Related Experiment Videos
A blockchain-enabled scalable and secure architecture for cloud big data storage using sharding and swarming
R N Karthika1, C Valliyammai2, V UmaRani3
1Department of IT, Saveetha Engineering College, Chennai, Tamil Nadu, India. karthikanarasiman@gmail.com.
Scientific Reports
|May 20, 2026
Summary
This study integrates blockchain with cloud big data storage, using sharding and swarming for enhanced scalability and speed. An optimized Proof of Work mechanism ensures efficiency and security in decentralized data management.
Area of Science:
- Computer Science
- Data Science
- Information Security
Background:
- Cloud computing offers on-demand resources but faces scalability challenges with big data.
- Current big data systems struggle with elasticity and rapid scaling under fluctuating workloads.
- Regulatory compliance and real-time analytics are significant hurdles for cloud-based big data.
Purpose of the Study:
- To develop a secure and efficient framework for big data storage in the cloud using blockchain technology.
- To address the scalability limitations of blockchain-based big data systems.
- To enhance the reliability and analytical value of big data through decentralized management.
Main Methods:
- Integration of blockchain technology with cloud-based big data storage.
- Implementation of sharding to partition data for improved manageability.
- Application of swarming techniques for parallel and low-latency data retrieval.
- Optimization of the Proof of Work (PoW) mechanism for efficiency and fairness.
Main Results:
- A novel architecture combining sharding and swarming to overcome blockchain scalability issues.
- Enhanced data security and reliability through decentralized, cryptographically secure management.
- Reduced latency and improved access speed via parallel data retrieval.
- Increased computational fairness and reduced energy consumption with an optimized PoW.
Conclusions:
- The integration offers a transformative solution for secure, scalable, and high-performance cloud big data management.
- This approach advances decentralized applications and analytics capabilities.
- Blockchain technology provides a robust framework for overcoming big data challenges in cloud environments.
Related Concept Videos
Issues And Trends In Healthcare Delivery System
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Maximum Size of Aggregate
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can result...
Scale-Up Processes
The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
Storage
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...
Distributed Loads
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
State Space Representation
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Consider an RLC circuit, a...