AVirtual Cloud Storage Architecture for Enhanced Data Security

The sensitive data stored in the public cloud by privileged users,such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and hackers.The proposed Virtual Cloud Storage Archi-tecture is primarily concerned with data integrity and confidentiality,as well as availability.To provide confidentiality and availability,thefile to be stored in cloud storage should be encrypted using an auto-generated key and then encoded into distinct chunks.Hashing the encoded chunks ensured thefile integrity,and a newly proposed Circular Shift Chunk Allocation technique was used to determine the order of chunk storage.Thefile could be retrieved by performing the opera-tions in reverse.Using the regenerating code,the model could regenerate the missing and corrupted chunks from the cloud.The proposed architecture adds an extra layer of security while maintaining a reasonable response time and sto-rage capacity.Experimental results analysis show that the proposed model has been tested with storage space and response time for storage and retrieval.The VCSA model consumes 1.5x(150%)storage space.It was found that total storage required for the VCSA model is very low when compared with 2x Replication and completely satisfies the CIA model.The response time VCSA model was tested with different sizedfiles starting from 2 to 16 MB.The response time for storing and retrieving a 2 MBfile is 4.96 and 3.77 s respectively,and for a 16 MBfile,the response times are 11.06 s for storage and 5.6 s for retrieval.

Fuzzy User Access Trust Model for Cloud Access Control

Cloud computing belongs to a set of policies,protocols,technologies through which one can access shared resources such as storage,applications,net-works,and services at relatively low cost.Despite the tremendous advantages of cloud computing,one big threat which must be taken care of is data security in the cloud.There are a dozen of threats that we are being exposed to while avail-ing cloud services.Insufficient identity and access management,insecure inter-faces and Applications interfaces(APIs),hijacking,advanced persistent threats,data threats,and many more are certain security issues with the cloud platform.APIs and service providers face a huge challenge to ensure the security and integ-rity of both network and data.To overcome these challenges access control mechanisms are employed.Traditional access control mechanisms fail to monitor the user operations on the cloud platform and are prone to attacks like IP spoofing and other attacks that impact the integrity of the data.For ensuring data integrity on cloud platforms,access control mechanisms should go beyond authentication,identification,and authorization.Thus,in this work,a trust-based access control mechanism is proposed that analyzes the data of the user behavior,network beha-vior,demand behavior,and security behavior for computing trust value before granting user access.The method that computes thefinal trust value makes use of the fuzzy logic algorithm.The trust value-based policies are defined for the access control mechanism and based on the trust value outcome the access control is granted or denied.

A Cloud Service Architecture for Analyzing Big Monitoring Data

Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures,platforms, and applications. Analysis of monitoring data delivers insights of the system＇s workload and usage pattern and ensures workloads are operating at optimum levels. The analysis process involves data query and extraction, data analysis, and result visualization. Since the volume of monitoring data is big, these operations require a scalable and reliable architecture to extract, aggregate, and analyze data in an arbitrary range of granularity. Ultimately, the results of analysis become the knowledge of the system and should be shared and communicated. This paper presents our cloud service architecture that explores a search cluster for data indexing and query. We develop REST APIs that the data can be accessed by different analysis modules. This architecture enables extensions to integrate with software frameworks of both batch processing（such as Hadoop） and stream processing（such as Spark） of big data. The analysis results are structured in Semantic Media Wiki pages in the context of the monitoring data source and the analysis process. This cloud architecture is empirically assessed to evaluate its responsiveness when processing a large set of data records under node failures.

IoT-Enabled Autonomous System Collaboration for Disaster-Area Management

Timely investigating post-disaster situations to locate survivors and secure hazardous sources is critical,but also very challenging and risky.Despite first responders putting their lives at risk in saving others,human-physical limits cause delays in response time,resulting in fatality and property damage.In this paper,we proposed and implemented a framework intended for creating collaboration between heterogeneous unmanned vehicles and first responders to make search and rescue operations safer and faster.The framework consists of unmanned aerial vehicles(UAVs),unmanned ground vehicles(UGVs),a cloud-based remote control station(RCS).A light-weight message queuing telemetry transport(MQTT)based communication is adopted for facilitating collaboration between autonomous systems.To effectively work under unfavorable disaster conditions,antenna tracker is developed as a tool to extend network coverage to distant areas,and mobile charging points for the UAVs are also implemented.The proposed framework’s performance is evaluated in terms of end-to-end delay and analyzed using architectural analysis and design language(AADL).Experimental measurements and simulation results show that the adopted communication protocol performs more efficiently than other conventional communication protocols,and the implemented UAV control mechanisms are functioning properly.Several scenarios are implemented to validate the overall effectiveness of the proposed framework and demonstrate possible use cases.

Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

Active learning can be used for optimizing and speeding up the screening phase of systematic reviews.Running simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training data.This paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud technologies.We provide a technical explanation of the proposed cloud architecture and its usage.In addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM settings.Our analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing usage.The parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.