Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective search.Attribute-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud storage.However,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations.In a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE schemes.In the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network.Thus,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS.With the assistance of blockchain technology,the proposed scheme offers two main benefits.First,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of failure.Second,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network.Specifically,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus nodes.This eliminates the need of the trusted authority and reduces the burden of data users,respectively.Further,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users.It has been verified both theoretically and practically in the performance analysis section.

Privacy Protection Based Access Control Scheme in Cloud-Based Services

With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection(PS-ACS). In the PS-ACS scheme, we divide users into private domain(PRD) and public domain(PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption(KAE) and the Improved Attribute-based Signature(IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption(CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.

Intelligent Spectrum Management Based on Radio Map for Cloud-Based Satellite and Terrestrial Spectrum Shared Networks

Cloud-based satellite and terrestrial spectrum shared networks(CB-STSSN)combines the triple advantages of efficient and flexible net-work management of heterogeneous cloud access(H-CRAN),vast coverage of satellite networks,and good communication quality of terrestrial networks.Thanks to the complementary coverage characteristics,any-time and anywhere high-speed communications can be achieved to meet the various needs of users.The scarcity of spectrum resources is a common prob-lem in both satellite and terrestrial networks.In or-der to improve resource utilization,the spectrum is shared not only within each component but also be-tween satellite beams and terrestrial cells,which intro-duces inter-component interferences.To this end,this paper first proposes an analytical framework which considers the inter-component interferences induced by spectrum sharing(SS).An intelligent SS scheme based on radio map(RM)consisting of LSTM-based beam prediction(BP),transfer learning-based spec-trum prediction(SP)and joint non-preemptive prior-ity and preemptive priority(J-NPAP)-based propor-tional fair spectrum allocation is than proposed.The simulation result shows that the spectrum utilization rate of CB-STSSN is improved and user blocking rate and waiting probability are decreased by the proposed scheme.

DADOS:A Cloud-based Data-driven Design Optimization System

This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including the design of experiments,surrogate models,model validation and selection,prediction,optimization,and sensitivity analysis.Moreover,it also includes an exclusive ensemble surrogate modeling technique,the extended hybrid adaptive function,which can make use of the advantages of each surrogate and eliminate the effort of selecting the appropriate individual surrogate.To improve ease of use,DADOS provides a user-friendly graphical user interface and employed flow-based programming so that users can conduct design optimization just by dragging,dropping,and connecting algorithm blocks into a workflow instead of writing massive code.In addition,DADOS allows users to visualize the results to gain more insights into the design problems,allows multi-person collaborating on a project at the same time,and supports multi-disciplinary optimization.This paper also details the architecture and the user interface of DADOS.Two examples were employed to demonstrate how to use DADOS to conduct data-driven design optimization.Since DADOS is a cloud-based system,anyone can access DADOS at www.dados.com.cn using their web browser without the need for installation or powerful hardware.

Toward Cloud-Based Parking Facility Management System: A Preliminary Study

An e-tag used on the freeway is a kind of passive sensors composed of sensors and radio- frequency identification （RFID） tags. The principle of the electronic toll collection system is that the sensor emits radio waves touching the e-tag within a certain range, the e-tag will respond to the radio waves by induction, and the sensor will read and write information of the vehicles. Although the RFID technology is popularly used in campus management systems, there is no e-tag technology application used in a campus parking system. In this paper, we use the e-tag technology on a campus parking management system based on the cloud-based construction. By this, it helps to achieve automated and standardized management of the campus parking system, enhance management efficiency, reduce the residence time of the vehicles at the entrances and exits, and improve the efficiency of vehicles parked at the same time.

Evaluation and Optimization of the Mixed Redundancy Strategy in Cloud-Based Systems

Mixed redundancy strategies are generally used in cloud-based systems,with different node switch mechanisms from traditional fault-tolerant strategies.Existing studies often concentrate on optimizing a single strategy in cloud computing environment and ignore the impact of mixed redundancy strategies.Therefore,a model is proposed to evaluate and optimize the reliability and performance of cloud-based degraded systems subject to a mixed active and cold standby redundancy strategy.In this strategy,node switching is triggered by a continual monitoring and detection mechanism when active nodes fail.To evaluate the transient availability and the expected job completion rate of systems with such kind of strategy,a continuous-time Markov chain model is built on the state transition process and a numerical method is used to solve the model.To choose the optimal redundancy for the mixed strategy under system constraints,a greedy search algorithm is proposed after sensitivity analysis.Illustrative examples were presented to explain the process of calculating the transient probability of each system state and in turn,the availability and performance of the whole system.It was shown that the near-optimal redundancy solution could be obtained using the optimizationmethod.The comparison with optimization of the traditional mixed redundancy strategy proved that the system behavior was different using different kinds of mixed strategies and less redundancy was assigned for the new type of mixed strategy under the same system constraint.

A Cloud-Based BPM Architecture with User-End Distribution of Non-Compute-Intensive Activities and Sensitive Data

While cloud-based BPM（Business Process Management） shows potentials of inherent scalability and expenditure reduction,such issues as user autonomy,privacy protection and efficiency have popped up as major concerns.Users may have their own rudimentary or even full-edged BPM systems,which may be embodied by local EAI systems,at their end,but still intend to make use of cloud-side infrastructure services and BPM capabilities,which may appear as PaaS（Platform-as-a-Service） services,at the same time.A whole business process may contain a number of non-compute-intensive activities,for which cloud computing is over-provision.Moreover,some users fear data leakage and loss of privacy if their sensitive data is processed in the cloud.This paper proposes and analyzes a novel architecture of cloud-based BPM,which supports user-end distribution of non-compute-intensive activities and sensitive data.An approach to optimal distribution of activities and data for synthetically utilizing both user-end and cloud-side resources is discussed.Experimental results show that with the help of suitable distribution schemes,data privacy can be satisfactorily protected,and resources on both sides can be utilized at lower cost.

Cloud-based data management system for automatic real-time data acquisition from large-scale laying-hen farms

Management of poultry farms in China mostly relies on manual labor.Since such a large amount of valuable data for the production process either are saved incomplete or saved only as paper documents,making it very difficult for data retrieve,processing and analysis.An integrated cloud-based data management system(CDMS)was proposed in this study,in which the asynchronous data transmission,distributed file system,and wireless network technology were used for information collection,management and sharing in large-scale egg production.The cloud-based platform can provide information technology infrastructures for different farms.The CDMS can also allocate the computing resources and storage space based on demand.A real-time data acquisition software was developed,which allowed farm management staff to submit reports through website or smartphone,enabled digitization of production data.The use of asynchronous transfer in the system can avoid potential data loss during the transmission between farms and the remote cloud data center.All the valid historical data of poultry farms can be stored to the remote cloud data center,and then eliminates the need for large server clusters on the farms.Users with proper identification can access the online data portal of the system through a browser or an APP from anywhere worldwide.

FunnelCloud:a cloud-based system for exploring tornado events

Recent research has shown an increase in the number of extreme tornado outbreaks per year.The characterization of the spatio-temporal pattern of tornado events is therefore a critical task in the analysis of meteorological data.Currently,there are a large number of available meteorological datasets that can be used for such analysis.However,much of these data are distributed across multiple websites and are not accessible in a central location.This poses a significant challenge for a scientist who is interested in exploring meteorological patterns associated with tornado events.This paper presents a novel system which uses cloud-based technology for integrating,storing,exploring,analyzing,and visualizing meteorological data associated with tornado outbreaks.The system employs a novel NoSQL database schema and web services architecture for data integration and provides a user friendly interface that allows scientists to explore the spatio-temporal pattern of tornado events.Furthermore,scientists can use this interface to analyze the relationship between different meteorological variables and properties of tornado outbreaks using a number of spatio-temporal statistical and data mining methods.The efficacy of the system is demonstrated on a use case centered on the analysis of climatic indicators of large spatio-temporally clustered tornado outbreaks.

Cloud-Based Software Development Lifecycle:A Simplified Algorithm for Cloud Service Provider Evaluation with Metric Analysis

At present,hundreds of cloud vendors in the global market provide various services based on a customer’s requirements.All cloud vendors are not the same in terms of the number of services,infrastructure availability,security strategies,cost per customer,and reputation in the market.Thus,software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental activities.Thus,there is a need to evaluate various cloud service providers(CSPs)and platforms before choosing a suitable vendor.Already existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service attributes.However,they require more time to collect data,simulate and evaluate the vendor.The proposed work compares various CSPs in terms of major metrics,such as establishment,services,infrastructure,tools,pricing models,market share,etc.,based on the comparison,parameter ranking,and weightage allocated.Furthermore,the parameters are categorized depending on the priority level.The weighted average is calculated for each CSP,after which the values are sorted in descending order.The experimental results show the unbiased selection of CSPs based on the chosen parameters.The proposed parameter-ranking priority level weightage(PRPLW)algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.

Cloud-Base Distribution and Cirrus Properties Based on Micropulse Lidar Measurements at a Site in Southeastern China

The cloud fraction （CF） and cloud-base heights （CBHs）, and cirrus properties, over a site in southeastern China from June 2008 to May 2009, are examined by a ground-based lidar. Results show that clouds occupied the sky 41% of the time. Significant seasonal variations in CF were found with a maximum/minimum during winter/summer and similar magnitudes of CF in spring and autumn. A distinct diurnal cycle in the overall mean CF was seen. Total, daytime, and nighttime annual mean CBHs were 3.05 ± 2.73 km, 2.46 ± 2.08 kin, and 3.51 ± 3.07 km, respectively. The lowest/highest CBH occurred around noon/midnight. Cirrus clouds were present ～36.2% of the time at night with the percentage increased in summer and decreased in spring. Annual mean values for cirrus geometrical properties were 8.89 ± 1.65 km, 9.80 ± 1.70 kin, 10.73 ± 1.86 km and 1.83± 0.91 km for the base, mid-cloud, top height, and the thickness, respectively. Seasonal variations in cirrus geometrical properties show a maximum/minimum in summer/winter for all cirrus geometrical parameters. The mean cirrus lidar ratio for all cirrus cases in our study was ～ 25 ± 17 sr, with a smooth seasonal trend. The cirrus optical depth ranged from 0.001 to 2.475, with a mean of 0.34 ± 0.33. Sub-visual, thin, and dense cirrus were observed in ～12%, 43%, and 45% of the cases, respectively. More frequent, thicker cirrus clouds occurred in summer than in any other season. The properties of cirrus cloud over the site are compared with other lidar-based retrievals of midlatitude cirrus cloud properties.

A Novel Attribute-Based Encryption Approach with Integrity Verification for CAD Assembly Models

Cloud manufacturing is one of the three key technologies that enable intelligent manufacturing.This paper presents a novel attribute-based encryption(ABE)approach for computer-aided design(CAD)assembly models to effectively support hierarchical access control,integrity verification,and deformation protection for co-design scenarios in cloud manufacturing.An assembly hierarchy access tree(AHAT)is designed as the hierarchical access structure.Attribute-related ciphertext elements,which are contained in an assembly ciphertext(ACT)file,are adapted for content keys decryption instead of CAD component files.We modify the original Merkle tree(MT)and reconstruct an assembly MT.The proposed ABE framework has the ability to combine the deformation protection method with a content privacy of CAD models.The proposed encryption scheme is demonstrated to be secure under the standard assumption.Experimental simulation on typical CAD assembly models demonstrates that the proposed approach is feasible in applications.

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.

Renewable and Nonrenewable Energy Flow Resiliency for Day-to-Day Production and Consumption

Energy resilience is about ensuring a business and end-use consumers have a reliable,regular supply of energy and contingency measures in place in the event of a power failure,generating a source of power such as electricity for daily needs from an uninterrupted source of energy no matter either renewable or nonrenewable.Causes of resilience issues include power surges,weather,natural disasters,or man-made accidents,and even equipment failure.The human operational error can also be an issue for grid-power supply to go down and should be factored into resilience planning.As the energy landscape undergoes a radical transformation,from a world of large,centralized coal plants to a decentralized energy world made up of small-scale gas-fired production and renewables,the stability of electricity supply will begin to affect energy pricing.Businesses must plan for this change.The challenges that the growth of renewables brings to the grid in terms of intermittency mean that transmission and distribution costs consume an increasing proportion of bills.With progress in the technology of AI(Artificial Intelligence)integration of such progressive technology in recent decades,we are improving our resiliency of energy flow,so we prevent any unexpected interruption of this flow.Ensuring your business is energy resilient helps insulate against price increases or fluctuations in supply,becoming critical to maintaining operations and reducing commercial risk.In the form short TM(Technical Memorandum),this paper covers this issue.

Mode of Operation for Modification, Insertion, and Deletion of Encrypted Data

Due to the development of 5G communication,many aspects of information technology(IT)services are changing.With the development of communication technologies such as 5G,it has become possible to provide IT services that were difficult to provide in the past.One of the services made possible through this change is cloud-based collaboration.In order to support secure collaboration over cloud,encryption technology to securely manage dynamic data is essential.However,since the existing encryption technology is not suitable for encryption of dynamic data,a new technology that can provide encryption for dynamic data is required for secure cloudbased collaboration.In this paper,we propose a new encryption technology to support secure collaboration for dynamic data in the cloud.Specifically,we propose an encryption operation mode which can support data updates such as modification,addition,and deletion of encrypted data in an encrypted state.To support the dynamic update of encrypted data,we invent a new mode of operation technique named linked-block cipher(LBC).Basic idea of our work is to use an updatable random value so-called link to link two encrypted blocks.Due to the use of updatable random link values,we can modify,insert,and delete an encrypted data without decrypt it.

Energy Driven by Internet of Things Analytics and Artificial Intelligence

Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.

Improving Students’Participation and Collaboration With Adjusting Cloud Education Platform During the Real-Time Interactive Class

The purpose of this study was to find a way to promote the collaboration and interaction of students and bring about the growth of learners through feedback while taking advantage of real-time interactive class via video conferencing tools.Although real-time interactive class with using video conferencing tools had great advantages,but there were also limitations of active interaction.To this end,real-time interactive tool and cloud-based educational platform were applied to create cases of learner participation classes and analyze the cases.The convergence of real-time interactive class tools and cloud tools has been able to draw students’participation and collaboration in non-face-to-face situations,and it can be seen that it is very helpful in creating learner-centered educational activities based on communication and interaction with students.Through this,the application of the cloud-based educational platform in real-time interactive class could lead students to participate and collaborate even in non-face-to-face situations.

Software Defined Things in Manufacturing Networks

IoT technologies are being rapidly adopted for manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management. A recent on-demand model of manufacturing that is leveraging IoT technologies is called Cloud-Based Manufacturing. We propose a Software-Defined Industrial Internet of Things (SD-IIoT) platform for as a key enabler for cloud-manufacturing, allowing flexible integration of legacy shop floor equipment into the platform. SD-IIoT enables access to manufacturing resources and allows exchange of data between industrial machines and cloud-based manufacturing applications.

Remote Sensing of Tropical Cyclone Thermal Structure from Satellite Microwave Sounding Instruments: Impacts of Background Profiles on Retrievals

A variational retrieval system often requires background atmospheric profiles and surface parameters in its minimization process. This study investigates the impacts of specific background profiles on retrievals of tropical cyclone(TC) thermal structure. In our Microwave Retrieval Testbed(MRT), the K-means clustering algorithm is utilized to generate a set of mean temperature and water vapor profiles according to stratiform and convective precipitation in hurricane conditions. The Advanced Technology Microwave Sounder(ATMS) observations are then used to select the profiles according to cloud type. It is shown that the cloud-based background profiles result in better hurricane thermal structures retrieved from ATMS observations. Compared to the Global Positioning System(GPS) dropsonde observations, the temperature and specific humidity errors in the TC inner region are less than 3 K and 2.5 g kg^(–1), respectively, which are significantly smaller than the retrievals without using the cloud-based profiles. Further experiments show that all the ATMS observations could retrieve well both temperature and humidity structures, especially within the inner core region. Thus, both temperature and humidity profiles derived from microwave sounding instruments in hurricane conditions can be reliably used for evaluation of the storm intensity with a high fidelity.

Channeling the water data deluge:a system for flexible integration and analysis of hydrologic data

The hydrologic cycle and understanding the relationship between rainfall and runoff is an important component of earth system science,sustainable development,and natural disasters caused by floods.With this in mind,the integration of digital earth data for hydrologic sciences is an important area of research.Currently,it takes a tremendous amount of effort to perform hydrologic analysis at a large scale because the data to support such analyses are not available on a single system in an integrated format that can be easily manipulated.Furthermore,the state-of-the-art in hydrologic data integration typically uses a rigid relational database making it difficult to redesign the data model to incorporate new data types.The HydroCloud system incorporates a flexible document data model to integrate precipitation and stream flow data across spatial and temporal dimensions for large-scale hydrologic analyses.In this paper,a document database schema is presented to store the integrated data-set along with analysis tools such as web services for data access and a web interface for exploratory data analysis.The utility of the system is demonstrated based on a scientific workflow that uses the system for both exploratory data analysis and statistical hypothesis testing.