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Fog computing is a paradigm that works in tandem with cloud computing. The emergence of fog computing has boosted cloud-based computation, especially in the case of delay-sensitive tasks, as the fog is situated closer to end devices such as sensors that generate data. While scheduling tasks, the fundamental issue is allocating resources to the fog nodes. With the ever-growing demands of the industry, there is a constant need for gateways for efficient task offloading and resource allocation, for improving the Quality of Service (QoS) parameters. This paper focuses on the smart gateways to enhance QoS and proposes a smart gateway framework for delay-sensitive and computation-intensive tasks. The proposed framework has been divided into two phases: task scheduling and task offloading. For the task scheduling phase, a dynamic priority-aware task scheduling algorithm (DP-TSA) is proposed to schedule the incoming task based on their priorities. A Memoization based Best-Fit approach (MBFA) algorithm is proposed to offload the task to the selected computational node for the task offloading phase. The proposed framework has been simulated and compared with the traditional baseline algorithms in different test case scenarios. The results show that the proposed framework not only optimized latency and throughput but also reduced energy consumption and was scalable as against the traditional algorithms. [ABSTRACT FROM AUTHOR]

In order to study the operating characteristics of variable speed constant frequency wind turbine under different working conditions and the monitoring system of wind turbine. In this paper, the simulation model of each component system of wind turbine is established by MATLAB/Simulink module, and the influence law of different wind speed and ground fault types on the output power of wind turbine is studied. The active power of wind turbines under different short-circuit fault types is compared. At the same time, in order to realize real-time monitoring of wind turbine speed and output power, an online monitoring system for wind turbine operation based on industrial Internet of Things is proposed, and the composition and operation characteristics of this remote monitoring system are given. The practical application shows that the on-line monitoring system can accurately and remotely monitor the running status of the wind turbine and avoid the unstable running of the wind turbine. The research conclusions of this paper can provide reference for the design and construction of wind turbines and the operation of connecting to the power grid. [ABSTRACT FROM AUTHOR]

In this paper, a new timing synchronization algorithm for the main synchronous signal (PSS) is proposed for the risk identification of cross-border e-commerce in the Internet of things, aiming at the problems of poor performance of anti frequency bias and high computational complexity of the improved PSS timing synchronization algorithm. Based on the piecewise correlation algorithm, the normalized frequency deviation of PSS sequence is preset. The segmented ones are pre stored at the terminal by using the conjugate symmetry of PSS sequence. The fast correlation of each segment correlation window is realized by combining convolution and overlapping reservation block method. Then, the threshold judgment is made after the time delay accumulation of the correlation values of the segments is made, so as to complete the joint detection of timing synchronization and coarse frequency deviation. The simulation results show that the algorithm can improve the performance of the system anti frequency offset effectively, reduce the complexity of the calculation and show that the timing synchronization conditions of the Internet can be satisfied. At the same time, under the background of the current development of cross-border logistics, this paper reviews the current research status of Transnational E-commerce logistics and Transnational E-commerce logistics risk. By comparing the advantages and disadvantages of various risk assessment methods, neural network and genetic algorithm are selected as the basic risk assessment methods in this paper. Based on the improved PSS timing synchronization algorithm and the Internet of things, the risk indicators of e-commerce logistics transnational will be selected from five risk dimensions: platform risk, customs clearance risk, organizational risk, process risk and environmental risk. Through the comprehensive literature and expert's opinion, the logistics risk assessment index system of cross-border e-commerce is established. [ABSTRACT FROM AUTHOR]

With the advent of the era of Industry 4.0, big data and the Internet of Things came into being. The rise of big data and the Internet of Things has had an impact on all walks of life. The development of big data and the Internet of Things provides an opportunity for the evolution of street form. With the rapid development of smart cities, the street shape is also changing more and more towards the direction of intelligence. Taking Chunxi Road in Chengdu as an example, this paper studies the generation mechanism of street morphology. The factors influencing the generation of street form are divided into two categories: (1) legal factors. (2) Architectural design factors. By building a model, the article quantitatively analyzes the impact of these two types of factors on street form. At the same time, the article conducts an in-depth study on the evolution of ancient Chinese street morphology. The ancient Chinese street form is divided into four stages, namely the Tang and Song Dynasties, the Yuan, Ming and Qing Dynasties, the Late Qing Dynasty, and the founding of New China. The influencing factors of street shape are divided into four categories, namely, the influence of commodity economy, the influence of foreign culture, the influence of building materials, and the influence of traffic. This paper elaborates the influence mechanism of these four factors on the street form in China. The article finally puts forward the development trend of my country's future street form, and points out that the evolution of my country's street form will develop in the direction of intelligence, and intelligent transportation, intelligent garbage classification system, etc. will be applied. The number of brick-and-mortar stores in my country's street layout may decrease. [ABSTRACT FROM AUTHOR]

In modern social APP interface design, how to effectively improve the corporate image and create the connotation of corporate culture is a significant key problem. With the emergence of APP, a growing number of people use them, increasing communication energy usage and slowing network operation. To improve app compatibility and speed, it is necessary to combine it with the most advanced and dependable technology, such as ZigBee, which is regarded as the best solution for wireless sensor networks. The ZigBee protocol is primarily used to incorporate working and data transmission in wireless sensor networks that are based on ZigBee technology. As a result, incorporating ZigBee technology into APP interface design in the Internet of Things (IoT) domain can significantly improve brand APP interface design's network operation efficiency. This paper presents a novel approach to enhance the performance and corporate image of brand mobile applications (APPs) by integrating ZigBee technology. The primary objective is to improve the operating efficiency and user experience of the brand APPs. The study involves a comparison between 10 brand APPs that have not integrated ZigBee technology and 10 brand APPs that have adopted ZigBee technology. The experimental results indicate that the operating efficiency of the brand APPs incorporating ZigBee technology is 97%, while the efficiency of the brand APPs without ZigBee technology is 85%, resulting in a notable difference of 12%. To assess the effectiveness of ZigBee technology integration, the study conducted experiments with 100 users, randomly assigned to interact with both types of brand APPs. The user feedback and observations revealed that brand APPs integrated with ZigBee technology exhibit significantly higher operating efficiency, contributing to a 12% improvement over their counterparts lacking ZigBee integration. Moreover, 90 out of 100 users reported a preference for the brand APPs integrated with ZigBee technology due to their superior user experience. The integration of ZigBee technology in brand APPs not only enhances the user experience but also contributes to the improvement of the company's corporate image. Adopting ZigBee technology in brand APPs is a valuable strategy that can facilitate the long-term development and success of the company. [ABSTRACT FROM AUTHOR]

Sustainability is one of the main goals to pursue in several aspects of everyday life; the recent energy shortage and the price raise worsen this problem, especially in the management of energy in buildings. As the Internet of Things (IoT) is an assessed computing paradigm able to capture meaningful data from the field and send them to cloud infrastructures, other approaches are also enabled, namely model-based approaches. These methods can be used to predict functional and non-functional properties of Building Energy Management Systems (BEMS) before setting up them. This paper aims at bridging the gap between model-based approaches and physical realizations of sensing and small computing devices. Through an integrated approach, able to exploit the power of different dialects of Petri Nets, this paper proposes a methodology for the early evaluation of BEMS properties as well as the automatic generation of IoT controllers. [ABSTRACT FROM AUTHOR]

In order to improve the security and performance of the oral English instant translation model, this paper optimizes the instant translation model through the Internet of Things (IoT) security technology and deep learning technology. In this paper, the real-time translation model based on deep learning and IoT technology is analyzed in detail to show the application of these two technologies in the real-time translation model, and the related information security issues are discussed. Meanwhile, this paper proposes a method combining deep learning network and IoT technology to further improve the security of instant translation model. The experimental results show that under the optimized model, the parameter upload time is 60 seconds, the aggregation calculation time is 6.5 seconds, and the authentication time is 7.5 seconds. Moreover, the average recognition accuracy of the optimized model reaches 93.1%, and it is superior to the traditional machine translation method in accuracy and real-time, which has wide practical value and application prospects. Therefore, the research has certain reference significance for improving the security of the English corpus oral instant translation model. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) services are directly deployed on resource-constrained smart devices. Smart devices are characteristic by spatial and temporal constraints and energy limitations. A single IoT service cannot meet the complex requirements of users, so multiple IoT services need to be combined to provide services to users. As more and more smart devices are deployed in IoT, how to select IoT services with lower energy consumption and better quality of service (QoS) for service composition becomes a challenging problem. Combined with the characteristics that the data information of IoT is closely related to geographical location, the GeoHash algorithm is used to locally screen services based on geographical location, so as to narrow the range of candidate services. For smart devices with energy constraints, this paper proposes a combined optimization model. The model considers not only the transmission energy consumption and switching energy consumption, but also the execution energy consumption when the device provides services. In order to balance QoS attributes and energy consumption, the composition problem is regarded as a multi-objective optimization problem and solved using a genetic algorithm (GA). The simulation results show that service composition scheme selected by this service composition optimization model has lower energy consumption and higher service quality. [ABSTRACT FROM AUTHOR]

Today, Internet of Things (IoT) has provided intelligent interactions between sensors, smart devices, actuators, and cloud-based applications to ease human life. Currently, IoT-based connectivity management systems use computer-assisted learning methods to increase learning level and better understanding of the curriculums for students in universities, schools and research centers. On the other hand, virtual connectivity management systems are applied to facilitate teaching and learning methods under taken of pandemic effects. Because, data mining methods have important effect to enhancement and navigate IoT-based connectivity management systems, this paper presents a technical analysis on Artificial Intelligence (AI) approaches for connectivity management systems in IoT environments. This paper provides a comprehensive perspective on vehicular communication systems, Internet of Vehicles (IoV) methods and Vehicular Ad Hoc Network (VANET) environments that have evaluated using machine learning, fuzzy logic and intelligent algorithms. Also, applied evaluation metrics to predict and detect efficient connectivity methods, succeed learning models and enhancement of IoT-based connectivity management systems are discussed and analyzed for existing AI approaches. Finally, new research directions and emerging challenges are outlined to improve the performance of advanced IoT-based connectivity management systems. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT), mobile devices are playing an increasingly important role in our daily lives. There are various malware threats present in these mobile devices, which can steal users' personal information. Some malware exploits Inter-Component Communication (ICC) to execute malicious activities for unauthorized data access and system control, enabling communication between different components within an app and between different apps. In this paper, we propose an Adaptive Transformer-based malware framework (named AdaTrans) that combines sensitive Application Programming Interface (API)- and ICC-related features. The framework first extracts sensitive function call subgraphs (SFCS) to reflect the caller-callee relationships, and then utilizes ICC interactions to reveal hidden communication patterns in malicious activities. Moreover, we propose a novel adaptive Transformer model to detect malicious behaviors. We evaluate our framework on real-world datasets and demonstrate that AdaTrans consistently outperforms other existing state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Red culture was born in the revolutionary war era. It contains a rich revolutionary spirit and profound historical and cultural connotations. It plays a pivotal role in education, politics, economy and society, and has a crucial influence in all aspects. In this paper, based on the analysis of big data and IoT-related theories, the current issues of red culture dissemination and influence development are obtained through interviews and questionnaire surveys. In terms of red culture dissemination channels, based on big data, solutions such as establishing a digital red culture database, pinpointing demand groups and combining recommendation algorithms are proposed to improve the speed and impact of dissemination. The coordinated red culture has been improved in terms of dissemination speed, dissemination efficiency, depth of influence, number of influencers, and breadth of influence. The innovation of this paper is to study the development of red culture through big data and Internet of things technology, explore the problems existing in the development of red culture from a new perspective, and put forward corresponding strategies, which is of great innovation. [ABSTRACT FROM AUTHOR]

Trust is essential in the digital world. It is a critical task to build digital trust for the ongoing digital engineering transformation. Aiming at developing a blockchain-based digital trust mechanism for Cloud Manufacturing or Manufacturing-as-a-Service (MaaS), in this paper, we use the manufacturing of low dead space (LDS) medical syringes through Cloud Manufacturing as a motivating scenario to develop a basic framework. To meet the need of optimally saving COVID-19 vaccine doses to save more lives, the medical device manufacturing community needs to make a swift move to meet the surged need for LDS syringes. Cloud Manufacturing is a form of emerging Digital Manufacturing facilitated with Cloud/Edge Computing, the Internet of Things, and other digital technologies. Cloud manufacturing allows quickly establishing a digital virtual enterprise that pools together various manufacturing resources worldwide to meet the surged needs of products and save cost and time. Trusting the product quality and safety is a significant challenge when using Cloud Manufacturing to manufacture the products. This paper proposes a schema of blockchain-based digital trust mechanisms with examples of using Cloud Manufacturing of medical LDS syringes for the urgent needs of catering COVID-19 vaccination. [ABSTRACT FROM AUTHOR]

Background: In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network nodes in IoT healthcare are vulnerable to an increased level of security threats., Objective: This paper introduces a secure Electronic Health Record (EHR) framework with a focus on IoT., Methods: Initially, the IoT sensor nodes are designated as registered patients and undergo initialization. Subsequently, a trust evaluation is conducted, and the clustering of trusted nodes is achieved through the application of Tasmanian Devil Optimization (STD-TDO) utilizing the Student's T-Distribution. Utilizing the Transposition Cipher-Squared random number generator-based-Elliptic Curve Cryptography (TCS-ECC), the clustered nodes encrypt four types of sensed patient data. The resulting encrypted data undergoes hashing and is subsequently added to the blockchain. This configuration functions as a network, actively monitored to detect any external attacks. To accomplish this, a feature reputation score is calculated for the network's features. This score is then input into the Swish Beta activated-Recurrent Neural Network (SB-RNN) model to classify potential attacks. The latest transactions on the blockchain are scrutinized using the Neutrosophic Vague Set Fuzzy (NVS-Fu) algorithm to identify any double-spending attacks on non-compromised nodes. Finally, genuine nodes are granted permission to decrypt medical records., Results: In the experimental analysis, the performance of the proposed methods was compared to existing models. The results demonstrated that the suggested approach significantly increased the security level to 98%, reduced attack detection time to 1300 ms, and maximized accuracy to 98%. Furthermore, a comprehensive comparative analysis affirmed the reliability of the proposed model across all metrics., Conclusion: The proposed healthcare framework's efficiency is proved by the experimental evaluation.

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices. [ABSTRACT FROM AUTHOR]

Transformational advances in environmental sensing have been made possible by the convergence of Cloud Computing and the Internet of Things (IoT). The potential for these technologies is to work together and improve environmental monitoring data analytics. Integrating IoT in the cloud creates a robust environment for advanced analytics by overcoming previous hurdles in data granularity, real-time monitoring, and geographical coverage. These developments aren't without their own set of difficulties, however, including data security, interoperability, and ethical concerns. This paper explores integrating Cloud IoT with environmental sensing. The paper addresses these issues and emphasises the need to establish ethical data-gathering methods, implement standardised communication protocols, and address privacy concerns. The study concludes by examining the challenges, concerns, and potential of integrating Cloud IoT. The integration of Cloud Computing and IoT not only transforms environmental sensing but also provides a solid foundation for collaborative research, data-driven decision-making, and environmentally conscious management. [ABSTRACT FROM AUTHOR]

In the wake of the wide promotion of 5G network, the era of super-high-speed networks and the Internet of Everything is approaching. Combining digital technologies led by 5G with landscape architecture has become an important way for the sustainable development of garden ecology. In order to achieve refined management of gardens and improve the accuracy and consistency of garden environmental data monitoring, this study constructs a new IoT sensor multi data fusion algorithm model. Considering the high redundancy and large error data collected by multiple sensors, this paper proposes a multi data fusion algorithm based on adaptive trust estimation and improved D-S evidence theory. The experimental data demonstrates that matched with IGA-BP, algorithm in this paper obtained the largest fitness value and the fastest convergence speed in three sensor application scenarios with different numbers of nodes. The lowest values were obtained in terms of unit energy consumption and network latency indicators. In the monitoring experiment for environmental data of landscape architecture, the algorithm obtained lower relative error and mean square error than IGA-BP in four environmental parameters of temperature, humidity, light intensity and carbon dioxide concentration. Therefore, the algorithm is effective in real-time monitoring of landscape garden environmental data, and can provide decision-making data for garden management as a reference. [ABSTRACT FROM AUTHOR]

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters. [ABSTRACT FROM AUTHOR]

In order to improve the early warning effect of equipment abnormal state and shorten the early warning time, this paper designs an early warning method of laboratory equipment abnormal state based on the Internet of things and running big data. Collect the running status data of laboratory equipment in the environment of Internet of things, and implement dimension reduction processing on the collected running status data. After the dimensionality reduction, extract the abnormal characteristics of big data of laboratory equipment running. On the basis of iterative update, the real-time feature analysis results are compared with the abnormal feature set, and the early warning response program is started according to the abnormal. According to the experimental results, the maximum false alarm rate of this method is only 1.34%, and the abnormal state response is always kept below 4.0 s when applied, which fully proves that this method effectively realizes the design expectation. [ABSTRACT FROM AUTHOR]

The well-known Radio Frequency Identification (RFID) technology is now evolving its applications: indeed, at the industrial level, it was firstly conceived at the pallet/case level for enhancing productivity and accuracy of logistics processes, while at present the focus is shifting towards item-level tagging applications for also managing in-store processes, including the selling experience. Due to a more technological customer, to the spread of e-commerce and the need of omnichannel sales availability, also Fast-Moving Consumer Goods (FMCG) are subject to an evolution of the way of selling, enabled by an item-level RFID tagging. Since these technologies are connected to the fourth industrial revolution, this novelty is referred to as Retail 4.0, which is still an uncommon term among the scientific community. For this reason, this paper makes an attempt to define the state-of-the-art of research and industrial projects on this topic, and it represents the first study investigating the Retail 4.0 from a practical side, closing the scientific gap regarding this issue. Eleven documents and fourteen projects focusing on item-level RFID tagging of FMCGs are examined and categorized according to a pre-existing use cases framework, and the enabled scenarios under a Retail 4.0 perspective are identified and defined. Inventory and Life-Cycle Management related use cases turned out to be the most investigated (7 scientific documents and 12 projects), while the User Experience was only considered at the industrial level (12 projects). From both sides, the Product Integrity was not perceived as relevant (2 articles and 6 projects); similarly, scarce attention was paid to the sustainability functionality of packaging with a total of 1 scientific paper and 5 projects. [ABSTRACT FROM AUTHOR]

With the rapid development of technologies such as cloud computing, big data, and the Internet of Things, the scale of data continues to grow. The recommendation system has become one of the important intelligent software to help users make decisions. The recommendation model based on user rating data is widely studied and applied, but the data sparsity problem and the cold start problem seriously affect the recommendation quality. In this paper, Multi-view Hybrid Recommendation Model (MHRM) based on deep learning is proposed. First, we use WLDA (an improved Latent Dirichlet Allocation method) to extract the vector representation of user review text, and then apply LSTM to contextual semantic level user review sentiment analysis. At the same time, the emotion fusion method based on user score embedding is proposed. The problems such as deviations between the user score and actual interest preference, and unbalanced distribution of the score level are solved. This paper has been tested on Amazon product data and compared with various classic recommendation algorithms, using Mean Absolute Error (MAE), hit rate and standardized discount cumulative return for performance evaluation. The experimental results show that the prediction of the MHRM proposed in this paper on the 7 recommendation data and the TopN recommendation index have been significantly improved. [ABSTRACT FROM AUTHOR]

Arrhythmia disorders are the leading cause of death worldwide and are primarily recognized by the patient's irregular cardiac rhythms. Wearable Internet of Things (IoT) devices can reliably measure patients' heart rhythms by producing electrocardiogram (ECG) signals. Due to their non-invasive nature, ECG signals have been frequently employed to detect arrhythmias. The manual procedure, however, takes a long time and is prone to error. Utilizing deep learning models for early automatic identification of cardiac arrhythmias is a preferable approach that will improve diagnosis and therapy. Though ECG analysis using cloud-based methods can perform satisfactorily, they still suffer from security issues. It is essential to provide secure data transmission and storage for IoT medical data because of its significant development in the healthcare system. So, this paper proposes a secure arrhythmia classification system with the help of effective encryption and a deep learning (DL) system. The proposed method mainly involved two phases: ECG signal transmission and arrhythmia disease classification. In the ECG signal transmission phase, the patient's ECG data collected through the IoT sensors is encrypted using the optimal key-based elgamal elliptic curve cryptography (OKEGECC) mechanism, and the encrypted data is securely transmitted to the cloud. After that, in the arrhythmia disease classification phase, the system collects the data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database to perform training. The collected data is preprocessed by applying the continuous wavelet transform (CWT) to improve the quality of the ECG data. Next, the feature extraction is carried out by deformable attention-centered residual network 50 (DARNet-50), and finally, the classification is performed using butterfly-optimized Bi-directional long short-term memory (BOBLSTM). The experimental outcomes showed that the proposed system achieves 99.76% accuracy, which is better than the existing related schemes. [ABSTRACT FROM AUTHOR]

With the medical internet of things, many automated diagnostic models related to eye diseases are easier. The doctors could quickly contrast and compare retina fundus images. The retina image contains a lot of information in the image. The task of detecting diabetic macular edema from retinal images in the healthcare system is difficult because the details in these images are very small. This paper proposed the new model based on the medical internet of things for predicting diabetic macular edema in retina fundus images. The method called DMER (Diabetic Macular Edema in Retina fundus images) to detect diabetic macular edema in retina fundus images based on improving deep residual network being combined with feature pyramid network in the context of the medical internet of things. The DMER method includes the following stages: (i) ResNet101 improved combining with feature pyramid network is used to extract features of the image and obtain the map of these features; (ii) a region proposal network to look for potential anomalies; and (iii) the predicted bounding boxes against the true bounding box by the regression method to certify the capability of macular edema. The MESSIDOR and DIARETDB1 datasets are used for testing with evaluation criteria such as sensitivity, specificity, and accuracy. The accuracy of the DMER method is about 98.08% with MESSIDOR dataset and 98.92% with DIARETDB1 dataset. The results of the method DMER are better than those of the other methods up to the present time with the above datasets. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT), a distributed healthcare system has integrated different medical resources with sensors and actuators. In this research paper proposes a secure healthcare monitoring system for IoT based distributed healthcare systems in the cloud using blockchain and deep learning (DL) mechanisms. The proposed system involved three phases: secure data transmission, data storage, and disease classification system. Initially, the patients are authenticated via blockchain mechanism and their data is encrypted via Effective Key-based Rivest Shamir Adelman (EKRSA), in which the keys are generated using Circle chaotic map and Linear inertia weight-based Honey Badger Optimization (CLHBO) algorithm. Next, in the data storage phase, these encrypted IoT data are securely stored in the cloud using blockchain technology in a distributed manner. Finally, in the disease classification, the data are gathered from the publicly available dataset, and these collected datasets are preprocessed to handle missing values and data normalization. After that, the proposed system applies a radial basis kernel-based linear discriminant analysis (RBKLDA) model to reduce the dimensionality of the dataset. At last, the disease classification is done by optimal parameter-centered bidirectional long short-term memory (OPCBLSTM). The proposed EKRSA system archives maximum throughput of 99.05% and reliability of 99.66, which is superior to the existing approaches. The OPCBLSTM is investigated for its disease classification process, the proposed one achieves 99.64% accuracy with less processing time of 6 ms, which is superior to the existing classifiers. The experimental analysis proves that the system attained better security and classification metrics results than the existing methods. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of information exchange transmission and reduce the transmission response delay, this paper proposes an industrial Internet of things communication information exchange transmission method based on audio information hiding. Firstly, information embedding algorithm is used to extract the communication audio carrier information. Secondly, audio information hiding algorithm is used to realize the hidden embedding of specific information; Then, the information transmission data integration is realized according to the similarity matrix. Finally, the Pareto distribution is used to realize the information transmission data matching, and the audio information hiding is used to realize the data exchange transmission control. The experimental results show that the transmission accuracy of this method is 98.3%, the transmission accuracy is as high as 96.0%, and the response delay is only 16 ms, indicating that this method can improve the transmission effect of industrial Internet of things communication information exchange. [ABSTRACT FROM AUTHOR]

Big data and the idea of the Internet of Everything have become popular technologies at the moment. The application of these two technologies has also promoted the development of various industries. The education and training industry is the focus of the country and the public. Piano education is the cradle of artists. The application of Internet of Things technology to the piano education industry will help to upgrade the industry teaching model. In order to investigate the current situation of online and offline hybrid teaching in piano education, this paper uses the method of questionnaire distribution to conduct a survey on teachers and students in 50 colleges of music and music training institutions. It was found that after using big data and Internet of Things technology to innovate the online and offline hybrid teaching mode of piano education, so the teaching efficiency of teachers and the learning effect of students have been greatly improved. [ABSTRACT FROM AUTHOR]

With the continuous progress of data science, big data technology has been continuously integrated into human daily life. The application of big data technology in the field of education to improve the teaching and learning efficiency of teachers and students has become the focus of current scholars. With the popularization of the concept of the Internet of Everything, the Internet of Things technology has gradually become a hot spot for technology fans. It is widely used in home and body-worn devices. Applying the Internet of Things technology in the education field to obtain student status feedback data can make teachers more accurately understand the goals of student learning. The continuous raging of the epidemic has led to the change of many courses from offline face-to-face teaching to home online teaching, which has brought about a huge change in the traditional offline face-to-face physical education teaching method. Therefore, in order to better protect against the epidemic and prevent the spread of the epidemic more effectively, X Academy responded to the country's call to "suspend classes without teaching, and without stopping learning", so that physical education courses other than other cultural courses will be conducted offline and face-to-face. Teaching has changed to online physical education. This paper firstly defines the concepts of big data, Internet of Things and online physical education teaching, and then conducts research on the current situation and countermeasures of online physical education teaching. Find optimized countermeasures, so that X College can get better teaching results when it conducts online physical education teaching again in the face of similar public health emergencies in the future, allowing students to exercise, improve physical fitness, enhance sports skills, and enhance their own resistance. power goal. The research method of this paper mainly adopts the data analysis method of questionnaire survey and statistics. Through the second survey, it is found that all aspects of online physical education teaching have been improved after adopting the improved strategy. [ABSTRACT FROM AUTHOR]

With the continuous emergence of new technologies and the efficient and convenient development of people's daily life and work, the topic of intelligent building is becoming more and more hot. There are two kinds of data in intelligent building, namely static data and dynamic data. The digital expression of intelligent building is realized through the fusion and interaction of these two kinds of data. The building information model (BIM) stores a large amount of static data, including size, material, color and other data, while the Internet of Things (IoT) collects real-time dynamic data, including temperature, humidity, light and other data, through sensors in every space within the building. The related problems of building data management in the design and operation and maintenance stages mainly include data collection relying on manual work, low data processing efficiency, scattered and disorderly data storage, difficult data interaction, slow speed, and low data visualization. In order to solve these problems, this paper combines the characteristics and advantages of BIM and IoT technology, analyzes the interaction and application value of BIM and IoT technology in the design and operation and maintenance stage of intelligent buildings, and constructs a data management system based on the Internet of things, a 3D visualization technology based on BIM, and an operation and maintenance management platform based on BIM and IoT. The application of BIM and IoT technology in the design and operation and maintenance stage of intelligent buildings has promoted the development of the construction industry. The Internet of Things can provide many functional services, such as real-time monitoring, remote tracking, automatic update, plan management and a series of personalized services and management, to achieve the management, control and detection of various things, and ensure the high-speed and effective operation of the system. The emergence of the building system of modern communication network technology has brought many conveniences to people, and there is still a broad space for development. The combination of new Internet of Things technology and intelligent buildings will certainly be an important development direction. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) is a recent developing technology in the field of smart healthcare. But it is difficult to transfer the patient's health record as a centralized network. So, "blockchain technology" has excellent consideration due to its unique qualities such as decentralized network, openness, irreversible data, and cryptography functions. Blockchain technology depends on cryptography hash techniques for safe transmission. For increased security, it transforms the variable size inputs into a constant length hash result. Current cryptographic hash algorithms with digital signatures are only able to access keys up to a size of 256 bytes and have concerns with single node accessibility. It just uses the bits that serve as the key to access the data. This paper proposes the "Revised Elliptic Curve Cryptography Multi-Signature Scheme" (RECC-MSS) for multinode availability to find the nearest path for secure communications with the medical image as keys. Here, the input image key can be converted into an array of data that can be extended up to 512 bytes of size. The performance of the proposed algorithm is analyzed with other cryptography hash functions like Secure Hashing Algorithms (SHAs) such as "SHA224", "SHA256", "SHA384", "SHA512", "SHA3-224", "SHA3-256", "SHA3-384", "SHA3-512", and "Message Digest5" (MD5) by "One-way ANOVA" test in terms of "accuracy", "throughput" and "time complexity". The proposed scheme with ECC achieved the throughput of 17.07 kilobytes per 200 nano seconds, 93.25% of accuracy, 1.5 nanoseconds latency of signature generation, 1.48 nanoseconds latency of signature verification, 1.5 nanoseconds of time complexity with 128 bytes of hash signature. The RECC-MSS achieved the significance of 0.001 for accuracy and 0.002 for time complexity which are less than 0.05. From the statistical analysis, the proposed algorithm has significantly high accuracy, high throughput and less time complexity than other cryptography hash algorithms. [ABSTRACT FROM AUTHOR]

With the continuous layout of intelligent processing technology in the teaching field, it has become an important trend to apply big data and Internet of Things technology to the teaching management of college students. Big data technology can comprehensively analyze the teaching situation of students through massive data, and then provide the best solution, the current big data technology already has a strong technical foundation, which can provide assistance for students' teaching. The Internet of Things technology can collect personal information of students through sensors and miniature portable devices, and provide the data to the background server for big data analysis. In order to analyze the current status of the informationization of teaching management for college students, this paper first surveys some colleges and institutions through questionnaire surveys, and conducts data analysis on the collected questionnaires, and puts forward a solution based on big data and the Internet of Things based on the data analysis results. Finally, the combination of big data and Internet of Things technology for student teaching data collection, effect evaluation scheme, intelligent arrangement of integrated courses, intelligent recommendation scheme for students' teaching needs, and teaching management information visualization scheme are analyzed, and it is found that the combination of big data and Internet of Things related technologies It can effectively improve the efficiency of teaching management of college students. [ABSTRACT FROM AUTHOR]

Modern network infrastructures are in a constant state of transformation, in large part due to the exponential growth of Internet of Things (IoT) devices. The unique properties of IoT-connected networks, such as heterogeneity and non-standardized protocol, have created critical security holes and network mismanagement. In this paper we propose a new measurement tool, Intrinsic Dimensionality (ID), to aid in analyzing and classifying network traffic. A proxy for dataset complexity, ID can be used to understand the network as a whole, aiding in tasks such as network management and provisioning. We use ID to evaluate several modern network datasets empirically. Showing that, for network and device-level data, generated using IoT methodologies, the ID of the data fits into a low dimensional representation. Additionally we explore network data complexity at the sample level using Local Intrinsic Dimensionality (LID) and propose a novel unsupervised intrusion detection technique, the Weighted Hamming LID Estimator. We show that the algortihm performs better on IoT network datasets than the Autoencoder, KNN, and Isolation Forests. Finally, we propose the use of synthetic data as an additional tool for both network data measurement as well as intrusion detection. Synthetically generated data can aid in building a more robust network dataset, while also helping in downstream tasks such as machine learning based intrusion detection models. We explore the effects of synthetic data on ID measurements, as well as its role in intrusion detection systems. [ABSTRACT FROM AUTHOR]

Power Internet of Things (IoT) is an important support for digital innovation service of power energy internet, covering all aspects of power system. Power IoT security defense system may have customer data information leakage during transmission because of the use of traditional means of isolation. This paper proposes a reliable transmission and application security architecture for power smart IoT based on energy interconnection, aiming to solve the reliable transmission and security authentication problems existing in power systems. The paper first analyzes the security risk of the grid wise IoT system, proposes an effective power IoT security transmission scheme, and evaluates the safe and reliable transmission of the grid wise IoT system. Then designed a safe and reliable transmission of the grid smart IoT system, to deal with the traditional power network transmission security and communication security problems. The final application is in the construction of the security transmission platform of the wisdom park of Shanxi Electric Power Company, which provides the corresponding security protection capability in the power IoT through the situational awareness security measures of each layer, and realizes the reliable transmission and security application of the source network load storage and other links in the power IoT environment. [ABSTRACT FROM AUTHOR]

My country is currently in a new era of industrial revolution and technological development, among which the most representative is the rapid development of information technology. And the action plan has been formulated, and it is pointed out that it is necessary to deeply integrate the real economy and artificial intelligence, the Internet and digital China, as well as the smart society and big data. As the most critical part of community governance, the complexity and difficulty of community sports center governance is relatively high. Therefore, one of the key goals that all sectors of society are currently concerned about is how to build and develop community sports through more advanced technical means center. The innovative governance community sports center must effectively combine and integrate the current advanced technologies such as the Internet of Things and big data, so that the level of community governance can be effectively improved. This paper draws on the academic background of "governance" in the field of public management. Different from social management, "governance" mainly reflects the administrative aspect, the cooperation between the government and all sectors of society, while the traditional social management mainly highlights the state's administrative management. It reflects the concept that the government's management power is inviolable and unshareable, which is also very different from the development direction of the gradual liberalization of the administrative power advocated by the Chinese government. Therefore, the effective way to solve various social problems at this stage is to change the concept of social management to community governance, and this approach has also been supported and valued by the government in recent years. The research shows that the management efficiency of community sports facilities can be effectively improved by optimizing the community sports governance system, making full use of the convenience brought by information resources, and using its own advanced technology. [ABSTRACT FROM AUTHOR]

With the ever-increasing demand for IoT Devices which enable all objects to connect and exchange information in applications such as healthcare applications, Industry 4.0, smart cities and smart homes, etc. IoT devices play a crucial role in our day-to-day life like homes, offices, healthcare, wearable, and agriculture. With the development of IoT devices, securing device-to-device communication has attracted more and more attention and we need to ensure the privacy and security of data amongst these IoT devices. User authentication has emerged as a major security concern while connecting IoT devices and the cloud. Many authentication schemes like mutual authentication, group authentication have been proposed to ensure only authenticated users and with very high confidence we can rely on the decision-making process. Symmetric key based as well as Asymmetric key-based solutions have been proposed but due to the resource constraint nature of the IoT devices designing lightweight, robust, provably secure authentication schemes is a big challenge. This paper discusses the various authentication techniques designed for low-powered IoT devices and proposes a lightweight authentication scheme for IoT. [ABSTRACT FROM AUTHOR]

Exploiting dynamic spatial and temporal features of location information for robot modeling is of great importance in many real applications. It has gained increasing attention in the era of the Internet of Things (IoT). However, successful modeling and accurate localization for robot in indoor environment is still a challenge, where the environment factors are complex and unpredictable, such as signal noise, obstacles and spare fingerprints. Existing studies usually employ data driven and learning based models to capture spatial and temporal features for robot location estimation, modeling dynamics of robot and make robot decision. However, the modeling and localization performance is not satisfied. In this paper, to address above challenges, a novel deep learning framework called multi-faceted deep learning based dynamics modeling and robot localization learning (DMLoc) method is proposed. Specifically, a localization attention module is designed to capture the features from original fingerprints and optimized fingerprints information. Then, a multi-faceted localization module is proposed, which integrates extraction model and optimized model with long short-term memory (LSTM) and gate recurrent unit (GRU). Moreover, a multi-feature fusion layer is designed to fuse the extracted features and generate localization results. Extensive simulation results show the efficiency of the proposed DMLoc. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively. [ABSTRACT FROM AUTHOR]

Nowadays, the number and types of IoT devices are increasing rapidly, which leads to an expansion in the attack surface of this kind of device. Besides, the number of Botnet malware on IoT devices also grows with a lot of new variants. This context leads to an urgent demand for an effective solution in detecting new variants of IoT Botnet malware. There have been many studies focusing on IoT Botnet malware detection using static and dynamic analysis. In particular, the combination of the dynamic method with machine learning has shown outstanding advantages to detect IoT Botnet variants. However, the preprocessing of behavioral data originated from malware is still complicated, and the number of input vector dimensions of the machine learning model is still huge. In addition, these models also consume a lot of resources and have limited detection capabilities. Besides, dynamic analysis studies based on system calls mostly use call frequency characteristics and have not effectively exploited IoT Botnet malware's life cycle characteristics. In this paper, we propose the Directed System Call Graph (DSCG) feature to sequentially structure the system calls. This DSCG graph will be vectorized and used as an input for building a malware analysis model based on popular machine learning classifiers such as KNN, SVM, Decision Tree, etc. Experiments on the datasets demonstrate that the features extracted from this graph have low complexity but still ensure high accuracy in detecting IoT Botnets, especially with newly emerged IoT Botnet families. The proposed model was evaluated with ACC = 98.01 % , TPR = 97.93 % , FPR = 1.5 % , AUC = 0.9961 on a dataset of 5023 IoT Botnets and 3888 benign samples. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) devices have limited resources and are vulnerable to attacks, so optimizing their network topology to resist random failures and malicious attacks has become a key issue. The scale-free network model has strong resistance to random attacks, but it is very vulnerable to malicious attacks. The existing studies mostly adopt heuristic algorithms to optimize the ability of scale-free networks to resist malicious attacks, but their high computational cost cannot meet the timeliness requirements of the real IoT. Therefore, this paper proposes an intelligent topology robustness optimization model based on a graph convolutional network (ROGCN). The model extracts the onion-like structural features of the highly robust network topology from the data set through supervised learning, and on this basis, different search strategies are designed to meet the needs of different IoT scenarios. The extensive experimental results demonstrate that ROGCN can more effectively improve the robustness of scale-free IoT networks against malicious attacks compared to two existing heuristic algorithms, with a lower computational cost. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things technology, its application value in various fields has been widely concerned, especially in enterprise supply chain management. By connecting the physical world with the digital world, IoT technology helps companies monitor and manage all aspects of the supply chain in real time, thereby increasing efficiency, reducing costs, improving risk management, and increasing customer satisfaction. This paper studies the application and value of the Internet of Things in enterprise supply chain management in detail, first introduces the basic concept of the Internet of Things and its role in supply chain management, then builds an enterprise supply chain management model based on the Internet of Things, and analyzes the specific application of the Internet of Things in each link of the supply chain. Then, using relevant data and formulas to simulate and analyze the specific value of IoT technology in improving supply chain efficiency, reducing cost and improving risk management, the research results are summarized, and suggestions are put forward on how to better apply IoT technology. The results show that the Internet of Things technology provides important support for the optimization of enterprise supply chain management and helps enterprises to improve their competitiveness. [ABSTRACT FROM AUTHOR]

Traditional body temperature monitoring technology has some problems, such as inaccurate manual reading, unable to monitor continuously for a long time, and difficult data storage and analysis. To solve these problems, this paper designs an application scenario of Internet of Things in body temperature monitoring, which integrates Internet of Things, network communication, network programming and other technologies, designs the overall architecture, topology structure and IP address allocation scheme of the system, and realizes real-time acquisition, transmission, storage, analysis, and display functions based on the Packet Tracer simulation platform. The performance evaluation and optimization are also carried out. The system realizes wireless monitoring and analysis of the body temperature data collected by the temperature sensor through the home gateway and single chip microcomputer and other devices. After testing, the designed system is stable, which provides a solution for the application of Internet of Things in body temperature monitoring. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) necessitates secure communication and high availability among objects at the network edge to ensure reliable object-to-object transactions. In the IoT networks, despite resource limitations, especially at the edge of the network, the potential for error is high. Therefore, a mechanism to increase the reliability, lifetime, and stability of the network is necessary. In this paper, we introduce a trust evaluation framework based on a reliability-based friendly relationship method in IoT networks. We present a conceptual trust model that captures the overall performance of the IoT social network based on parameters such as nodes’ communication history experiences. Trust in the IoT network is built upon a harmonious communication environment that aligns with the trustworthiness of each object and its ability to maintain continuous interactions. We propose an empirical Trust Indicator (TI) that captures individual agents’ experiences in IoT groups, considering the results of system executions, current experience values, and timestamps of interactions. Mathematical models are developed to analyze the dynamics of trust, including trust increase through increased reliability and collaborative interactions and trust decay due to non-cooperative interactions and lack of communication. The model parameters in IoT groups through simulation show that in this system based on the level of reliability and its increase or decrease, its direct effect can be evaluated by quantitative measurement of mean time to failure (MTTF), which is a measure of devices trust and the network itself. [ABSTRACT FROM AUTHOR]

Routing protocols for Internet of Things (IoT) play a major role in the performance of the network. The standard Routing Protocol for Low-Power and Lossy Networks (RPL) suffers from a number of limitations including congestion of higher-level nodes and unbalanced topology. This paper proposes a novel Objective Function called Load Balanced Minimum Rank with Hysteresis Objective Function (LB_MRHOF), which assigns child nodes to the most suitable parent in the topology. The Objective Function utilizes a weight of the Expected Transmission Count (ETX) and number of children to calculate the Composite ETX and Number of Children (CENOC) which estimates the load on each node. The attained CENOC is used to select the optimum parent for each node in the topology, where nodes with high CENOC are avoided in the parent selection process. The proposed Objective Function has been evaluated under random and hierarchical network topologies. In addition, the evaluation has investigated the influence of the number of nodes by testing for small, medium and large-scale networks. Results have shown that the proposed Objective Function outperforms MRHOF, OF_FUZZY and OF-EC in terms of Packet Delivery Ratio (PDR) and reduces nodal hop-count under all tested scenarios, with no compromise in energy consumption. They have also revealed that the best performance achieved by LB_MRHOF is attained under large-scale networks. The resulting network topology which is formed by the proposed Objective Function has shown improved balance and more depth. [ABSTRACT FROM AUTHOR]

The applications of IoT have increased manyfold owing to the declining prices of electronic devices and the growing internet. This study discusses at length the various characteristics, applications, merits, demerits, and problems associated with IoT. The current research work showcases the design and development of application coined as MIAWM: MQTT based IoT Application for Weather Monitoring. The application describes how the MQTT protocol can easily be used to perform efficient routing in IoT. The Application describes how to create a simulation for the MQTT Publisher Subscriber model which provides desired weather data to a remote subscriber anywhere in the world. The dataset used contains information about the temperature, precipitation, and wind speed in Berlin and Germany. This paper puts the various quality of service to experiment by comparing the time taken to service the request. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a source of knowledge about the surrounding environment and people in such an environment. The insights collected by IoT can provide the knowledge needed to improve people's health and overall well-being. Schools are one environment where IoT is scarcely applied, yet, it is expected that this is where children and teenagers spend most of their time. Drawing on previous findings, this paper presents preliminary results from qualitative inquiry investigating how and what IoT-based solutions could support health and well-being in elementary educational settings.

The Semantic Web (Web 3.0) is an advancement of the existing web in which knowledge is given well-defined importance, allowing people and machines to operate better. The Semantic Web is the next step in the evolution of the Web. The semantic web improves online technologies in need of generating, distributing, and linking material. In literature, multiple surveys have been done on the semantic web (Web 3.0), but those surveys are limited to some specific topics. According to the best of our understanding, none of the surveys provides a comprehensive study about the applications, challenges, and future of the semantic web along with its relationship with the Internet of things (IoT). The previous surveys focused on the Web 3.0 without touching on applications or challenges or focused on only the application prospect of the web 3.0, focused on the just the challenges, or focused on web 3.0 relationship with either internet of things or knowledge graphs but failed to touch the other important factors i.e., failed to provide comprehensive web 3.0 survey. This survey paper covers the gaps created from the previous survey papers in the same field and provides a comprehensive survey about web 3.0, a comparison between web 1.0, 2.0, and 3.0, the study of application and challenges in web 3.0, the relationship between web 3.0 with IoT and knowledge graph. Moreover, it focuses on the evolution of the web, and semantic web along with an explanation of the various layers, ontology tools, and semantic web tools with their comparison and semantic web service search. Despite all the shortcomings and challenges, the semantic web is moving in the right direction, and it is the future of the web. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) has significantly influenced day-to-day life in large industrial systems. The Internet of Things (IoT) offers a platform for information systems to integrate effectively with network servers. In contrast, cyber threats are becoming critical, especially for IoT servers. A strong strategy must be in place to protect the network system from multiple attacks. In order to detect malicious behaviors that deteriorate network performance, an intrusion detection system (IDS) is crucial. An IDS use a detection method to monitor network activity to alert IoT users regularly. This paper proposes a novel IDS for IoT using log-sigmoid kernel principal component analysis (LSK-PCA) and activation updated deep feed-forward neural network (AU-DFFNN) based dimensionality reduction (DR) and classification technique. Initially, the input data is taken from the NSLKDD dataset and undergoes pre-processing. Afterwards, attribute extraction is carried out, followed by Fisher's Yates Adapted Golden Eagle Optimizer (FY-GEO) based feature selection. Then, DR of the feature selected data is done using the LSK-PCA model. Finally, the reduced dataset is given as an input to the classifier for classifying the data as attacked and normal data. As a final point, experimental analysis is performed using performance metrics like precision (PR), recall (RC), f-score (FS), accuracy (AC), false alarm rate (FAR) and computational time (CT). The results proved that the proposed work detects intrusion effectively compared to state-of-art techniques. [ABSTRACT FROM AUTHOR]

Integrating Internet of Things (IoT) sensor data from heterogeneous sources with domain knowledge and context information in real-time is a challenging task in IoT healthcare data management applications that can be solved with semantics. Existing IoT platforms often have issues with preserving the privacy of patient data. Moreover, configuring and managing context-aware stream processing queries in semantic IoT platforms requires much manual, labor-intensive effort. Generic queries can deal with context changes but often lead to performance issues caused by the need for expressive real-time semantic reasoning. In addition, query window parameters are part of the manual configuration and cannot be made context-dependent. To tackle these problems, this paper presents DIVIDE, a component for a semantic IoT platform that adaptively derives and manages the queries of the platform's stream processing components in a context-aware and scalable manner, and that enables privacy by design. By performing semantic reasoning to derive the queries when context changes are observed, their real-time evaluation does require any reasoning. The results of an evaluation on a homecare monitoring use case demonstrate how activity detection queries derived with DIVIDE can be evaluated in on average less than 3.7 seconds and can therefore successfully run on low-end IoT devices. [ABSTRACT FROM AUTHOR]

As an important part of digital building, building internet of things (BIoT) plays a positive role in promoting the construction of smart cities. Existing schemes utilize blockchain to achieve trusted data storage in BIoT. However, the full-copy storage mechanism of blockchain and the management requirements of massive data have brought computing and storage challenges to edge nodes with limited resources. Therefore, a data management scheme for BIoT based on blockchain sharding is proposed. The scheme proposes a hybrid storage mechanism, which uses inter-planetary file system (IPFS) to ensure the integrity and availability of data outside the chain, and reduces the storage overhead of edge nodes. Based on the hybrid storage mechanism, the sharding algorithm is designed to divide the blockchain into multiple shards, and the storage overhead and computing overhead are offloaded to each shard, which effectively balances the computing and storage overhead of edge nodes. Finally, comparative analysis was made with existing schemes, and effectiveness of proposed scheme was verified from the perspectives of storage overhead, computation overhead, access delay and throughput. Results show that proposed scheme can effectively reduce storage overhead and computing overhead of edge nodes in BIoT scenario. [ABSTRACT FROM AUTHOR]

The modern network social mode accelerates the interaction of data. Under the background of the continuous development of big data and artificial intelligence technology, the ability to collect and process data has become one of the core competitiveness of enterprises. A good data management mode can also help enterprises to achieve more information resources, and win more opportunities in the industry competition, so as to obtain more benefits. The standard definition of big data by scholars in recent years is to search and make decisions based on massive data sets, and the data that can be used for analysis is the internal data of big data. The key to big data is not the massive data set, but the method to analyze the data. At present, big data has penetrated into the development and analysis of all industries, for example, the medical industry records the personal diagnostic data of patients with different diseases. Through comparative analysis and decision-making with the diagnostic data of other historical cases, a third-party reference is provided for the treatment of current patients, thereby avoiding misjudgment and misdiagnosis. With the rapid development of electronic components and data science and technology, IoT technology has gradually entered the lives of citizens. The concept of the Internet of Everything is no longer an empty talk. Smart homes have become a must-have smart device for most homes. Other similar smart cities, smart communities, and smart building designs have also begun to adopt IoT technology, and enterprise management and monitoring have also followed the trend. Connect with IoT technology. If an enterprise wants to gain a firm foothold in the industry, it not only needs excellent manufacturing level, but also needs to carry out effective cost management, and manage costs in a more scientific way, which can gain advantages for the company's product prices. Because if the cost management of the enterprise is successful, it can reduce unnecessary waste of funds when the enterprise produces products, thereby driving the overall operating income of the enterprise. Through big data and Internet of Things technology, it can help in all aspects of enterprise management. Combining with the management dilemma of BYD in the era of big data, this paper proposes an enterprise management and monitoring method that combines big data and Internet of Things technology, business opportunity acquisition, business quality monitoring and other aspects have greatly improved. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technologies increasingly integrate unmanned aerial vehicles (UAVs). IoT devices that are becoming more networked produce massive data. The process and memory of this enormous volume of data at local nodes, particularly when utilizing artificial intelligence (AI) algorithms to collect and utilize useful information, have been declared vital issues. In this paper, we introduce UAV computing to solve greater energy consumption, delay difficulties using task offload and clustered approaches, and make cloud computing operations accessible to IoT devices. First, we present a clustering technique to group IoT devices for data transmission. After that, we apply the Q-learning approach to accomplish task offloading and allocate the difficult tasks to UAVs that are not yet fully loaded. The sensor readings from the CHs are then collected using UAV path planning. Furthermore, We use a convolutional neural network (CNN) to achieve UAV route planning. In terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets, the effectiveness of the current study is finally compared with the existing techniques using UAVs. The results showed that the suggested strategy outperformed the current approaches in terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets. Additionally, the proposed technique consumed less energy due to CNN-based route planning and dynamic positioning, which reduced UAV transmits power. Overall, the study concluded that the suggested approach is effective for improving energy-efficient and responsive data transmission in crises. [ABSTRACT FROM AUTHOR]

Low power wide area networks (LPWANs) are made to survive conditions of extensive installation. Technological innovations, including Global Network Operator, Long Range Wide Area Network (LoRaWAN), Narrowband Internet of Things (NB-IoT), Weightless, Sigfox, etc., have adopted LPWANs. LoRaWAN is currently regarded to be one of the most cutting-edge and intriguing technology for the widespread implementation of the IoT. Although LoRaWAN offers the best features that make it fit with Internet - of - things specifications, there are still certain technical issues to overcome, such as link coordination, resource allocation and reliable transmission. In LoRaWAN, End-devices transmit randomized uplink frames to the gateways using un-slotted random-access protocol. This randomness with the restrictions placed on the gateways is a reason that leads to a considerable decline in network performance, in particular downlink frames. In this paper, we propose a new approach to increase Acknowledgement (ACK) messages throughput. The suggested method takes advantage of both class A and class B features to enhance and assist LoRaWAN's reliability by ensuring that an ACK message is sent for every confirmed uplink while retaining the minimum energy level that is utilized by nodes. [ABSTRACT FROM AUTHOR]