Quantum circuit-based proxy blind signatures:A novel approach and experimental evaluation on the IBM quantum cloud platform

This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits,aiming to enhance security while safeguarding sensitive information.The main objective of this research is to introduce a quantum proxy blind signature(QPBS)protocol that utilizes quantum logical gates and quantum measurement techniques.The QPBS protocol is constructed by the initial phase,proximal blinding message phase,remote authorization and signature phase,remote validation,and de-blinding phase.This innovative design ensures a secure mechanism for signing documents without revealing the content to the proxy signer,providing practical security authentication in a quantum environment under the assumption that the CNOT gates are securely implemented.Unlike existing approaches,our proposed QPBS protocol eliminates the need for quantum entanglement preparation,thus simplifying the implementation process.To assess the effectiveness and robustness of the QPBS protocol,we conduct comprehensive simulation studies in both ideal and noisy quantum environments on the IBM quantum cloud platform.The results demonstrate the superior performance of the QPBS algorithm,highlighting its resilience against repudiation and forgeability,which are key security concerns in the realm of proxy blind signatures.Furthermore,we have established authentic security thresholds(82.102%)in the presence of real noise,thereby emphasizing the practicality of our proposed solution.

Cloud-Processing Platform for Traffic Flow Based on Internet of Car

Internet of Car, resulting from the Internet of Things, is a key point for the forthcoming smart city. In this article, GPS technology, 3G wireless technology and cloud-processing technology are employed to construct a cloud-processing network platform based on the Internet of Car. By this platform, positions and velocity of the running cars, information of traffic flow from fixed monitoring points and transportation videos are combined to be a virtual traffic flow data platform, which is a parallel system with real traffic flow and is able to supply basic data for analysis and decision of intelligent transportation system.

Research on Network Security Level Protection Measurement Strategy in the Context of Cloud Platforms

Platforms facilitate information exchange,streamline resources,and reduce production and management costs for companies.However,some viral information may invade and steal company resources,or lead to information leakage.For this reason,this paper discusses the standards for cybersecurity protection,examines the current state of cybersecurity management and the risks faced by cloud platforms,expands the time and space for training on cloud platforms,and provides recommendations for measuring the level of cybersecurity protection within cloud platforms in order to build a solid foundation for them.

Application Analysis of Doctor-Nurse Cooperation Follow-up Model Based on the Internet Platform in Continuation Care of Patients after Urooncology Surgery

Objective:To explore the application effect of a doctor-nurse cooperation follow-up model based on an Internet platform in the continuation of care for patients after uro-oncology surgery.Methods:A convenient sampling method was used to select patients with urinary system tumors who underwent surgery in the Department of Urology in Grade III A general hospital in Shanghai from May to August 2022.Patients who underwent surgery from May to June 2022 were assigned to the control group,and those who underwent surgery from July to August 2022 were assigned to the experimental group.The control group received routine post-discharge nursing health education and telephone follow-up.On the basis of routine discharge guidance,the experimental group implemented the intervention method based on the Internet platform in continuation care.The levels of self-management efficacy,satisfaction,and incidence of unplanned readmission were compared one month after discharge between the two groups.Results:One month after discharge,the self-management efficacy of the experimental group(90.15±7.92)was significantly higher than that of the control group(79.10±7.84),and the patient satisfaction score(97.83±2.32)was significantly higher than that of the control group(90.23±2.58),with statistical significance(P<0.05).Additionally,the incidence of unplanned readmissions within one month after discharge in the experimental group(1.59%)was slightly lower than that in the control group(4.84%).Conclusion:The doctor-nurse cooperation follow-up model based on the Internet platform in continuation care can significantly improve the self-management efficiency of patients after discharge and enhance patient satisfaction,providing a new approach for discharge follow-up of urological tumor patients after surgery.

A Novel Load Balancing Strategy of Software-Defined Cloud/Fog Networking in the Internet of Vehicles

The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture.

Research on Mobile Internet Mobile Agent System Dynamic Trust Model for Cloud Computing

This paper analyzes the reasons for the formation of security problems in mobile agent systems, and analyzes and compares the security mechanisms and security technologies of existing mobile agent systems from the perspective of blocking attacks. On this basis, the host protection mobile agent protection technology is selected, and a method to enhance the security protection of mobile agents (referred to as IEOP method) is proposed. The method first encrypts the mobile agent code using the encryption function, and then encapsulates the encrypted mobile agent with the improved EOP protocol IEOP, and then traces the suspicious execution result. Experiments show that using this method can block most malicious attacks on mobile agents, and can protect the integrity and confidentiality of mobile agents, but the increment of mobile agent tour time is not large.

An Efficient Adaptive Failure Detection Mechanism for Cloud Platform Based on Volterra Series

Failure detection module is one of important components in fault-tolerant distributed systems,especially cloud platform.However,to achieve fast and accurate detection of failure becomes more and more difficult especially when network and other resources' status keep changing.This study presented an efficient adaptive failure detection mechanism based on volterra series,which can use a small amount of data for predicting.The mechanism uses a volterra filter for time series prediction and a decision tree for decision making.Major contributions are applying volterra filter in cloud failure prediction,and introducing a user factor for different QoS requirements in different modules and levels of IaaS.Detailed implementation is proposed,and an evaluation is performed in Beijing and Guangzhou experiment environment.

Design and Implementation of Cloud Platform for Intelligent Logistics in the Trend of Intellectualization

Intellectualization has become a new trend for telecom industry, driven by intelligent technology including cloud computing, big data, and Internet of things. In order to satisfy the service demand of intelligent logistics, this paper designed an intelligent logistics platform containing the main applications such as e-commerce, self-service transceiver, big data analysis, path location and distribution optimization. The intelligent logistics service platform has been built based on cloud computing to collect, store and handling multi-source heterogeneous mass data from sensors, RFID electronic tag, vehicle terminals and APP, so that the open-access cloud services including distribution, positioning, navigation, scheduling and other data services can be provided for the logistics distribution applications. And then the architecture of intelligent logistics cloud platform containing software layer(SaaS), platform layer(PaaS) and infrastructure(IaaS) has been constructed accordance with the core technology relative high concurrent processing technique, heterogeneous terminal data access, encapsulation and data mining. Therefore, intelligent logistics cloud platform can be carried out by the service mode for implementation to accelerate the construction of the symbiotic win-winlogistics ecological system and the benign development of the ICT industry in the trend of intellectualization in China.

Personal control in rehabilitation: An internet platform for patients with schizophrenia and their caregivers

In order to facilitate optimal quality and continuity of care and self-management of patients with schizophrenia, Personal Control in Rehabilitation (PCR) has been developed, a comprehensive internet-based platform. PCR aims to reach patients with schizophrenia or related psychotic disorders and their (in)formal caregivers. It provides caregivers and patients with a kind of dashboard giving them (tailored) information about the physical and psychosocial situation of the patient, their compliance with treatment, and facilitates optimal self-management for patients. With the use of PCR, informal caregivers have access to information about treatment and can consult formal caregivers, formal caregivers have easy access to the multidisciplinary guideline schizophrenia and to the regional care plans. Patients and their formal and informal caregivers completed questionnaires about empowerment, quality of life, quality of care and workload before the introduction of PCR and nine months later. A sample of them also participates in focus group discussions about the usefulness and usability of PCR and bottle necks in using the platform. Response to the questionnaires, especially at followup, was not adequate for statistical analyses of its results. Therefore, the focus of this paper is on the process evaluation of the implementation of PCR in two large mental health organisations in the Netherlands. Data for this evaluation are results of a number of open questions of the questionnaire and results of the focus group discussions. In these discussions, 19 patients, 12 informal caregivers, 33 formal caregivers and three managers participated. Preliminary results of this study show that the implementation of PCR is feasible and patients, formal and informal caregivers were satisfied with the use of PCR and frequently used PCR to communicate, make appointments, give feedback and look for information about their disease and its treatment. PCR is considered as a help in reaching more self-management by patients, to facilitate communication between patients and caregivers, and to match treatment and support. It seems useful also for other patient groups and their (in)formal caregivers like patients with depression, autism and addiction disorders.

Security Requirement Management for Cloud-Assisted and Internet of Things—Enabled Smart City

The world is rapidly changing with the advance of information technology.The expansion of the Internet of Things(IoT)is a huge step in the development of the smart city.The IoT consists of connected devices that transfer information.The IoT architecture permits on-demand services to a public pool of resources.Cloud computing plays a vital role in developing IoT-enabled smart applications.The integration of cloud computing enhances the offering of distributed resources in the smart city.Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability,security,performance,condentiality,and privacy.The key reason for cloud-and IoT-enabled smart city application failure is improper security practices at the early stages of development.This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications.Its three-layered architecture includes privacy preserved stakeholder analysis(PPSA),security requirement modeling and validation(SRMV),and secure cloud-assistance(SCA).A case study highlights the applicability and effectiveness of the proposed framework.A hybrid survey enables the identication and evaluation of signicant challenges.

Distributed energy storage node controller and control strategy based on energy storage cloud platform architecture

Based on the energy storage cloud platform architecture,this study considers the extensive configuration of energy storage devices and the future large-scale application of electric vehicles at the customer side to build a new mode of smart power consumption with a flexible interaction,smooth the peak/valley difference of the load side power,and improve energy efficiency.A plug and play device for customer-side energy storage and an internet-based energy storage cloud platform are developed herein to build a new intelligent power consumption mode with a flexible interaction suitable for ordinary customers.Based on the load perception of the power grid,this study aims to investigate the operating state and service life of distributed energy storage devices.By selecting an integrated optimal control scheme,this study designs a kind of energy optimization and deployment strategy for stratified partition to reduce the operating cost of the energy storage device on the client side.The effectiveness of the system and the control strategy is verified through the Suzhou client-side distributed energy storage demonstration project.

The Construction of Human Resource Management Cloud Service Platform

Human resource management service faced the challenges of promoting efficiency, costs saving, quick responding and so on. In order to face these challenges, this paper puts forward a “6 + 1” structure of human resource management service combined with some characteristics of cloud computing, elaborates the service mode of the cloud service platform based on this structure, the characteristics and challenges of the platform, and hopes to provide a new service perspective to human resource management.

Fine-Grained Binary Analysis Method for Privacy Leakage Detection on the Cloud Platform

Nowadays cloud architecture is widely applied on the internet.New malware aiming at the privacy data stealing or crypto currency mining is threatening the security of cloud platforms.In view of the problems with existing application behavior monitoring methods such as coarse-grained analysis,high performance overhead and lack of applicability,this paper proposes a new fine-grained binary program monitoring and analysis method based on multiple system level components,which is used to detect the possible privacy leakage of applications installed on cloud platforms.It can be used online in cloud platform environments for fine-grained automated analysis of target programs,ensuring the stability and continuity of program execution.We combine the external interception and internal instrumentation and design a variety of optimization schemes to further reduce the impact of fine-grained analysis on the performance of target programs,enabling it to be employed in actual environments.The experimental results show that the proposed method is feasible and can achieve the acceptable analysis performance while consuming a small amount of system resources.The optimization schemes can go beyond traditional dynamic instrumentation methods with better analytical performance and can be more applicable to online analysis on cloud platforms.

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

With the rapid development of the Internet of Things(IoT),there are several challenges pertaining to security in IoT applications.Compared with the characteristics of the traditional Internet,the IoT has many problems,such as large assets,complex and diverse structures,and lack of computing resources.Traditional network intrusion detection systems cannot meet the security needs of IoT applications.In view of this situation,this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance.Usually,traditional intrusion detection algorithms require considerable time for training,and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes;therefore,it is necessary to study intrusion detection algorithms with low weights,short training time,and high detection accuracy for deployment and application on cloud nodes.An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats.This paper discusses the problems related to IoT intrusion prevention in cloud computing environments.Based on the analysis of cloud computing security threats,this study extensively explores IoT intrusion detection,cloud node monitoring,and intrusion response in cloud computing environments by using cloud computing,an improved extreme learning machine,and other methods.We use the Multi-Feature Extraction Extreme Learning Machine(MFE-ELM)algorithm for cloud computing,which adds a multi-feature extraction process to cloud servers,and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes.In our simulation experiments,a classical dataset for intrusion detection is selected as a test,and test steps such as data preprocessing,feature engineering,model training,and result analysis are performed.The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes.Furthermore,it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time,so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.

Mobile Internet Mobile Agent System Dynamic Trust Model for Cloud Computing

In mobile cloud computing,trust is a very important parameter in mobile cloud computing security because data storage and data processing are performed remotely in the cloud.Aiming at the security and trust management of mobile agent system in mobile cloud computing environment,the Human Trust Mechanism(HTM)is used to study the subjective trust formation,trust propagation and trust evolution law,and the subjective trust dynamic management algorithm(MASTM)is proposed.Based on the interaction experience between the mobile agent and the execution host and the third-party recommendation information to collect the basic trust data,the public trust host selection algorithm is given.The isolated malicious host algorithm and the integrated trust degree calculation algorithm realize the function of selecting the trusted cluster and isolating the malicious host,so as to enhance the security interaction between the mobile agent and the host.Given algorithm simulation and verification were carried out to prove its feasibility and effectiveness.

Exploring the Innovative Teaching Mode of Geomatics Based on the Teaching Cloud Platform

Geomatics is an interdisciplinary subject.Many disciplines have teaching demands in this field.A new course on“Geomatics Technology”has been suggested by the Weiyang College of Tsinghua University of China for the major of“Mathematical and Scientific Basic Science+Civil,Hydraulic and Marine Engineering”.This paper offers a data-led geomatics teaching mode,developing a customized teaching cloud platform,to explore the cross-integrated innovative teaching methods.Teachers and students can assign and submit assignments on this platform.The platform constitutes a data flow with the data download,data processing and result sharing.It encourages communication among students in various majors,grades and units using data as the medium,from data processing to application upstream and downstream.In the“Geomatics Technology”course,geospatial data has emerged as a vital element of the multidisciplinary approach.This kind of teaching mode has been used in the postgraduate remote sensing course offered by Tsinghua University’s Department of Civil Engineering and Construction Management.Furthermore,the mode will be used for the first time in the autumn semester of 2022 in the undergraduate teaching of Weiyang College and civil engineering,to offer a novel idea for the reform of courses linked to geospatial informatics.

An Industrial Internet Platform for Massive Pig Farming (IIP4MPF)

Pig farming is becoming a key industry of China’s rural economy in recent years. The current pig farming is still relatively manual, lack of latest Information and Communication Technology (ICT) and scientific management methods. This paper proposes an industrial internet platform for massive pig farming, namely, IIP4MPF, which aims to leverage intelligent pig breeding, production rate and labor productivity with the use of artificial intelligence, the Internet of Things, and big data intelligence. We conducted requirement analysis for IIP4MPF using software engineering methods, designed the IIP4MPF system for an integrated solution to digital, interconnected, intelligent pig farming. The practice demonstrates that the IIP4MPF platform significantly improves pig farming industry in pig breeding and productivity.

Education Technology Cloud Platform Framework Establishment and Security

With more educational business absorbed into information management system at universities, traditional information management platform seems unable to provide efficient service for teaching and research. Some universities then resort to cloud computing platform. In view of the problems existing in the traditional information platform, this study presented an information management framework designed with cloud technology, and introduced the security techniques for its protection.

Design and Implementation of Novel Precision Internet Marketing Patterns under the Big Data and Cloud Environment

Nowadays most of the cloud applications process large amount of data to provide the desired results. The Internet environment, the enterprise network advertising, network marketing plan, need partner sites selected as carrier and publishers. Website through static pages, dynamic pages, floating window, AD links, take the initiative to push a variety of ways to show the user enterprise marketing solutions, when the user access to web pages, use eye effect and concentration effect, attract users through reading web pages or click the page again, let the user detailed comprehensive understanding of the marketing plan, which affects the user＇ s real purchase decisions. Therefore, we combine the cloud environment with search engine optimization technique, the result shows that our method outperforms compared with other approaches.

Blockchain Platform for Industrial Internet of Things

Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management. Cloud-Based Manufacturing is a recent on-demand model of manufacturing that is leveraging IoT technologies. While Cloud-Based Manufacturing enables on-demand access to manufacturing resources, a trusted intermediary is required for transactions between the users who wish to avail manufacturing services. We present a decentralized, peer-to-peer platform called BPIIoT for Industrial Internet of Things based on the Block chain technology. With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer-to-peer network to interact with each other without the need for a trusted intermediary.