested from radio frequency (RF) power sources is too unrealistic. In addition, waveform design from the aspect of power harvesting through SWIPT still has room for consideration. Recent studies have explored multitone based SWIPT to increase the power conversion efficiency (PCE). In these schemes, information is modulated by changing each tone’s nature or varying the number of tones. Among these methods, we focused on modulation schemes known as frequency-shift multitone based SWIPT, which shifts frequencies among the tones for information encoding. Unlike previously proposed methods where demodulation requires some power-consuming fast Fourier transfers (FFTs), especially under small communication bandwidths, we applied a signal detection method by measuring output peak to average power ratios (PAPRs) for frequency-shift multitone based SWIPT to reduce power consumption. Based on our analysis, different tone configurations in the frequency domain would yield varieties of nonlinear outputs during the rectification process. In addition, these specific nonlinear output patterns depend on the tone configurations. With this feature, it is possible to demodulate in the time domain at the receiver side using PAPR based measurements, which could eliminate FFT operation. This paper describes how measuring PAPRs enables the detection of signals in theory and validates this through simulations and experiments. We also estimate the communication rates. Based on our results, we achieved 0.46 bits/s/Hz when the number of tones was 6 and estimated that there were $(N-1)(N-2)/2 + 1$ different PAPRs from a given multitone waveform whose number of tones was $N$ .

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.

Publisher's version (útgefin grein), Multiple-input multiple-output (MIMO) antennas are considered to be the key components of fifth generation (5G) mobile communications. One of the challenges pertinent to the design of highly integrated MIMO structures is to minimize the mutual coupling among the antenna elements. The latter arises from two sources, the coupling in the free space and the coupling currents propagating on a ground plane. In this paper, an array of H-shaped parasitic patches is proposed as a decoupling structure for compact MIMO antennas to reduce propagation of the coupling currents on a shared ground plane. The proposed decoupling structure is generic, and it can be applied to different antenna configurations as demonstrated in the work. Furthermore, it is employed to develop a new high-performance compact dual-band MIMO structure featuring acceptable level of element coupling at both operating frequencies. The design is validated both numerically and experimentally. The mutual coupling levels are less than -17 dB and -20 dB, with the total efficiency of 89% and 90%, and the realized gain of 6.6 dB and 7 dB at the two resonant frequencies of 5 GHz and 6 GHz, respectively. Topological complexity of the compact MIMO systems featuring elaborated decoupling structures, a large number of geometry parameters, as well as the necessity of handling multiple performance figures, constitute the major challenges of antenna design, in particular, its re-design for various specifications. To alleviate these difficulties, the paper also provides a procedure for rapid geometry scaling of the dual-band MIMO antennas. Our approach is based on inverse surrogate modeling methods, and results in numerically-derived expressions that enable a precise control over the operating antenna bands within broad ranges thereof (from 4 GHz to 8 GHz for the lower band, and from 1.1 to 1.3 ratio of the upper to lower operating frequency). The aforementioned procedure is accompanied by an optimization-based design refinement scheme. A practical utility of the procedure is corroborated using multiple verification case studies as well as physical measurements of the antenna designed for the exemplary set of performance specifications., This work was supported in part by the Icelandic Centre for Research (RANNIS) under Grant 206606051, and in part by the National Science Centre of Poland under Grant 2020/37/B/ST7/01448.

6G is anticipated to become more human-centered than 5G and should not only explore more spectrum at high-frequency bands but, more importantly, converge driving technological trends such as blockchain technologies and connected robotics. This paper focuses on the emerging field of robonomics, which studies the sociotechnical impact of blockchain technologies on social human-robot interaction and behavioral economics for the social integration of robots into human society. Advanced blockchain technologies such as oracles enable the on-chaining of blockchain-external off-chain information stemming from human users. In doing so, they leverage on human intelligence rather than machine learning only. In this paper, we investigate the widely studied trust game of behavioral economics in a blockchain context, paying close attention to the importance of developing efficient cooperation and coordination technologies. After identifying open research challenges of blockchain-enabled implementations of the trust game, we first develop a smart contract that replaces the experimenter in the middle between trustor and trustee and demonstrate experimentally that a social efficiency of up to 100% can be achieved by using deposits to enhance both trust and trustworthiness. We then present an on-chaining oracle architecture for a networked $N$ -player trust game that involves a third type of human agents called observers, who track the players’ investment and reciprocity. The presence of third-party reward and penalty decisions helps raise the average normalized reciprocity above 80%, even without requiring any deposit. Finally, we experimentally demonstrate that mixed logical-affective persuasive strategies for social robots improve the trustees’ trustworthiness and reciprocity significantly.

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.

Cube Satellites, aka CubeSats, are a class of nano satellites that have gained popularity recently, especially for those that consider CubeSats as an emerging alternative to conventional satellites for space programs. This is because they are cost-effective, and they can be built using commercial off-the-shelf components. Moreover, CubeSats can communicate with each other in space and ground stations to carry out many functions such as remote sensing (e.g., land imaging, education), space research, wide area measurements and deep space communications. Consequently, communications between CubeSats and ground stations is critical. Any antenna design for a CubeSat needs to meet size and weight restrictions while yielding good antenna radiation performance. To date, a limited number of works have surveyed, compared and categorised the proposed antenna designs for CubeSats based on their operating frequency bands. To this end, this paper contributes to the literature by focusing on different antenna types with different operating frequency bands that are proposed for CubeSat applications. This paper reviews 48 antenna designs, which include 18 patch antennas, 5 slot antennas, 4 dipole and monopole antennas, 3 reflector antennas, 3 reflectarray antennas, 5 helical antennas, 2 metasurface antennas and 3 millimeter and sub-millimeter wave antennas. The current CubeSat antenna design challenges and design techniques to address these challenges are discussed. In addition, we classify these antennas according to their operating frequency bands, e.g., VHF, UHF, L, S, C, X, Ku, K/Ka, W and mm/sub-mm wave bands and provide an extensive qualitative comparison in terms of their size, −10 dB bandwidths, gains, reflection coefficients, and deployability. The suitability of different antenna types for different applications as well as the future trends for CubeSat antennas are also presented.

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.

The main core purpose of artificial emotional intelligence is to recognize human emotions. Technologies such as facial, semantic, or brainwave recognition applications have been widely proposed. However, the abovementioned recognition techniques for emotional features require a large number of training samples to obtain high accuracy. Human behaviour pattern can be trained and recognized by the continuous movement of the Spatial Temporal Graph Convolution Network (ST-GCN). However, this technology does not distinguish between the speed of delicate emotions, and the speed of human behaviour and delicate changes of emotions cannot be effectively distinguished. This research paper proposes Spatial Temporal Variation Convolutional Network training for human emotion recognition, using skeleton detection technology to calculate the degree of skeleton point change between consecutive actions and using the nearest neighbour algorithm to classify speed levels and train the ST-GCN recognition model to obtain the emotional state. Application of the speed change recognition ability of the Spatial Temporal Variation Graph Convolution Network (STV-GCN) to artificial emotional intelligence calculation makes it possible to efficiently recognize the delicate actions of happy, sad, fear, and angry in human behaviour. The STV-GCN technology proposed in this paper is compared with ST-GCN and can effectively improve the recognition accuracy by more than 50%.

Accurate jamming detection in the broadband frequency hopping (FH) systems plays a vital role in improving the anti-jamming performance of the communication systems. The traditional jamming detection algorithms are prone to misjudge FH communication signals as jamming signals, leading to inaccurate jamming detection. Therefore, this paper proposes a jamming detection algorithm for broadband FH systems based on multi-segment signals spectrum clustering (MSSC). According to the difference in the time-frequency characteristics between FH signals and jamming signals, the proposed algorithm accurately detects the jamming by clustering the spectral components of signals in multiple periods. In addition, a jamming detector suitable for broadband FH systems is designed and the theoretical solution to the threshold factor of signal detection based on the Welch spectrum is also given in this paper. The simulation results show that the MSSC-based jamming detection algorithm proposed in this paper can reduce the jamming false detection probability and significantly improve the estimation accuracy of jamming parameters, effectively overcoming the defects of the traditional FFT-based energy detection and the multi-hop accumulation algorithms whose detection accuracy is sensitive to FH signals.

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.

The art of paper folding also known as Origami plays an important role in various research areas of different scientific fields. Origami structures possess many useful characteristics such as reconfigurability, flexibility, deployability, compactness, and multi-functionality. These attributes are inevitable for modern communication systems. Additionally, they provide antenna engineers an extra degree of freedom while blueprinting next-generation designs. One of the desirable features of origami is its folding capability. Using this property, it can be transformed into a two-Dimensional (2D) sheet from a three-Dimensional (3D) structure and vice versa through external stimulus. In recent years, researchers have proposed numerous smart materials to optimize folding behavior. These smart materials extend the functionality of origami technology for designing self-folding structures required in space systems, small scale devices, and self-assembly systems. Origami structures feature many useful characteristics pertinent to modern day design challenges of communication systems such as reconfiguration, flexibility, compactness, and multifunctionality. In this work, a state of the art review is presented on conceptualization, design challenges, and fabrication of light-weight and low-cost deployable origami antennas. Furthermore, to provide critical insights deployment challenges and possible solutions are also provided. It is believed that this work will not only help peers to understand the basic working principles of different origami structures but will also lay the foundation for future research in the evolving field of origami-based antennas. Moreover, the design methodology proposed in this paper will also provide pragmatic solutions for origami antennas suitable for the harsh and rigid environments.

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.

Unsupervised person re-identification has been improved significantly by the development of cross domain person re-identification models, which apply useful knowledge in source data to completely unlabeled target data. However, existing cross domain re-identification models still remain a major limitation that they are all based on single-source and single-target setting. The only one source domain may remain a tremendous gap between target, generating negative effect for the model training in target domain. To overcome this drawback, this paper proposes a Multi-Source Transfer Network to learn a shared target-biased feature space between multi-source and target domains, which achieves transfer learning in feature-level, pixel-level, and task-level by the proposed target-biased multi-source transfer learning module, relativistic adversarial learning module, and task-gap bridging module, respectively. Through leveraging the domain gaps in feature-level, pixel-level, and task-level, this network can synthetically learn a discriminative model from multiple source domains to effectively conduct re-identification in target domain. Furthermore, this paper conducts extensive experiments on three widely-recognized person re-identification datasets, and the proposed network achieves rank-1 accuracies of 80.9% and 74.6% on DukeMTMC-reID and Market-1501 datasets, respectively. The results demonstrate the contribution of the proposed method, compared with state-of-the-art methods, including hand-crated feature, clustering and transfer learning based methods.

Digital watermarking has the properties such as invisibility and anti-aggression, so the digital watermarking technology has been widely used in copyright protection, information hiding. The watermarking technology takes into account the invisibility and robustness of the watermark by controlling the embedding intensity and position of the watermark mainly in the transformation domain. In this paper, discrete cosine transform (DCT) is adopted to transform the given image from spatial domain to frequency domain for adding watermark information. In order to meet the demands of image watermarking batch processing and cloud processing in the future, this paper optimized the DCT algorithm and the data precision, and successfully deployed the designed accelerator kernel on the FPGA cloud platform to speed up the processing of watermarking. The implementation of data processing based on cloud platform is the development trend of big data era. The cloud platform adopted in this paper is based on the OpenCL heterogeneous architecture combining CPU and FPGA. The cloud-based implementation makes digital watermarking application highly extensible, widely shareable, and more secure. The whole system implements a series of complete cloud processes including image decoding, image preprocessing, watermark embedding, and watermarked image encoding. The watermarking algorithm is accelerated by the efficient parallel computing capabilities of FPGA. It can be seen that the result of acceleration is remarkable, providing the state-of-the-art throughput of 1.676 GBps and the highest processing speed of 937 FPS for $800\times800$ sized colorimage.

Distributed Ledger Technologies (DLTs), like Blockchain, are characterized by features such as transparency, traceability, and security by design. These features make the adoption of Blockchain attractive to enhance information security, privacy, and trustworthiness in very different contexts. This paper provides a comprehensive survey and aims at analyzing and assessing the use of Blockchain in the context of Distributed Trust and Reputation Management Systems (DTRMS). The analysis includes academic research as well as initiatives undertaken in the business domain. The paper defines two taxonomies for both Blockchain and DTRMS and applies a Formal Concept Analysis. Such an approach allowed us to identify the most recurrent and stable features in the current scientific landscape and several important implications among the two taxonomies. The results of the analysis have revealed significant trends and emerging practices in the current implementations that have been distilled into recommendations to guide Blockchain's adoption in DTRMS systems.

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.

Vehicular Ad-hoc Network (VANET) is a significant component of intelligent transportation system, which facilitates vehicles to share sensitive information and corporate with others. However, due to its unique characteristics, such as openness, dynamic topology and high mobility, VANET suffers from various attacks. This paper proposes an anti-attack trust management scheme in VANET called AATMS to evaluate the trustworthiness of vehicles. With the help of AATMS, vehicles in VANET can avoid malicious vehicles and cooperate with trusted vehicles. The idea of AATMS is mainly inspired by TrustRank algorithm, which is used to combat web spams. In this paper, we calculate local trust and global trust, which indicate the local and global trust relationships among vehicles. First, Bayesian inference is adopted to calculate local trust of vehicles based on historical interactions. Then we select a small set of seed vehicles according to local trust and some social factors. Once we identify the reputable seed vehicles, we use the local trust link structure of vehicles to evaluate the global trust of all vehicles. The simulation results show that AATMS can efficiently identify trustworthy and untrustworthy vehicles in VANET even under malicious attacks.