Internet Of People Research Articles

Multi-Screen Adaptive Displaying With Augmented Reality Manipulation Based on the Internet of People

Multi-Screen Adaptive Displaying With Augmented Reality Manipulation Based on the Internet of People

Dual Mutual Robust Graph Convolutional Network for Weakly Supervised Node Classification in Social Networks of Internet of People

Social networks are a crucial component of the Internet of People (IoP) which is the frontier of the next generation of Internet of Things (IoT). Predicting a large number of unknown node labels with few known labels is one of the challenging problems in social networks analysis. Fortunately, Graph Convolution Network (GCN) and subsequent variants have achieved impressive performance on Semi-Supervised Node Classification (SSNC). However, existing works only focus on the case of clean labels and rarely study the problem of SSNC under Noisy Labels (SSNCNL), which is a more challenging and practical problem in the field of weakly supervised learning. To address this challenge, in this paper, we propose a novel Dual Mutual Robust Graph Convolutional Network named DMRGCN, which is inspired by deep mutual learning and robust learning in the domain of image recognition. Specifically, we first employ two GCNs with different learning abilities to construct network architecture. Then we define a new loss function which is a weighted sum of the supervised loss, mutual loss, and robust loss. Finally, we train the network in the paradigm of pseudo-siamese network. Experimental results on three social network benchmark datasets with different levels of noise on labels demonstrate that DMRGCN outperforms the vanilla GCN and several variants in terms of classification accuracy. In particular, under the two conditions of clean labels and noisy labels, the accuracy of node classification obtained by our proposed DMRGCN can be 3.05% and 6.44% higher than that of the vanilla GCN, respectively.

Tobler’s first law in the digital economy based on the Internet of Everything: Priority issues

Introduction. W. Tobler, an American researcher, worded the first law of geographyby linking the location with the interaction among economic agents. This produced spatial analysis and shaped spatial interactions in the pre-Internet era. However, the development of information and communication technologies doubted the significance of location and spatial distance in the emerging digital economy based on the Internet of People. Scientific discussions concerning this issue and empirical studies established that the modern digital economy, including the one derived from the Internet of Things, partially depends on the actual geographical space. In the next decade, further development of technologiescould lead to a digital economy based on the Internet of Everything. Currently, there is no data about how spatially distributed economic agents will interact in the future digital world. Purpose. The article formulates the questions concerningthe Tobler’s first law with their future answers to confirm or refute the hypothesis about the spatial interactions in the digital economy based on the Internet of Everything. Results. To achieve this goal, the Tobler’s first law is examined for the first time in the Russian scientific literature. Key implications and ambiguities of this law are formulated. The spatial interaction concept is clarified. The Internet of Things and the Internet of Everything are briefly described. The future digital economy is outlined, and the key questions are formulated.Conclusion. Seven questions are given with their answers to assist us in identifying spatial interactions from different perspectives by machine-to-machine interaction, coverage of all types of economic activity, geographical proximity to the end user, gravitational interaction, economic-geographical location, economic regionalization, and spatial autocorrelation.

Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People

Social networks are an essential component of the Internet of People (IoP) and play an important role in stimulating interactive communication among people. Graph convolutional networks provide methods for social network analysis with its impressive performance in semi-supervised node classification. However, the existing methods are based on the assumption of balanced data distribution and ignore the imbalanced problem of social networks. In order to extract the valuable information from imbalanced data for decision making, a novel method named minority-weighted graph neural network (mGNN) is presented in this article. It extends imbalanced classification ideas in the traditional machine learning field to graph-structured data to improve the classification performance of graph neural networks. In a node feature aggregation stage, the node membership values among nodes are calculated for minority nodes’ feature aggregation enhancement. In an oversampling stage, the cost-sensitive learning is used to improve edge prediction results of synthetic minority nodes, and further raise their importance. In addition, a Gumbel distribution is adopted as an activation function. The proposed mGNN is evaluated on six social network data sets. Experimental results show that it yields promising results for imbalanced node classification.

Cyber attack risks to construction data management in the fourth industrial revolution era: a case of Gauteng province, South Africa

The 4IR or the digital revolution refers to a collective term for a value chain organizational concepts and technologies that together build the Internet of Things, Internet of people, Cyber-Physical Systems (CPS), Internet of Services and the Internet of Energy. While this digital revolution has helped the construction industry to prevent cost and time overruns and enable efficiency and good work’s quality, it also has disadvantages and risks such as cyberattacks and loss of jobs. This study set out to determine the risks associated with data management (cyberattacks) on construction projects in the fourth industrial revolution era. The research study will address the questions of “what are the cyberattacks risk attributed to construction data management in the fourth revolution era?” and “What are the differences in the opinion of respondents concerning the identified cyberattacks?”. This research work used a quantitative method and gathered information from different construction professionals in the South African construction industry precisely in Gauteng province via a well-structured questionnaire through online platforms. These professionals involve quantity surveyors, architects, civil, mechanical, and electrical engineers practising under a firm, company, organisation, or institution within the Gauteng province, South Africa. Findings revealed that viruses, hacking, and password cracking are the most frequent risk to data management encountered in the construction industry. It was also indicated that construction project stakeholders need a strong knowledge of how attackers operate to address, avoid, and stop the different risks rising when executing a project. The study contributes to the body of knowledge by highlighting the various risks encountered in managing data in the construction industry which will assist professionals in the industry to pay attention to means of mitigating the identified risks. This will keep stakeholders abreast of how simple negligence from their side can deeply affect the project data thereby affecting project delivery. It was concluded based on findings that construction professionals need to avoid the occurrence of these risks to enhance satisfactory project delivery and protect their project information. The study recommended that all construction project parties require full training sessions on risks to data to prevent any types of intrusion into the company’s information system.

RUSSIA IN THE 2020S: ECONOMICS AND LAW FACING CHALLENGES INDUSTRY 4.0

The article is devoted to the analysis of the main direction of the prospective development of the world community-Industry 4.0, as a materialized expression of the fourth scientific and technological revolution, which will be decisive for the prospects for the development of modern civilization for the next 25-30 years, and possibly more. The study is conducted from the perspective of the two most important areas of modern public life-economics and law. Based on this, the purpose of the work is the formation of a comprehensive understanding of the current stage and prospects for the integration of Industry 4.0 into the modern global and domestic social structure, specifically in the prism of its interpretations in the economy and the legal sphere. The economic interpretation of Industry 4.0 today consists of three main hypostases-the introduction of Artificial Intelligence technologies into the functioning of economic actors, the transformation of the modern Internet in the direction of such innovative forms as the Internet of Things (IoT) and the Internet of People (IoP) with the deepening of the integration of economic actors into this virtual environment, formation of a «smart enterprise» through the creation of cyber-physical systems. The legal sphere, in the context of integrating the components of Industry 4.0 into the public environment as a whole, will face a number of challenges, among which, first of all, it is necessary to highlight the need to adapt the political and legal system to new conditions and form adequate responses to emerging trends. Also, already today, in order to predict, a list of those economic and legal problems that society will face in the practice of introducing the above elements into socio-economic practice can be proposed.

Applications of Fog Computing and Internet of Things in Indian Smart Cities

Fog-computing and Internet of Things (IoT) together will work as a robust enabling technology for the success of a Smart city. Hence, the study focuses on the critical success factors based on Fog computing-IoT (FIoT) to bring success in Indian Smart cities by introducing a two-stage neural network that includes structural equation modeling and artificial neural network (SEM-ANN) hybrid approach. The research article introduces a new factor, Fog Computing (FC). IoT is again sub-divided into three more factors like the Internet of People (IoP), Internet of Services (IoS), and Internet of Energy (IoE) as the independent variables. 13 Smart cities and 379 respondents are involved in this study in India. The data analysis is done through two-stage SEM and ANN, which measures linear and non-linear relationships. From SEM output, it is identified that the IoE, Internet of People (IoP), and IoS have some significant positive effects on FIOT. The SEM accepted variables are considered the input for the next layer of ANN analysis that identified IoS as FIOT.

A Queueing-Based Model Performance Evaluation for Internet of People Supported by Fog Computing

Following the Internet of Things (IoT) and the Internet of Space (IoS), we are now approaching IoP (Internet of People), or the Internet of Individuals, with the integration of chips inside people that link to other chips and the Internet. Low latency is required in order to achieve great service quality in these ambient assisted living facilities. Failures, on the other hand, are not tolerated, and assessing the performance of such systems in a real-world setting is difficult. Analytical models may be used to examine these types of systems even in the early phases of design. The performance of aged care monitoring systems is evaluated using an M/M/c/K queuing network. The model enables resource capacity, communication, and service delays to be calibrated. The proposed model was shown to be capable of predicting the system’s MRT (mean response time) and calculating the quantity of resources required to satisfy certain user requirements. To analyze data from IoT solutions, the examined architecture incorporates cloud and fog resources. Different circumstances were analyzed as case studies, with four main characteristics taken into consideration. These case studies look into how cloud and fog resources differ. Simulations were also run to test various routing algorithms with the goal of improving performance metrics. As a result, our study can assist in the development of more sophisticated health monitoring systems without incurring additional costs.

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

Application of Internet of Things in Industry 4.0

Introduction. Industry 4.0 is a concept that is considered a new phase in the Industrial Revolution, closely related to the application of information technologies and the digital transformation of manufacturing. The main purpose is to be created a more holistic and more connected ecosystem, focused on supply chain management in industrial companies. Implementation of solutions in Industry 4.0 is mostly related to the concept of the Internet of Things (IoT). Mass deployment of this type of technology in industrial enterprises is the basis of the so-called Industrial Internet of Things (IIoT). Achieving interoperability in the IIoT requires the combination of two technologies: the Internet of Things and the Internet of People.
 Aim and tasks. This article describes the implementation of the concept of the Internet of Things in industrial enterprises, as a key technology factor for developing Industry 4.0.
 Results. A brief overview of the evolution of industrial production - from the beginning of the Industrial Revolution to the emergence of Industry 4.0 is made. The main principles for implementing Industry 4.0 solutions ensure that the entire production process is computerized. Industry 4.0 solutions are mostly associated with the concept of the Internet of Things (IoT) whose definition and essence are obtained in this article. Based on the various concepts of the IoT are presented solutions that can be used in the industry, namely: in consumer devices in technology used in public organizations in infrastructure applications in industrial applications, also called the Industrial Internet of Things (IIoT). Therefore, we can say that there is a significant potential for improving production processes as regards: optimization of operations, forecasting equipment support, inventory optimization, improving workers' security, shipping chain optimization, etc.
 Conclusions. The application of the Internet of Things in enterprises is an important and decisive step in the process of their digital transformation and transition to Industry 4.0. The interaction between humans and machines, carried out through Internet technologies, leads to the emergence of the Internet of Everything, which will be a basic concept in industrial production in the coming years. However, the role of man in the production process should not be completely eliminated, but solutions should be sought that support and intellectualize his work.

‘You can't eat data’?: Moving beyond the misconfigured innovations of smart farming

This paper presents a critical examination of smart farming. I follow other critical analyses in recognizing the centrality of innovation processes in generating smart farming products, services, arrangements, and problematic outcomes. I subsequently use insights from critical human geography scholarship on the significance of understanding topological transformations to move beyond interpretations that identify only a narrow range of smart farming problems, such as a lack of coordination or limited uptake by farmers. Instead, I examine a broader set of challenges produced by smart farming developments. The overriding concern, I argue, is that smart farming unfolds via the production of numerous ‘misconfigured innovations.’ Using insights from literature on responsible research and innovation I then probe the stakes of looking beyond the misconfigured innovations of smart farming and discuss how new technologies might come to play a role in producing emancipatory smart farming. I pay attention to research on the ‘internet of people,’ which paints a stark new picture of social life generally, and in particular how rural life might be computed and calculated according to new conceptualizations of sociality and spatiality.

Design Engineering in the Age of Industry 4.0

Abstract Industry 4.0 is based on the digitization of manufacturing industries and has raised the prospect for substantial improvements in productivity, quality, and customer satisfaction. This digital transformation not only affects the way products are manufactured but also creates new opportunities for the design of products, processes, services, and systems. Unlike traditional design practices based on system-centric concepts, design for these new opportunities requires a holistic view of the human (stakeholder), artefact (product), and process (realization) dimensions of the design problem. In this paper we envision a “human-cyber-physical view of the systems realization ecosystem,” termed “Design Engineering 4.0 (DE4.0),” to reconceptualize how cyber and physical technologies can be seamlessly integrated to identify and fulfil customer needs and garner the benefits of Industry 4.0. In this paper, we review the evolution of Engineering Design in response to advances in several strategic areas including smart and connected products, end-to-end digital integration, customization and personalization, data-driven design, digital twins and intelligent design automation, extended supply chains and agile collaboration networks, open innovation, co-creation and crowdsourcing, product servitization and anything-as-a-service, and platformization for the sharing economy. We postulate that DE 4.0 will account for drivers such as Internet of Things, Internet of People, Internet of Services, and Internet of Commerce to deliver on the promise of Industry 4.0 effectively and efficiently. Further, we identify key issues to be addressed in DE 4.0 and engage the design research community on the challenges that the future holds.

Цифрова економіка: сутність, особливості та глобальні тенденції розвитку

The new type of economic management, which begins to be built nowadays and is based on the widespread use of information and communication technologies (ICT), has been named the «digital economy». The use of ICT and digital systems carries great advantages, comprehensively changes the national economies. The transition to digital economy is objective and irrevocable, so one needs to clearly understand this new essence, its features and development tendencies. The publication is aimed at outlining a modern understanding of the essence, determining the features, characteristics and peculiarities of digital economy, as well as defining tendencies in its development in the context of globalization of the world economy. The article explains the importance of ICT in economics, the creation of digital systems, the Internet of people and things, cyberphysical systems, various equipment and devices. The main results of widespread use of ICT, especially the development of the Internet, are formulated. Explanations of digitalization and digital transformation are provided. In general, the consequences of the use of ICT at the intra-organization, meso-economic and macro-economic levels are specified. The essence features of digital economy and the general bases of its emergence with an emphasis on the role of the Internet are distinguished. In this context, a number of characteristics of digital economy are defined. Four major approaches to determining the essence of digital economy (political-economic, technological, resource-factor, entrepreneurial) are substantiated. To disclose the structure of digital economy, its main types are distinguished, including the auxiliary ones (designing the websites, Internet marketing, analytics, etc.). The importance of payment transactions is emphasized. The use of ICT to ensure the activities of industrial enterprises and the creation of «smart» things, cities, infrastructure, etc. are taken into account. It is proposed to consider digital economy as a modernization and a new paradigm for the development of socio-economic systems. In this context, structural, organizational, managerial, institutional-legal, social, spatial features of such an economy are distinguished. The need to study the global aspects of the development of digital economy is emphasized, within terms of which the relevant tendencies are determined, taking into account the development of e-business, industries and market segments, the transformation of digital economy into a source of economic growth and development.

An Energy-Aware, Highly Available, and Fault-Tolerant Method for Reliable IoT Systems

The Internet of Things (IoT) is one of the highly influencing and promising technologies of today’s world, consisting of sensor devices. The internet smoothly changes from an internet of people towards an Internet of Things (IoT), which allows different things and objects to connect wirelessly. Things and objects are grouped into IoT subgroups in the IoT system, which are called clusters, and each cluster is controlled by a central authority and checked by the broker’s help. A concept of keeping backup data is used to increase the lifespan of IoT subgroups by avoiding re-clustering overhead for smooth transmission of packets and increasing availability concerns. A novel approach is used for the selection of cluster head/broker and backup nodes simultaneously. Cluster head and Backup Storage Point node (BSP) remain the same unless and until the residual power of the broker/cluster head is greater than the threshold energy. A novel Energy Efficient Message scheduling algorithm EAAFTMS (An Energy-Aware Available and Fault-Tolerant System with Message Scheduling in IoT) is incorporated at broker node for smooth transmission of messages. This proposed approach is not only solving availability issues over the wireless network but also proved to be energy efficient by prolonging the battery-powered network lifetime. Simulation results prove EAAFTMS, many folds better than benchmark protocols. This system ensures fault-tolerant and available schemes for IoT systems while stabilizing the energy of the overall system. The results shown prove the effectiveness and efficiency of the proposed system.

ComPath: User Interest Mining in Heterogeneous Signed Social Networks for Internet of People

The Internet of People (IoP) is a human-centric computing paradigm, where the people are not considered merely as end users, but become the center of the computing architecture. The computing model of IoP requires that the system understand the social characters of the users, such as the users’ emotions, personality types, and interests. User interest detection is an important task in IoP. In this article, we propose a user interest detection framework for user interest detection in the context of a signed social network for IoP. First, we propose a new proximity function that measures the similarity between users based on their interests/disinterests with respect to the relative popularity of these interests/disinterests among other users. Second, we propose a greedy community detection algorithm that detects communities of users with common interests with possible overlapping communities using the adaptive clique relaxation technique. Finally, we introduce a novel link prediction algorithm named ComPath that leverages the community affiliation information to predict the unknown links in heterogeneous signed social networks. Experimental results show that ComPath outperforms other computational-based baselines as well as deep-learning-based baselines especially in the cold start phase with only a few training data.

Internet of people enabled framework for evaluating performance loss and resilience of urban critical infrastructures

Critical infrastructures (CIs) such as road networks play a critical role in transporting affected people to hospitals and shelters during disasters. Timely evaluation of road networks’ performance loss and resilience enables emergency management agencies (EMAs) to make quick and optimized decisions on resource allocation to critical road segments and bridges. However, real-time monitoring of road network conditions has been recognized as a challenge during major natural disasters because the affected regions are often inaccessible. This research introduces the Internet of People (IoP) enabled framework to assess road network’s performance loss during disasters and proposes a performance loss rate to evaluate the road network resilience. The framework is illustrated by a case study of Hurricane Florence in Wilmington, North Caroline, USA. Through semantic analysis of road-related social media data, this study identifies the affected roads during the hurricane. To further evaluate the road network resilience, this research utilizes road network analysis to generate a service zone for each main hospital before and after the hurricane. The population-weighted travel time in each service zone are compared before and after the hurricane to indicate the degree of performance loss. The findings show that our IoP enabled framework can effectively support CI protection for building smart and resilient city environments.

From IoT to Future Cyber-Enabled Internet of X and Its Fundamental Issues

As Internet of Things (IoT) is a fascinating paradigm in which all things and objects are connected together, it holds a significant position in fostering intelligent high-level services. However, the future IoT architecture is still under evolution profiting from the overwhelming development of cyberspace and cyber technologies. Based on the traditional physical-based IoT, social-inspired Internet of People (IoP) and brain-abstracted Internet of Thinking (IoTk), an intelligent embryo of cyber-enabled Internet of X (IoX) is being established where all things, entities, people and thinking are interacted seamlessly. In this article, we clearly introduce the cyber-enabled IoX from perspective of both ubiquitous connections and space convergence, and design an architecture with four pillars, namely, things, people, thinking and cyberentities in respective spaces. In addition, we analyze the fundamental issues in IoX development, such as information exploding, link exploding and application exploding from the view of ubiquitous connections, entity exploding and relationship exploding on the basis of space convergence, and service exploding from overall aspects, where potential solutions are discussed at the same time. The intelligent cyber-enabled IoX will be the cornerstone for future techniques and applications, and proves to be the solid foundation for upcoming intelligent and proactive era.

WITHDRAWN: An evolutionary study on the threats, trust, security, and challenges in SIoT (social internet of things)

Abstract Internet of Things (IoT) defined as the meeting point of the smart objects that communicate with another over the internet. The paradigm of the “Internet of people “is overlooked by the term “Internet of things” in recent times. There are enormous devices connected to the internet, and the figure is expected to grow exponentially with time. The social networking of the IoT where the IoT clubs with the concept of social networking which make the people interact with the devices over the internet. Primary importance has to be given for the challenges related to the security and privacy for a better IoTecosystem to be built. Owing to the rising number of challenges in vulnerabilities of the devices in IoT, the minimum resources with assorted technologies along with the deficiency of proper standards naturally pave enough way for many cyber threats. This research article aims to highlight the issues that are more critical in the security of the IoT by an analysis in terms of the essential layers of the IoT and to bring out the future research of the IoT security. Last, the future challenges are discussed for the researchers to think upon and contribute towards resolving those.

Data-Driven Diffusion Recommendation in Online Social Networks for the Internet of People

Recommendation systems are gaining popularity with the proliferation of the Internet of People (IoP). The popularity and use of online social networks facilitate integrating these social relationships with recommender systems under a single framework of IoP. This article proposes a new approach for item recommendation based on the diffusion method that combines user relationships in social networks with user–item relationships derived from the IoP. Especially, a resource redistribution process is explored in the user–object network that gives mass diffusion a higher recommendation accuracy and heat conduct a greater diversity by considering the social degree of users whilst calculating the user degree in the network. A tuning parameter is introduced to adjust the weight of resources that the objects finally receives from users based on their social relationships. Finally, extensive experiments conducted on the real-world datasets which contain friendship relationships, demonstrate the efficiencies of our proposed method in achieving notable performance improvements in terms of the recommendation accuracy, service diversity, and practical dependability.

Supporting food choices in the Internet of People: Automatic detection of diet-related activities and display of real-time interventions via mixed reality headsets

With the emergence of the Internet of People (IoP) and its user-centric applications, novel solutions to the many issues facing today’s societies are to be expected. These problems include unhealthy diets, with obesity and diet-related diseases reaching epidemic proportions. We argue that the proliferation of mixed reality (MR) headsets as next generation primary interfaces provides promising alternatives to contemporary digital solutions in the context of diet tracking and interventions. Concretely, we propose the use of MR headset-mounted cameras for computer vision (CV) based detection of diet-related activities and the consequential display of visual real-time interventions to support healthy food choices. We provide an integrative framework and results from a technical feasibility as well as an impact study conducted in a vending machine (VM) setting. We conclude that current neural networks already enable accurate food item detection in real-world environments. Moreover, our user study suggests that real-time interventions significantly improve beverage (reduction of sugar and energy intake) as well as food choices (reduction of saturated fat). We discuss the results, learnings, and limitations and provide an overview of further technology- and intervention-related avenues of research required by developing an MR-based user support system for healthy food choices.