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As an important carrier of healthcare data, Electronic Medical Records (EMRs) generated from various sensors, i.e., wearable, implantable, are extremely valuable research materials for artificial intelligence and machine learning. The efficient circulation of EMRs can improve remote medical services and promote the development of the related healthcare industry. However, in traditional centralized data sharing architectures, the balance between privacy and traceability still cannot be well handled. To address the issue that malicious users cannot be locked in the fully anonymous sharing schemes, we propose a trackable anonymous remote healthcare data storing and sharing scheme over decentralized consortium blockchain. Through an "on-chainoff-chain" model, it relieves the massive data storage pressure of medical blockchain. By introducing an improved proxy re-encryption mechanism, the proposed scheme realizes the fine-gained access control of the outsourced data, and can also prevent the collusion between semi-trusted cloud servers and data requestors who try to reveal EMRs without authorization. Compared with the existing schemes, our solution can provide a lower computational overhead in repeated EMRs sharing, resulting in a more efficient overall performance.

Nowadays, Federated Learning (FL) over Internet of Medical Things (IoMT) devices has become a current research hotspot. As a new architecture, FL can well protect the data privacy of IoMT devices, but the security of neural network model transmission can not be guaranteed. On the other hand, the sizes of current popular neural network models are usually relatively extensive, and how to deploy them on the IoMT devices has become a challenge. One promising approach to these problems is to reduce the network scale by quantizing the parameters of the neural networks, which can greatly improve the security of data transmission and reduce the transmission cost. In the previous literature, the fixed-point quantizer with stochastic rounding has been shown to have better performance than other quantization methods. However, how to design such quantizer to achieve the minimum square quantization error is still unknown. In addition, how to apply this quantizer in the FL framework also needs investigation. To address these questions, in this paper, we propose FEDMSQE - Federated Learning with Minimum Square Quantization Error, that achieves the smallest quantization error for each individual client in the FL setting. Through numerical experiments in both single-node and FL scenarios, we prove that our proposed algorithm can achieve higher accuracy and lower quantization error than other quantization methods.

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, Time Sensitive Network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then a non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultra-low latency services and guarantee efficient bandwidth utilization.

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%.

The 5G IoT is very complicated and there are many factors that affect the network performance. Presently, the optimization of network is still the focus of research. Although the existing literature has done a large number of researches in this aspect, there have always been problems, such as complex algorithms. Based on the previous research, we propose a big data mining analysis method, which improves the comprehensive performance of the network by analyzing the relationship of massive data variables so as to optimize the combination of the network. In this paper, according to each of terminal variables at any moment such as power consumption, bandwidth, noise power, subcarrier bandwidth, interference power and coding efficiency, etc. we develop the mathematical modeling of principal component multiple regression. Then we simulate this scheme by edge computing technology and combine it with intelligent algorithms. The research results show that this method can effectively predict the data concerned, and the residual is the smallest. Therefore, the research provides an important basic for application of the approach to the mobile edge network optimization of IoTs.

k nearest neighbor similarity join on high-dimensional data has broad applications in many fields; several key challenges still exist for this task such as “curse of dimensionality” and large scale of the dataset. A new dimensionality reduction scheme is proposed by using random projection technique, then we design two novel partition strategies, including equal width partition strategy and distance split tree-based partition strategy, and finally, we propose k nearest neighbor join algorithm on high-dimensional data based on the above partition strategies. We conduct comprehensive experiments to test the performance of the proposed approaches, and the experimental results show that the proposed methods have good effectiveness and performance.

Image encryption is highly required in the big data healthcare cloud to improve the security of the medical image for remote access. Data hiding method is the process of storing the medical information of the patient in the medical image in the hidden format. Many existing data hiding methods are based on wavelet and chaotic map due to its effectiveness. Wavelet based methods have limitations of lack of phase information, poor directionality, and shift sensitivity. Chaotic map is applied to improve the security of the medical image and chaotic map has the limitation of low sensitive to control parameters and initial conditions. In this research, the Improved Chaos Encryption (ICE) is applied to improve the security based on randomness. The average energy is calculated in the images and compared with adaptive threshold to segment the Lorenz 96 model applied in the chaos encryption algorithm to improve the model security. Lorenz 96 increased the randomness of the chaos encryption method due to its high sensitivity. Medial images were used to test the performance of the ICE in the image encryption and image hiding. The proposed ICE model evaluated the quality of the recovered and decrypted image in the various embedding rate. The result shows that the proposed ICE model has the PSNR value of 104.7 dB compared to the LSB-ROI method which has 97.61 dB PSNR.

A cancer tumour consists of thousands of genetic mutations. Even after advancement in technology, the task of distinguishing genetic mutations, which act as driver for the growth of tumour with passengers (Neutral Genetic Mutations), is still being done manually. This is a time-consuming process where pathologists interpret every genetic mutation from the clinical evidence manually. These clinical shreds of evidence belong to a total of nine classes, but the criterion of classification is still unknown. The main aim of this research is to propose a multiclass classifier to classify the genetic mutations based on clinical evidence (i.e., the text description of these genetic mutations) using Natural Language Processing (NLP) techniques. The dataset for this research is taken from Kaggle and is provided by the Memorial Sloan Kettering Cancer Center (MSKCC). The world-class researchers and oncologists contribute the dataset. Three text transformation models, namely, CountVectorizer, TfidfVectorizer, and Word2Vec, are utilized for the conversion of text to a matrix of token counts. Three machine learning classification models, namely, Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), along with the Recurrent Neural Network (RNN) model of deep learning, are applied to the sparse matrix (keywords count representation) of text descriptions. The accuracy score of all the proposed classifiers is evaluated by using the confusion matrix. Finally, the empirical results show that the RNN model of deep learning has performed better than other proposed classifiers with the highest accuracy of 70%.

Kubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. Despite plenty of research work in the comparison of relational and NoSQL databases in handling geospatial data, there is a shortage of existing knowledge about the performance of geo-database in a clustered environment like Kubernetes. This paper presents benchmarking of PostgreSQL/PostGIS geospatial databases operating on a clustered environment against non-clustered environments. The benchmarking process considers the average execution times of geospatial structured query language (SQL) queries on multiple hardware configurations to compare the environments based on handling computationally expensive queries involving SQL operations and PostGIS functions. The geospatial queries operate on data imported from OpenStreetMap into PostgreSQL/PostGIS. The clustered environment powered by Kubernetes demonstrated promising improvements in the average execution times of computationally expensive geospatial SQL queries on all considered hardware configurations compared to their average execution times in non-clustered environments.

In the resource-constrained embedded systems, the designing of efficient deep neural networks is a challenging process, due to diversity in the artificial intelligence applications. The quantization in deep neural networks superiorly diminishes the storage and computational time by reducing the bit-width of networks encoding. In order to highlight the problem of accuracy loss, the quantization levels are automatically discovered using Policy Feedback-based Reinforcement Learning Method (PF-RELEQ). In this paper, the Proximal Policy Optimization with Policy Feedback (PPO-PF) technique is proposed to determine the best design decisions by choosing the optimum hyper-parameters. In order to enhance the sensitivity of the value function to the change of policy and to improve the accuracy of value estimation at the early learning stage, a policy update method is devised based on the clipped discount factor. In addition, specifically the loss functions of policy satisfy the unbiased estimation of the trust region. The proposed PF-RELEQ effectively balances quality and speed compared to other deep learning methods like ResNet-1202, ResNet-32, ResNet-110, GoogLeNet and AlexNet. The experimental analysis showed that PF-RELEQ achieved 20% computational work-load reduction compared to the existing deep learning methods on ImageNet, CIFAR-10, CIFAR-100 and tomato leaf disease datasets and achieved approximately 2% of improvisation in the validation accuracy. Additionally, the PF-RELEQ needs only 0.55 Graphics Processing Unit on an NVIDIA GTX-1080Ti to develop DNNs that delivers better accuracy improvement with fewer cycle counts for image classification.

Edge computing can provide many key functions without connecting to centralized servers, which enables remote areas to obtain real-time medical diagnoses. The combination of edge computing and Internet of things (IoT) devices can send remote patient data to the hospital, which will help to more effectively address long-term or chronic diseases. CT images are widely used in the diagnosis of clinical diseases, and their characteristics are an important basis for pathological diagnosis. In the CT imaging process, speckle noise is caused by the interference of ultrasound on human tissues, and its component information is complex. To solve these problems, we propose a 3D reconstruction method for noisy CT images in the IoT using edge computing. First, we propose a multi-stage feature extraction generative adversarial network (MF-GAN) denoising algorithm. The generator of MF-GAN adopts the multi-stage feature extraction, which can ensure the reconstruction of the image texture and edges. Second, we apply the denoised images generated from the MF-GAN method to perform the 3D reconstruction. A marching cube (MC) algorithm based on regional growth and trilinear interpolation (RGT-MC) is proposed. With the idea of regional growth, all voxels containing iso-surfaces are selected and calculated, which accelerates the reconstruction efficiency. The intersection point of the voxel and iso-surface is calculated by the trilinear interpolation algorithm, which effectively improves the reconstruction accuracy. The experimental results show that MF-GAN has a better denoising effect than other algorithms. Compared to other representative 3D algorithms, the RGT-MC algorithm greatly improves the efficiency and precision.

In order to address the problems of traditional inpainting algorithm models, such as the inability to automatically identify the specific location of the area to be restored, the cost of inpainting and the difficulty of inpainting, and the problems of structural and texture discontinuity and poor model stability in deep learning-based image inpainting, this paper proposes an image inpainting based on a contextual coherent attention. This paper designs a network model based on generative adversarial networks. First, to improve the global semantic continuity and local semantic continuity of images in image inpainting, a contextual coherent attention layer is added to the network; second, to solve the problems of slow convergence and insufficient training stability of the model, a cross-entropy loss function is used; finally, the trained generator is used to repair images. The experimental results are compared using PSNR and SSIM metrics, compared with the traditional GAN model, our model has a 3.782dB improvement in peak signal-to-noise ratio and a 0.025% improvement in structural similarity. The experimental results show that the image inpainting method in this paper has better performance in terms of image edge processing, pixel continuity and overall image structure.

Visualization is one of the most intuitive and perceptible ways for information representation in the big data era. As an essential part of the visualization, 3D mesh reconstruction is facing great challenges due to its characteristics of quantity, non-structure, and low-accuracy. The traditional 3D mesh reconstruction method has strict theoretical proof and can be used to reconstruct the surface of the complex topological structure for computer rendering and display. However, it is not suitable to handle a large number of point cloud and noise point cloud in a big data platform because the process is inefficient, low-automation and requires massive calculations. To address this issue, we propose a region growing based 3D mesh reconstruction (R3MR) in the big data platform. Firstly, we divide the large data points into three categories: flat point set, high curvature point set, and boundary point set. The errors of topological structure for 3D meshes usually occur in the place with large curvatures and noise points, so the division of high curvature point set is beneficial to solve the low-accuracy problem in 3D mesh reconstruction. Moreover, the flat points can be treated as one kind of point to avoid repetitive calculations because their features are basically the same. Hence, the division of the flat point set is beneficial to solve the problem of quantity and massive calculations. Secondly, our proposal is to start the mesh reconstruction from the flat point set progressively, because it can obtain the outline of the 3D model. In many scenarios, such as autonomous driving, only the overall outline of the model is required. Finally, during the 3D mesh reconstruction, the inner edge adjacency list and optimal selection principle are set to improve the robustness of the whole system. Simulation experiments show that the proposed 3D mesh reconstruction can naturally reflect the detailed features of objects in the big data platform, especially effective for the scattered point cloud.

Color medical images better reflect a patient's lesion information and facilitate communication between doctors and patients. The combination of medical image processing and the Internet has been widely used for clinical medicine on Internet of medical things. The classical Welsh method uses matching pixels to achieve color migration of grayscale images, but it exists problems such as unclear boundary and single coloring effect. Therefore, the key information of medical images after coloring can't be reflected efficiently. To address this issue, we propose an image coloring method based on Gabor filtering combined with Welsh coloring and apply it to medical grayscale images. By using Gabor filtering, which is similar to the visual stimulus response of simple cells in the human visual system, filtering in 4 directions and 6 scales is used to stratify the grayscale images and extract local spatial and frequency domain information. In addition, the Welsh coloring method is used to render the image with obvious textural features in the layered image. Our experiments show that the color transboundary problem can be solved effectively after the layered processing. Compared to images without stratification, the coloring results of the processed images are closer to the real image.

Electronic prescription (E-prescription) is an emerging technology that allows health practitioners (doctors, physicians, pharmacists, or nurses) to electronically transmit prescriptions to pharmacies. E-prescription systems allow doctors to avoid traditional medical practices in which prescriptions are sent manually in handwritten form. Additionally, in cases in which a patient may not be able to collect the medication in person due to physical disabilities, the medications can be delivered to the patient’s home directly. Furthermore, payments can also be made online (e.g., using credit cards or bank transfers). However, these distinctive features require a series of guidelines for the successful deployment of the E-prescription system due to stringent legal requirements and privacy regulations. Two major security requirements i.e. confidentiality and authentication need to be addressed. In general, the solution to ensuring confidentiality and authentication lies in the combination of both the encryption and digital signature functions in a single logic step called signcryption. Therefore, in this article, we present a lightweight and provable secured certificate-based proxy signcryption (CB-PS) scheme for e-prescription systems. The formal security verification uses the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool along with informal security analysis, which authenticates that the proposed CB-PS scheme can potentially be implemented in resource-constrained low-computing electronic devices in E-prescription systems.

The most burning topic of today, calls for a holistic solution that is reliable, secure, privacy preserved, cost effective Cloud storage that can tide over the turbulent conditions of the rapidly budding digital storage technologies. This send an outcry for a devoted solution, in the form of an individualized, patient-centric care - IoMT that augments precise disease identifications, decrease in errors, reduction in costs of care through the support of technology, allows patients to direct health information data to doctors,manage drugs, keep Personal Health Records, caters to remote medical supports Care, provides proactive approach to preserving Good Health, improves and Accelerates Clinician Workflows, empowers extreme connectivity due to better automation and perceptions in the DNA of IoMT functions. But IoMT adoption is like a rose with thorns like constraints of increased administrative costs, deficiency of universal data access, present-day electronic medical records. The BCT is used in the framework to overcome the security issues of IoMT through the use of latest encryptions. Furthermore, this framework harnesses the benefits of Block Chain like reduced cost, speed, automation, immutability, near-impossible loss of data, permanence, removal of intermediaries, decentralization of consensus, legitimate access to health data, data safekeeping, accrual-based imbursement mechanisms, and medical supply chain efficacy. The outcomes in this paper are (i)A systematic investigation of the current IoMT, Block Chain and Cloud Storage in Health Care;(ii) Explore the challenges and necessities for the confluence of Block Chain (BC), Internet of Medical Things (IoMT), Cloud Computing (CC);(iii)Formulate the requirements necessary for the real-time remote Health Care of one-to-one care structure, which, supports the vital functions that are critical to the Patient Centric Health Care;(iv) Design and develop a novel BC IoMT U6 HCS (Block Chain based Internet of Medical Things for Uninterrupted, Ubiquitous, User-friendly, Unflappable, Unblemished, Unlimited Health Care Services) Layered Architecture, to support the vital functions critical for Patient Centric Health Care and (v) Implement and test with the previous established and proven techniques. The integrity of the Layered Architecture is validated with the already existing ones in terms of audit performances. The results from the Layered Architecture are validated and are proven to be competent in achieving safe auditing and surpass the former ones. The technology is in the sprouting phases, it is perilous that affiliates of the Health Care community realize the rudimentary ideas behind Block Chain, and detect its feasible impact on the future of patient centric medical care. Finally, and most importantly, this paper also gives a panoramic view on the current research status, and imminent directions of Secure Internet of Medical Things Using Block Chain.

With the wide application of the Internet of Things (IoT) in real world, the impact of the security on its development is becoming incrementally important. Recently, many advanced technologies, such as artificial intelligence (AI), computational intelligence (CI), and deep learning method, have been applied in different security applications. In intrusion detection system (IDS) of IoT, this paper developed an adaptive differential evolution based on simulated annealing algorithm (ASADE) to deal with the feature selection problems. The mutation, crossover, and selection processes of the self-adaptive DE algorithm are modified to avoid trapping in the local optimal solution. In the mutation process, the mutation factor is changed based on the hyperbolic tangent function curve. A linear function with generation is incorporated into the crossover operation to control the crossover factor. In the selection process, this paper adopts the Metropolis criterion of the SA algorithm to accept poor solution as optimal solution. To test the performance of the proposed algorithm, numerical experiments were performed on 29 benchmark functions from the CEC2017 and six typical benchmark functions. The experimental results indicate that the proposed algorithm is superior to the other four algorithms.

In the era of modern technology, people may readily communicate through facial expressions, body language, and other means. As the use of the Internet evolves, it may be a boon to the medical fields. Recently, the Internet of Medical Things (IoMT) has provided a broader platform to handle difficulties linked to healthcare, including people's listening and hearing impairment. Although there are many translators that exist to help people of various linguistic backgrounds communicate more effectively. Using kinesics linguistics, one may assess or comprehend the communications of auditory and hearing-impaired persons who are standing next to each other. When looking at the present COVID-19 scenario, individuals are still linked in some way via online platforms; however, persons with disabilities have communication challenges with online platforms. The work provided in this research serves as a communication bridge inside the challenged community and the rest of the globe. The proposed work for Indian Sign Linguistic Recognition (ISLR) uses three-dimensional convolutional neural networks (3D-CNNs) and long short-term memory (LSTM) technique for analysis. A conventional hand gesture recognition system involves identifying the hand and its location or orientation, extracting certain essential features and applying an appropriate machine learning algorithm to recognise the completed action. In the calling interface of the web application, WebRTC has been implemented. A teleprompting technology is also used in the web app, which transforms sign language into audible sound. The proposed web app's average recognition rate is 97.21%.

Intelligent transportation applications based on urban Internet of Things can improve the efficiency of government services and promote urban modernization. As smart cameras are more and more widely used in cities, artificial intelligence technology is an important force to achieve license plate recognition. An efficient license plate recognition algorithm not only improves the efficiency of traffic management but also saves management costs. This paper proposes a network based on the shufflenetv2 dilated convolution (SDC) model, which includes two parts: license plate location and license plate recognition. SDC model adopts shufflenetv2 as the backbone network, which combines dilated convolution and global context blocks. Therefore, the receptive field and feature expression ability of the model are enhanced. For license plate location, CIOU loss considers not only the coverage area of the bounding box but also the center distance and aspect ratio. For license plate recognition, CTC loss trains the network based on the sequence and solves the sample alignment problem, which improves the accuracy of license plate recognition. The experiments show that the precision of the SDC model in license plate location is 98.7%, which is 5.2%, 5.5%, and 4.1% higher than the precision of Faster-RCNN, YOLOv3, and SSD, respectively. The precision of the SDC model in license plate recognition is 98.2%, which is 5.3%, 3.7%, and 2.9% higher than the precision of LPRNet, AlexNet, and RPNet, respectively.

Medical data security is an important guarantee for intelligent medical system. Medical video data can help doctors understand the patients’ condition. Medical video retargeting can greatly reduce the storage capacity of data on the premise of preserving the original content information as much as possible. The smaller volume of medical data can reduce the execution time of data encryption and threat detection algorithm and improve the performance of medical data security methods. The existing methods mainly focus on the temporal pixel relationship and foreground motion between adjacent frames, but these methods ignore the user’s attention to the video content and the impact of background movement on retargeting, resulting in serious deformation of important content and area. To solve the above problems, this paper proposes an innovative video retargeting method, which is based on visual attention and motion estimation. Firstly, the visual attention map is obtained from eye tracking data, by K-means clustering method and Euclidean distance factor equation. Secondly, the motion estimation map is generated from both the foreground and background displacements, which are calculated based on the feature points and salient object positions between adjacent frames. Then, the visual attention map, the motion estimation map, and gradient map are fused to the importance map. Finally, video retargeting is performed by mesh deformation based on the importance map. Experiment on open datasets shows that the proposed method can protect important area and has a better effect on salient object flutter suppression.

Flying Ad hoc Network (FANET) presents various challenges during communication due to the dynamic nature of network and ever-changing topology. Owing to high mobility, it is difficult to ensure a well-connected network and link stability. Thus, flying nodes have a higher chance of becoming disconnected from the network. In order to overcome these discrepancies, this work provides a well-connected network, reducing the number of isolated nodes in FANETs utilizing the depth of machine learning by taking inspiration from biology. Every biological species is innately intelligent and has strong learning ability. Moreover, they can also learn from existing active events and can take decision based on previous experience. There may be some unusual events such as attack of predator or when it may become isolated from the rest of the community. This ability helps them to maintain connectivity and concentrate on target. In this work, we take inspiration from dragonflies, which provide novel swarming behaviors of dynamic swarming and static swarming. The nodes in FANETs learn from the dragonflies and use this learning to search for a neighbor, ensuring connectivity. Moreover, to avoid collision and establish larger coverage area, they employ separation and alignment. In case a drone is isolated, it strives to become part of the network using machine learning (ML) via the dragonfly algorithm (DA). The proposed scheme results in larger coverage area with reduced number of isolated drones. This improves the connectivity in FANETs adding to the network intelligence via learning through DA, allowing communication despite the complexity of mobility and dynamic network topology.

Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.

In this paper, nature inspired search algorithms, namely particle swarm optimization (PSO) and genetic algorithm (GA) are used to design fractional order proportional and integral (FOPI) and artificial neural network (ANN) controller based distribution static compensator (DSTATCOM) and electronic load controller (ELC) for power quality improvement. Improvement in power quality is achieved using DSTATCOM and an ELC. DSTATCOM is designed using FOPI and ANN based controllers, as opposed to conventional PI controllers which are comparatively less efficient. PSO and GA techniques are employed to determine the optimal parameters for the controllers. The improvement in the performance of the ANN and FOPI as compared to PI controller for the DSTATCOM and ELC is validated using MATLAB based modeling and simulations. Linear consumer loads were used to perform a comparative study in terms of maximum percentage error. Further, we analyzed the system for a nonlinear load and demonstrated decrease in the harmonic distortion.

Cloud computing is generally considered as a special energy-efficient form for the Internet of Things (IoT) resource usage. Dedicated server systems for cloud services, better capacity utilization and economies of scale because of the use of larger and more energy-efficient data centers are the reasons why cloud solutions typically use less energy than traditional on-premise systems. To scientifically and rationally configure the hardware and software resources of the cloud computing, the research on forecasting a cloud computing resource load becomes a research focus. However, the widely-used single forecasting model cannot contain all the characteristics of the cloud computing resource load sequence, resulting in inaccurate forecasting results. In this paper, a combined forecasting approach of cloud computing resource load based on wavelet decomposition is proposed, which combined the grey model and cubic exponential smoothing model. It can well preserve details and reduce noise. Firstly, the cloud computing resource load sequence is decomposed into several frequencies by the wavelet decomposition method. The decomposed load sequences with different characteristics are divided into different resolution scale subspaces in deferent frequencies. The noise of the load sequences is reduced by the wavelet threshold denoising method. And then, the load sequences are reconstructed according to the wavelet coefficients. The reconstructed load sequence not only contains less noise but also reserves detailed information. Consequently, it is closer to the real data and more regular. Experimental results show that our proposed combined forecasting model with wavelet decomposition can provide more accurate forecasting results than each single forecasting model or the combined forecasting model without using the wavelet decomposition method. Thus, our proposal is demonstrated to be efficient for forecasting the cloud computing resource load and helping to reduce energy consumption.

Information-Centric Networking (ICN) was born in an era that more and more users are shifting their interests to the content itself rather than the location where contents are stored. This paradigm shift in the usage patterns of the Internet, along with the urgent needs for content naming, pervasive caching, better security, and mobility support, has prompted researchers to consider a radical change to the Internet architecture. However, ICN is still in its infancy and development stage, and many issues still exist and need to be addressed. The common ICN architecture is lacking the host-centric communication ability and difficult to provide seamless mobility in current solutions. To solve this problem, our team had proposed the Named-Node Networking (3N) concept, which not only naming the content but also naming the node and it proved to have better performance of providing seamless mobility in the simulation. However, the previous contributions were limited in the 3N namespace for seamless mobility support in both producer and consumer. In this paper, we have proposed a 3N system which includes 3N naming, data delivery, mobility support, and data security. Moreover, we have created a 3N-based real-time video streaming system to evaluate data delivery performance and mobility handoff performance. The evaluation result proofs that our system performs better than TCP video streaming in a multi-client situation and a WiFi-based handoff was demonstrated.

Information-centric networking (ICN) is a new approach to networking contents rather than devices that hold the contents. It has recently attracted much attention of network research and standardization communities. National and multi-national funded research projects have progressed worldwide. International Telecommunication Union - Telecommunication Standardization Sector (ITU-T) started ICN standardization activities in 2012. In parallel, the standards-oriented research cooperation is progressing in the Information-Centric Networking Research Group (ICNRG) of the Internet Research Task Force (IRTF). All these global efforts have been collectively advancing the novel network architecture of ICN. However, there are very few surveys and discussions on the detailed ICN standardization status. To update the reader with information about the ICN research and standardization related activities, this paper starts with the history of global activities on ICN from 2010, giving references to various projects. It then describes the recent progress in the standardization of ICN component technologies in ITU-T and various documents produced by ICNRG. Lastly, it discusses the future directions for progressing ICN.