Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical

Technology advances—such as improving processing power, battery life, and communication functionalities—contribute to making mobile devices an attractive research area. In 2008, in order to manage mobility, the Internet Engineering Task Force (IETF) developed Proxy Mobile IPv6, which is a network-based mobility management protocol to support seamless connectivity of mobile devices. This protocol can

In order to calculate the node big data contained in complex networks and realize the efficient calculation of complex networks, based on voluntary computing, taking ICE middleware as the communication medium, the loose coupling distributed framework DCBV based on voluntary computing is proposed. Then, the Master, Worker, and MiddleWare layers in the framework, and the development structure of a DCBV

The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart

The popularity of 5G networks has made smart manufacturing not limited to high-tech industries such as semiconductors due to its high speed, ultra-high reliability, and low latency. With the advance of system on chip (SoC) design and manufacturing, 5G is also suitable for data transmission in harsh manufacturing environments such as high temperatures, dust, and extreme vibration. The defect of the

Building an accurate map is essential for autonomous robot navigation in the environment without GPS. Compared with single-robot, the multiple-robot system has much better performance in terms of accuracy, efficiency and robustness for the simultaneous localization and mapping (SLAM). As a critical component of multiple-robot SLAM, the problem of map merging still remains a challenge. To this end,

In an open-domain dialogue system, recognition and expression of emotions are the key factors for success. Most of the existing research related to Chinese dialogue systems aims at improving the quality of content but ignores the expression of human emotions. In this article, we propose a Chinese emotional dialogue response generation algorithm based on reinforcement learning that can generate responses

Autism spectrum disorder (ASD) is a serious hazard to the physical and mental health of children, which limits the social activities of patients throughout their lives and places a heavy burden on families and society. The developments of communication techniques and artificial intelligence (AI) have provided new potential methods for the treatment of autism. The existing treatment systems based on

Smart cities fully utilize the new generation of Internet of Things (IoT) technology in the process of urban informatization to optimize the urban management and service. However, in the IoT system, while information exchange and communication, wireless sensor network devices may not be able to resist all forms of attacks, which may lead to security issues such as user data disclosure. Aiming at the

Efficient signal control at isolated intersections is vital for relieving congestion, accidents, and environmental pollution caused by increasing numbers of vehicles. However, most of the existing studies not only ignore the constraint of the limited computing resources available at isolated intersections but also the matching degree between the signal timing and the traffic demand, leading to high

Coronavirus Disease 19 (COVID-19) is a highly infectious viral disease affecting millions of people worldwide in 2020. Several studies have shown that COVID-19 results in a severe acute respiratory syndrome and may lead to death. In past research, a greater number of respiratory diseases has been caused by exposure to air pollution for long periods of time. This article investigates the spread of COVID-19

With the advancement of Cyber Physic Systems and Social Internet of Things, the tourism industry is facing challenges and opportunities. We can now able to collect, store, and analyze large amounts of travel data. With the help of data science and artificial intelligence, smart tourism enables tourists with great autonomy and convenience for an intelligent trip. It is of great significance to make

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data

Volunteer computing based data processing is a new trend in healthcare applications. Researchers are now leveraging volunteer computing power to train deep learning networks consisting of billions of parameters. Breast cancer is the second most common cause of death in women among cancers. The early detection of cancer may diminish the death risk of patients. Since the diagnosis of breast cancer manually

Volunteer computing is an Internet-based distributed computing in which volunteers share their extra available resources to manage large-scale tasks. However, computing devices in a Volunteer Computing System (VCS) are highly dynamic and heterogeneous in terms of their processing power, monetary cost, and data transferring latency. To ensure both of the high Quality of Service (QoS) and low cost for

In the ever-growing world, the concepts of High-utility Itemset Mining (HUIM) as well as Frequent Itemset Mining (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these algorithms only used one factor to estimate an itemset. In the past, skyline pattern mining by considering both aspects of frequency and utility has been extensively discussed

Emotional cognitive ability is a key technical indicator to measure the friendliness of interaction. Therefore, this research aims to explore robots with human emotion cognitively. By discussing the prospects of 5G technology and cognitive robots, the main direction of the study is cognitive robots. For the emotional cognitive robots, the analysis logic similar to humans is difficult to imitate; the

With the construction and improvement of 5G infrastructure, more devices choose to access the Internet to achieve some functions. People are paying more attention to information security in the use of network devices. This makes lightweight block ciphers become a hotspot. A lightweight block cipher with superior performance can ensure the security of information while reducing the consumption of device

Multiple robots negotiating in a dynamic workspace may lead to collisions. To avoid such issues, multi-robot navigation and coordination becomes necessary but is computationally very challenging, particularly when there are many robots. This article addresses the problem of multi-robot navigation where individual robots require coordination. Although a few such attempts for modeling multi-robot coordination

The development of cognitive robotics brings an attractive scenario where humans and robots cooperate to accomplish specific tasks. To facilitate this scenario, cognitive robots are expected to have the ability to interact with humans with natural language, which depends on natural language understanding (NLU) technologies. As one core task in NLU, sentence semantic matching (SSM) has widely existed

Modern network infrastructures host converged applications that demand rapid elasticity of services, increased security, and ultra-fast reaction times. The Tactile Internet promises to facilitate the delivery of these services while enabling new economies of scale for high fidelity of machine-to-machine and human-to-machine interactions. Unavoidably, critical mission systems served by the Tactile Internet

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and

Deep learning techniques have shown significant success in cyber-physical-social systems (CPSS). As an instance of deep learning models, generative adversarial nets (GAN) model enables powerful and flexible image augmentation, image generation, and classification, thus can be applied to real-world CPSS settings. GAN model training needs a large collection of cyber-physical-social data originating from

As cryptographic tokens and altcoins are increasingly being built to serve as utility tokens, the notion of useful work consensus protocols is becoming ever more important. With useful work consensus protocols, users get rewards after they have carried out some specific tasks useful for the network. While in some cases the proof of some utility or service can be provided, the majority of tasks are

Social robots adopt an emotional touch to interact with users inducing and transmitting humanlike emotions. Natural interaction with humans needs to be in real time and well grounded on the full availability of information on the environment. These robots base their way of communicating on direct interaction (touch, listening, view), supported by a range of sensors on the surrounding environment that

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network

The study aims at discussing the security of medical data in the Internet era. By using k-anonymity (K-A) and differential privacy (DP), an algorithm model combining K-A and DP was proposed, which was simulated through the experiments. In the Magic and EIA datasets, the algorithm constructed was compared with K-A and the L-diversity model to verify the performance of the model. The model constructed

Fingerprint provides an extremely convenient way of identification for a wide range of real-life applications owing to its universality, uniqueness, collectability, and invariance. However, digitized fingerprints may reveal the privacy of individuals. Differential privacy is a promising privacy-preserving solution that is enforced by injecting random noise into preserved objects, such that an adversary

Time-series medical images are an important type of medical data that contain rich temporal and spatial information. As a state-of-the-art, computer-aided diagnosis (CAD) algorithms are usually used on these image sequences to improve analysis accuracy. However, such CAD algorithms are often required to upload medical images to honest-but-curious servers, which introduces severe privacy concerns. To

As one of the essential applications of health information technology, the eHealth system plays a significant role in enabling various internet medicine service scenes, most of which primarily rely on service recommendation or an evaluation mechanism. To avoid privacy leakage, some privacy-preserving mechanisms must be adopted to protect raters’ privacy and make evaluation trust reliable. To tackle

In this article, a blockwise regression-based image inpainting framework is proposed. The core idea is to fill the unknown region in two stages: Extrapolate the edges to the unknown region and then fill the unknown pixels values in each sub-region demarcated by the extended edges. Canny edge detection and linear edge extension are used to respectively identify and extend edges to the unknown region

Many Internet of Things applications in smart cities use elliptic-curve cryptosystems due to their efficiency compared to other well-known public-key cryptosystems such as RSA. One of the important components of an elliptic-curve-based cryptosystem is the elliptic-curve point multiplication which has been shown to be vulnerable to various types of side-channel attacks. Recently, substantial progress

The analysis for social networks, such as the socially connected Internet of Things, has shown a deep influence of intelligent information processing technology on industrial systems for Smart Cities. The goal of social media representation learning is to learn dense, low-dimensional, and continuous representations for multimodal data within social networks, facilitating many real-world applications

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple

Smart hospitals are important components of smart cities. An intelligent medical system for brain tumor segmentation is required to construct smart hospitals. To achieve intelligent brain tumor segmentation, morphological variety and serious category imbalance must be managed effectively. Conventional deep neural networks have difficulty in predicting high-accuracy segmentation images due to these

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework: Multi-Agent Ensemble. The framework leverages edge computing to facilitate ensemble

Green Internet of things (GIoT) generally refers to a new generation of Internet of things design concept. It can save energy and reduce emissions, reduce environmental pollution, waste of resources, and harm to human body and environment, in which green smart device (GSD) is a basic unit of GIoT for saving energy. With the access of a large number of heterogeneous bottom-layer GSDs in GIoT, user access

The present study aimed to use the proposed system to measure and analyze brain waves of users to allow intelligent upper limb rehabilitation and to optimize the system using a genetic algorithm. The study used EPOC Neuroheadset for Emotiv with EEG electrodes attached as a non-invasive method for measuring brain waves. The brain waves were measured according to the EEG 10-20 standard electrode layout

Parkinson's disease is a neurodegenerative disease that affects millions of people around the world and cannot be cured fundamentally. Automatic identification of early Parkinson's disease on feature data sets is one of the most challenging medical tasks today. Many features in these datasets are useless or suffering from problems like noise, which affect the learning process and increase the computational

Super-resolution methods for facial image via representation learning scheme have become very effective methods due to their efficiency. The key problem for the super-resolution of facial image is to reveal the latent relationship between the low-resolution (LR) and the corresponding high-resolution (HR) training patch pairs. To simultaneously utilize the contextual information of the target position

Nowadays, a sustainable and smart city focuses on energy efficiency and the reduction of polluting emissions through smart mobility projects and initiatives to “sensitize” infrastructure. Smart parking is one of the building blocks of intelligent mobility, innovative mobility that aims to be flexible, integrated, and sustainable and consequently integrated into a Smart City. By using the Internet of

Because of the complex activities involved in IoT networks of a smart city, an important question arises: What are the core activities of the networks as a whole and its basic information flow structure? Identifying and discovering core activities and information flow is a crucial step that can facilitate the analysis. This is the question we are addressing—that is, to identify the core services as

A smart city improves operational efficiency and comfort of living by harnessing techniques such as the Internet of Things (IoT) to collect and process data for decision-making. To better support smart cities, data collected by IoT should be stored and processed appropriately. However, IoT devices are often task-specialized and resource-constrained, and thus, they heavily rely on online resources in

Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal

Edge Artificial Intelligence (AI) is the latest trend for next-generation computing for data analytics, particularly in predictive edge analytics for high-risk diseases like Parkinson’s Disease (PD). Deep learning learning techniques facilitate edge AI applications for enhanced, real-time handling of data. Dopamine is the cause of Parkinson’s that happens due to the interference of brain cells that

The advancements in the internet of things (IoT) require specialized security protocols to provide unbreakable security along with computation and communication efficiencies. Moreover, user privacy and anonymity has emerged as an integral part, along with other security requirements. Unfortunately, many recent authentication schemes to secure IoT-based systems were either proved as vulnerable to different

In the last decade, twin support vector machine (TWSVM) classifiers have achieved considerable emphasis on pattern classification tasks. However, the TWSVM formulation still suffers from the following two shortcomings: (1) TWSVM deals with the inverse matrix calculation in the Wolfe-dual problems, which is intractable for large-scale datasets with numerous features and samples, and (2) TWSVM minimizes

In recent years, vaccine incidents occurred around the world, which endangers people’s lives. In the technical respect, these incidents are partially due to the fact that existing vaccine management systems are distributively managed by different entities in the vaccine supply chain. This architecture makes it relatively easy to modify or even delete the vaccine circulation data maliciously, which

Automatically summarizing a group of short texts that mainly share one topic is a fundamental task in many applications, e.g., summarizing the main symptoms for a disease based on a group of medical texts that are usually short, i.e., tens of words. Conventional unsupervised short text summarization techniques tend to find the most representative short text document. However, they may cause privacy

During the past several years, revealing some useful knowledge or protecting individual’s private information in an identifiable health dataset (i.e., within an Electronic Health Record) has become a tradeoff issue. Especially in this era of a global pandemic, security and privacy are often overlooked in lieu of usability. Privacy preserving data mining (PPDM) is definitely going to be have an important

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge

Nature-inspired optimization is one of the most prevalent research domains with a confounding history that fascinates the research communities. Particle Swarm Optimization is one of the well-known optimizers that belongs to the family of nature-inspired algorithms. It often suffers from premature convergence leading to a local optimum. To address this, several methods were presented using different

Multi-task learning has been widely applied to Alzheimer’s Disease (AD) studies due to its capability of simultaneously rating the disease severity (classification) and predicting corresponding clinical scores (regression). In this article, we propose a novel technique of Adaptive Multi-task Dual-Structured Learning, named AMDSL, by mutually exploring the dual manifold structure for the label and regression

Using unsupervised learning methods for clinical diagnosis is very meaningful. In this study, we propose an unsupervised multi-view & multi-medoid variant-entropy-based fuzzy clustering (M2VEFC) method for epilepsy EEG signals detecting. Comparing with existing related studies, M2VEFC has four main merits and contributions: (1) Features in original EEG data are represented from different perspectives

In today’s highly connected cyber-physical environments, users are becoming more and more concerned about their privacy and ask for more involvement in the control of their data. However, achieving effective involvement of users requires improving their privacy decision-making. This can be achieved by: (i) raising their awareness regarding the direct and indirect privacy risks they accept to take when

Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling

Recent advancements in information technology have ushered a new wave of systems integrating Internet technology with sensing, wireless communication, and computational resources over existing infrastructures. As a result, myriad complex, non-traditional Cyber-Physical Systems (CPS) have emerged, characterized by interaction among people, physical facilities, and embedded sensors and computers, all

Machine learning and artificial intelligence provide methods and algorithms to take advantage of sensor and actuator data in automated production systems. Product quality monitoring is one of the promising applications available for data-driven modeling, particularly in cases where the quality parameters cannot be measured with reasonable effort. This is the case for defects such as cracks in workpieces

The study aims at exploring the Internet of things (IoT) system from the perspective of data and further improving the performance of the IoT system. The IoT data energy collection and information transmission system model is constructed by combining IoT and wireless relay cooperative transmission technology. Moreover, the energy efficiency, outage probability (OP), and accuracy of the model are evaluated