Cloud Internet Research Articles

A Study on Big Data Analytics Tools

In this digital era, the emerging trends and technologies such as Internet of things and cloud computing really deals with huge and huge volume of data [1]. [3]Big Data Analytics has been gaining much focus of attention lately as researchers from industry and academia are trying to effectively extract and employ all possible knowledge from the overwhelming amount of data generated and received. Study of these massive data requires many levels to extract knowledge for decision making. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques [2]. Such huge data has to be studied and handled to extract knowledge from these datasets. Hence Big Data analysis is a current research area that mainly explore the potential impact of big data challenges, various tools used for big data analysis. This article deals with big data at numerous levels and opens a new horizon for researchers.

Integration of Blockchain and Digital Twins in the Smart Built Environment Adopting Disruptive Technologies—A Systematic Review

The integration of blockchain and digital twins (DT) for better building-lifecycle data management has recently received much attention from researchers in the field. In this respect, the adoption of enabling technologies such as artificial intelligence (AI) and machine learning (ML), the Internet of Things (IoT), cloud and edge computing, Big Data analytics, etc., has also been investigated in an abundance of studies. The present review inspects the recent studies to shed light on the foremost among those enabling technologies and their scope, challenges, and integration potential. To this end, 86 scientific papers, recognized and retrieved from the Scopus and Web of Science databases, were reviewed and a thorough bibliometric analysis was performed on them. The obtained results demonstrate the nascency of the research in this field and the necessity of further implementation of practical methods to discover and prove the real potential of these technologies and their fusion. It was also found that the integration of these technologies can be beneficial for addressing the implementation challenges they face individually. In the end, an abstract descriptive model is presented to provide a better understanding of how the technologies can become integrated into a unified system for smartening the built environment.

Edge-Enabled Two-Stage Scheduling Based on Deep Reinforcement Learning for Internet of Everything

Nowadays, the concept of Internet of Everything (IoE) is becoming a hotly discussed topic, which is playing an increasingly indispensable role in modern intelligent applications. These applications are known for their real-time requirements under limited network and computing resources, thus it becomes a highly demanding task to transform and compute tremendous amount of raw data in a cloud center. The edge–cloud computing infrastructure allows a large amount of data to be processed on nearby edge nodes and then only the extracted and encrypted key features are transmitted to the data center. This offers the potential to achieve an end–edge–cloud-based big data intelligence for IoE in a typical two-stage data processing scheme, while satisfying a data security constraint. In this study, a deep-reinforcement-learning-enhanced two-stage scheduling (DRL-TSS) model is proposed to address the NP-hard problem in terms of operation complexity in end–edge–cloud Internet of Things systems, which is able to allocate computing resources within an edge-enabled infrastructure to ensure computing task to be completed with minimum cost. A presorting scheme based on Johnson’s rule is developed and applied to preprocess the two-stage tasks on multiple executors, and a DRL mechanism is developed to minimize the overall makespan based on a newly designed instant reward that takes into account the maximal utilization of each executor in edge-enabled two-stage scheduling. The performance of our method is evaluated and compared with three existing scheduling techniques, and experimental results demonstrate the ability of our proposed algorithm in achieving better learning efficiency and scheduling performance with a 1.1-approximation to the targeted optimal IoE applications.

An Amendable Multi-Function Control Method using Federated Learning for Smart Sensors in Agricultural Production Improvements

Communications and Computer Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan School of Information Technology, Skyline University, Sharjah, 1797, UAE Smart Sensors are used for monitoring, sensing, and actuating controls in small and large-scale agricultural plots. From soil features to crop health and climatic observations, the smart sensors integrate with sophisticated technologies such as the Internet of Things or cloud for decentralized processing and global actuation. Considering this integration, an Amendable Multi-Function Sensor Control (AMFSC) is introduced in this proposal. This proposed method focuses on sensor operations that aid agricultural production improvements. The agriculture hindering features from the soil, temperature, and crop infections are sensed and response is actuated based on controlled operations. The control operations are performed according to the sensor control validation and modified control acute sensor, which helps to maximize productivity. The sensor control and operations are determined using federated learning from the accumulated data in the previous sensing intervals. This learning validates the current sensor data with the optimal data stored for different crops and environmental factors in the past. Depending on the computed, sensed, and optimal (adaptable) data, the sensor operation for actuation is modified. This modification is recommended for crop and agriculture development to maximize agricultural productivity. In particular, the sensing and actuation operations of the smart sensors for different intervals are modified to maximize production and adaptability. The efficiency of the system was evaluated using different parameters and the system maximizes the analysis rate (12.52%), control rate (7%), adaptability (9.65%) and minimizes the analysis time (7.12%), and actuation lag (8.97%)

Retracted: A Survey on Information-Centric Networking with Cloud Internet of Things and Artificial Intelligence

Retracted: A Survey on Information-Centric Networking with Cloud Internet of Things and Artificial Intelligence

Analysis of End-to-End Delay Video Conferencing Services on a Mobile Ad Hoc Network

The purpose of this study is to conduct a series of tests on each increase in the number of node-clients on the Mobile Ad hoc Network, while each node performs real-time video conferencing services from the internet cloud which can have a significant impact on the Quality of Service. The quality of service of the Mobile Ad hoc Network that supports video conferencing in real-time is evaluated based on End-to-End Delay. The model offered is in the form of the implementation of an ad hoc network on the zoom cloud meeting service. We assessed the transmission scenario of wireless video conferencing services while changing the number of user nodes. It carried the measurement out 5 times and calculated the average value obtained to be compared against the number of different nodes in this case the data obtained using Wireshark software. The results showed that the end-to-end delay value on the Mobile Ad hoc Network increased by 27% when the scalability of the Mobile Ad hoc network was increased from 2 nodes to 4 nodes. Our proposed model can be helpful in making decisions about aspects of the number of nodes in the Mobile Ad hoc network in real-time communication

Private Backend Server Software-Based Telehealthcare Tracking and Monitoring System

In these recent years, the world has witnessed a kind of social exclusion and the inability to communicate directly due to the Corona Virus Covid 19 (COVID-19) pandemic, and the consequent difficulty of communicating with patients with hospitals led to the need to use modern technology to solve and facilitate the problem of people communicating with each other. healthcare has made many remarkable developments through the Internet of things (IOT) and cloud computing to monitor real-time patients' data, which has enabled many patients' lives to be saved. this paper presents the design and implementation of a Private Backend Server Software based on an IoT health monitoring system concerned emergency medical services utilizing biosensors to detect multivital signs of an individual with an ESP32 microcontroller board and IoT cloud. The device displays the vital data, which is then uploaded to a cloud server for storage and analysis over an IoT network. Vital data is received from the cloud server and shown on the IoT medical client dashboard for remote monitoring. The proposed system allows users to ameliorate healthcare jeopardy and minify its costs by recording, gathering, sharing, and analyzing vast biodata streams such as Intensive Care Units (ICU) (i.e., temperature, heartbeat rate, Oxygen level (CO2), etc.), efficiently in real-time. In this proposal, the data will send from sensors fixed in the patient body to the Web and Mobile App continually in real time for collection and analysis.

Research and Development of Cellar Pool Environmental Monitoring System Based on NB-IOT and Cloud Platform

NB-IOT and cloud platform technology are used to monitor the cellar pool environment, which is difficult to collect by hand. Under the zigbee self-organizing network, the system collects the parameters of the terminal nodes distributed in each cellar pool through a coordinator, and then sends them to the ali cloud internet of things platform for display and control through the nb-IOT communication module. Experiments show that the system can display the environment data of the cellar pool in real time and open the execution device outside the threshold range, which proves the practicability and convenience of the system.

Novel Block Chain Technique for Data Privacy and Access Anonymity in Smart Healthcare

The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internet and computing resources. In recent years, many more IoT applications have been extensively used. For instance, Healthcare applications execute computations utilizing the user’s private data stored on cloud servers. However, the main obstacles faced by the extensive acceptance and usage of these emerging technologies are security and privacy. Moreover, many healthcare data management system applications have emerged, offering solutions for distinct circumstances. But still, the existing system has issues with specific security issues, privacy-preserving rate, information loss, etc. Hence, the overall system performance is reduced significantly. A unique blockchain-based technique is proposed to improve anonymity in terms of data access and data privacy to overcome the above-mentioned issues. Initially, the registration phase is done for the device and the user. After that, the Geo-Location and IP Address values collected during registration are converted into Hash values using Adler 32 hashing algorithm, and the private and public keys are generated using the key generation centre. Then the authentication is performed through login. The user then submits a request to the blockchain server, which redirects the request to the associated IoT device in order to obtain the sensed IoT data. The detected data is anonymized in the device and stored in the cloud server using the Linear Scaling based Rider Optimization algorithm with integrated KL Anonymity (LSR-KLA) approach. After that, the Timestamp-based Public and Private Key Schnorr Signature (TSPP-SS) mechanism is used to permit the authorized user to access the data, and the blockchain server tracks the entire transaction. The experimental findings showed that the proposed LSR-KLA and TSPP-SS technique provides better performance in terms of higher privacy-preserving rate, lower information loss, execution time, and Central Processing Unit (CPU) usage than the existing techniques. Thus, the proposed method allows for better data privacy in the smart healthcare network.

Security aspects related Intelligent e-Health Systems

Current health situations and crises require physicians and researchers to convert traditional healthcare services to a new generation of digital healthcare known as e-health smart systems that operate close to the end user with the objective to provide medical assistance using advanced technologies including Internet of Medical Things technology, edge computing, and cloud computing paradigms. However, this system architecture reveals limitations in terms of latency, bandwidth, power consumption as well as security and privacy of collected data. Through this paper, a presentation of different healthcare monitoring system layers has been defined, in addition to listed challenges faced by the deployment of this technology, while providing an introduction of security risks that threaten the user data safety alongside the e-health layers. Moreover, a summarized discussion of Blockchain technology came over to provide a secure decentralized architecture in sharing data across allowed entities.

Construction of Supply Chain Security Management Model for Information and Communication Technology Based on the Internet of Things and Cloud Computing

At the level of enterprise supply chain security management, with the rise of network technology big data technology methods such as the Internet of Things (IoT) and cloud computing, it has had a multi-level impact on its supply chain management mode, including operation mode, operation mode, and security management mode. The traditional supply chain management model is based on old-fashioned communication methods and manual intervention for judgment and processing. Information and communication technology has advantages such as immediacy and strong correlation. Based on this, this article applies the data interconnection technology of the Internet of Things to the construction of supply chain management models and combines it with efficient algorithms of cloud computing. This article also explores the construction of supply chain security management models from the perspectives of participants, activity modes, module operations, and conducts data comparisons based on them. The experimental results show that in the running tests of the model, its average accuracy is about 89.60%, and the prediction accuracy of benefits is as high as 98.48%. In stability testing, it is also evident that the average stability of the model after multiple iterations is about 96.02%. Research has shown that the Internet of Things and cloud computing technology have great application potential in the construction of supply chain security management, which can provide enterprises with more effective supply chain security management tools.

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Internet of things and cloud computing as the core technology and government management and industrial development as the core content, forming a large-scale, heterogeneous and dynamic distributed Internet of things environment between different Internet of things. There is a wide demand for cooperation between equipment and management institutions in the smart city. Therefore, it is necessary to establish a trust mechanism to promote cooperation, and based on this, prevent data disorder caused by the interaction between honest terminals and malicious terminals. However, most of the existing research on trust mechanism is divorced from the Internet of things environment, and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of things devices, resulting in the fact that the research on abstract trust mechanism cannot be directly applied to the Internet of things; On the other hand, various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered. Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals, a cross domain trust model (CDTM) based on self-authentication is proposed. Unlike most trust models, this model uses self-certified trust. The cross-domain process of internet of things (IoT) terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction. At the same time, in order to alleviate the collision attack and improve the accuracy of trust evaluation, the overall trust value is calculated by comprehensively considering the quantity weight, time attenuation weight and similarity weight. Finally, the simulation results show that CDTM has good anti collusion attack ability. The success rate of malicious interaction will not increase significantly. Compared with other models, the resource consumption of our proposed model is significantly reduced.

Embedded security framework for M2M communication using ULBC and Rubik’s Cube encryption algorithm

The potential of a variety of stockholders has increased as a direct result of the development of the Internet of Things and cloud computing. These individuals are now able to communicate and share data in a productive manner. This interaction with devices is regarded as extremely helpful and advantageous by a lot of users. Inadequate configuration of a network system makes it an easy target for potential security breaches. This is why it is important that a security framework is developed for the Internet of Things (IoT) and cloud computing models. This paper talks about the various frameworks that are used in these two technologies. It also explores the various security methods that are used to protect the devices that are connected to the cloud. Numerous studies have shown that there are various ways that people can manipulate the operations of machine-to-machine (M2M) in the digital world. While analyzing the methodology used in this technology, it is also important to keep in mind the security levels of the cloud and IoT ecosystem. The development of a secure connection between the various devices that are connected to the cloud is also important to ensure that the network is protected from security threats. This paper aims to provide a framework that will allow the integration of the Cloud and IoT and the Cloud Framework for Machine-to-Machine communication. The paper aims to develop a secure framework that will allow the integration of the Cloud and IoT with secure framework for Machine-to-Machine communication using (ultra-lightweight block cipher) ULBC and Rubik’s Cube encryption algorithm.

Retracted: Optimization of Online Course Platform for Piano Preschool Education Based on Internet Cloud Computing System.

[This retracts the article DOI: 10.1155/2022/6525866.].

Secure cloud internet of vehicles based on blockchain and data transmission scheme of map/reduce

Over the past few years, because of the popularity of the Internet of vehicles and cloud computing, the exchange of group information between vehicles is no longer out of reach. Through WiFi/5G wireless communication protocol, vehicles can instantly deliver traffic conditions and accidents to the back end or group vehicles traveling together, which can reduce traffic congestion and accidents. In addition, vehicles transmit real-time road conditions to the cloud vehicle management center, which can also share real-time road conditions and improve the road efficiency for pedestrians and drivers. However, the transmission of information in an open environment raises the issue of personal information security. Most of the security mechanisms provided by the existing Internet of vehicles require centralized authentication servers, which increase the burden of certificate management and computing. Moreover, the road side unit as a decentralized authentication center may be open to hacking or modification, but due to personal privacy and security concerns, vehicle-to-vehicle is not willing to share information with each other. Therefore, this study is conducted through blockchain to ensure the security of vehicle-based information transmission. Moreover, the elliptic curve Diffie?Hellman (ECDH) key exchange protocol and a secure conference key mechanism with direct user confirmation combined with the back-end cloud platform Map/Reduce is proposed to ensure the identities of Mappers and Reducers that participate in the cloud operation, avoid malicious participants to modify the transmission information, so as to achieve secure Map/Reduce operations, and improves vehicle and passenger traffic safety.

Design and Research of Teaching and Training Platform Based on Industrial Internet and Intelligent Manufacturing

Industrial internet and intelligent manufacturing application training platform is a comprehensive teaching and training platform for industrial internet, which contains a complete industrial internet architecture, including: sensing equipment, actuator, wireless sensor network, wired sensor network, industrial master control organization, industrial Internet cloud platform, industrial big data analysis platform. The industrial internet application technology training platform uses emerging technologies such as industrial internet, embedded, big data, mobile application, artificial intelligence, intelligent manufacturing, and built-in industrial internet cloud platform to provide teaching, practical training and scientific research support for the upgrading and reform of relevant disciplines in vocational colleges. The industrial internet and intelligent manufacturing training platform provides hardware and software resources with three layers of architecture, including equipment layer, platform layer and application layer, providing supporting teaching and practical training resources for related teaching. Industrial internet application technology training platform can meet the needs of industrial Internet teaching and training courses, including: "Industrial Ethernet and Network Equipment", "Network Security Technology", "Industrial Internet of Things", "Industrial Automation Control", "Network System Software Design", "Machine Vision Technology", "Manufacturing Execution System (MES) Application Technology" and so on.

Design and Implementation of a Cloud-IoT-Based Home Energy Management System

The advances in the Internet of Things (IoT) and cloud computing opened new opportunities for developing various smart grid applications and services. The rapidly increasing adoption of IoT devices has enabled the development of applications and solutions to manage energy consumption efficiently. This work presents the design and implementation of a home energy management system (HEMS), which allows collecting and storing energy consumption data from appliances and the main load of the home. Two scenarios are designed and implemented: a local HEMS isolated from the Internet and relies on its processing and storage duties using an edge device and a Cloud HEMS using AWS IoT Core to manage incoming data messages and provide data-driven services and applications. A testbed was carried out in a real house in the city of Valparaiso, Chile, over a one-year period, where four appliances were used to collect energy consumption using smart plugs, as well as collecting the main energy load of the house through a data logger acting as a smart meter. To the best of our knowledge, this is the first electrical energy dataset with a 10-second sampling rate from a real household in Valparaiso, Chile. Results show that both implementations perform the baseline tasks (collecting, storing, and controlling) for a HEMS. This work contributes by providing a detailed technical implementation of HEMS that enables researchers and engineers to develop and implement HEMS solutions to support different smart home applications.

Disordered Mixture of Self-Assembled Molecular Functional Groups on Heterointerfaces with p-Si Leads to Multiple Key Generation in Physical Unclonable Functions.

Physical unclonable function (PUF) security devices based on hardware are becoming an effective strategy to overcome the dependency of the internet cloud and software-based hacking vulnerabilities. On the other hand, existing Si-based artificial security devices have several issues, including the absence of a method for multiple key generation, complex and expensive fabrication processes, and easy prediction compared to devices retaining natural randomness. Herein, to generate unique and unpredictable multiple security keys, this paper proposes novel PUF devices consisting of a disordered random mixture of two self-assembled monolayers (SAMs) formed onto p-type Si. The proposed PUF devices exhibited multikeys at different voltage biasing, including 0 V, through the arbitrary dipole effect. As a result, multiple unpredictable hardware security keys were generated from one device using a simple solution-coating process. The PUF security device based on the mixture of materials with different dipoles developed in this study can provide valuable insights for implementing various PUF devices in the future.

Monitoring Environment Parameters of Gerbera Flower Cultivation in Greenhouse using Internet of Things

Objectives: To propose a system based on monitoring and analyzing of greenhouse environment, built for cultivating Gerbera with advanced technologies like the Internet of Things(IoT), Android applications, and cloud. Methods: To ensure optimal growth of the plant in a greenhouse, smart applications with sensors and Mobile apps are implemented for remote monitoring. Sensors are attached to the Arduino interface which collects and broadcasts the data onto the IoT cloud. This cloud platform (Ubidots) creates real-time dashboards for user-friendly graphic visualization. Further, through the Android Mobile application interface, the data can be regularly monitored and tracked by the end user to take necessary actions. Findings: The system is designed to automatically monitor four environmental parameters such as temperature, humidity, soil moisture, and light inside the greenhouse through IoT. The data recorded in the cloud is visualized through an app and the user can monitor remotely the evident conditions of greenhouse parameters. If any of these parameter values crosses the threshold set, then the notification will be sent to the end user. A mobile application developed will guide the farmers to remotely monitor the greenhouse and influence the grower’s decisions. Novelty: The proposed work ensures that the quality and yield of crops can be effective with automatic remote monitoring with the help of technology. This work highlights designing a user-friendly app for farmers which is safe and can track the current conditions, detect abnormalities, and easy water management. The Signup/login page, data visualization, and alert messages are implemented in the app which marks farming as more efficient compared to traditional methods. Keywords: Gerbera; Greenhouse; Ubidots; Internet of Things; Android

Accurate Management Practice of Medical Consumables in the Whole Process Based on SPD Supply Chain

Establish a hospital logistics information SPD system, carry out internet cloud procurement, and interface with the hospital HIS system, surgical anesthesia system, internal control management system, etc, to form an information system covering the entire process of external logistics, internal logistics, and internal control audit. Accurately classify consumables in multi-dimensions, improve the consumables dictionary field information dictionary database, refine supplier management, and realize the whole process of implanting interventional and high-value consumables into three codes (material code, medical insurance consumable code, charge code) traceability management, and data visualization. At the same time, it reduces hospital inventory and cost pressure, reduces the intensity of nursing staff's management of consumables, reduces the risks and loopholes of consumables management, and improves the level of digital and intelligent management of medical consumables.