Optimized Retrieval of Data from Cloud using Hybridization of Bellstra Algorithm

Kumar, K. Yashwanth; Kumar, N. Jagadish; Dhinakaran, D.; Udhaya Sankar, S. M.; Kumar, U. Janish; Yuvaraj, V.

doi:10.1109/wconf58270.2023.10234974

Cited by 16 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advent of the IoT and machine learning has transformed the landscape of aquaculture management. By leveraging sensor technology, wireless connectivity, and advanced data analytics, the IoT empowers fish farm operators to collect real-time environmental data with precision and efficiency [19][20][21]. ML algorithms, including RF, SVM, Gradient Boosting Machines (GBMs), and Neural Networks, offer the ability to process this data and make informed decisions in real-time.…”

Section: Amentioning

confidence: 99%

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

Dhinakaran

2023

IJRITCC

View full text Add to dashboard Cite

In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming.

show abstract

Section: Amentioning

confidence: 99%

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

Dhinakaran

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…PoS provides an effective means of managing key transactions securely while supporting the broader objectives of our integration. Authorized parties can retrieve encrypted keys from the blockchain by using their tokens to decrypt the stored transactions from cloud-based storage environment [30]. This retrieval process ensures that only those with the appropriate private keys can access the keys stored on the blockchain.…”

Section: Blockchain Integration With Oesmentioning

confidence: 99%

Privacy-Preserving Cloud Data Security: Integrating the Novel Opacus Encryption and Blockchain Key Management

2023

KSII TIIS

View full text Add to dashboard Cite

With the growing adoption of cloud-based technologies, maintaining the privacy and security of cloud data has become a pressing issue. Privacy-preserving encryption schemes are a promising approach for achieving cloud data security, but they require careful design and implementation to be effective. The integrated approach to cloud data security that we suggest in this work uses CogniGate: the orchestrated permissions protocol, index trees, blockchain key management, and unique Opacus encryption. Opacus encryption is a novel homomorphic encryption scheme that enables computation on encrypted data, making it a powerful tool for cloud data security. CogniGate Protocol enables more flexibility and control over access to cloud data by allowing for fine-grained limitations on access depending on user parameters. Index trees provide an efficient data structure for storing and retrieving encrypted data, while blockchain key management ensures the secure and decentralized storage of encryption keys. Performance evaluation focuses on key aspects, including computation cost for the data owner, computation cost for data sharers, the average time cost of index construction, query consumption for data providers, and time cost in key generation. The results highlight that the integrated approach safeguards cloud data while preserving privacy, maintaining usability, and demonstrating high performance. In addition, we explore the role of differential privacy in our integrated approach, showing how it can be used to further enhance privacy protection without compromising performance. We also discuss the key management challenges associated with our approach and propose a novel blockchain-based key management system that leverages smart contracts and consensus mechanisms to ensure the secure and decentralized storage of encryption keys.

show abstract

Swarm Intelligence with a Chaotic Leader and a Salp algorithm: HDFS optimization for reduced latency and enhanced availability

Jagadish Kumar,

Dhinakaran,

Naresh Kumar

et al. 2024

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

SummaryThe Hadoop distributed file system (HDFS) effectively manages data by segmenting it into blocks distributed across DataNodes in its cluster. While default block sizes in Hadoop 2.x and 1.x are 128 and 64 MB, respectively, they can be customized for larger files. HDFS ensures data reliability by replicating blocks across multiple DataNodes, but this can introduce high latency in cloud storage during heavy network traffic, particularly in big data processing. To address this, we introduce swarm intelligence with a chaotic leader and a salp (SI‐CLS) optimization algorithm. This algorithm reduces network traffic between racks in HDFS by optimizing block distribution. The SI‐CLS algorithm calculates a fitness value for each block, aiming to increase data availability and reduce latency. Performance metrics, including latency, data availability, and load balancing, indicate the effectiveness of SI‐CLS. Adopting this algorithm enhances HDFS performance, ensuring better data availability and lower latency, resulting in improved system reliability.

show abstract

Optimized Retrieval of Data from Cloud using Hybridization of Bellstra Algorithm

Cited by 16 publications

References 28 publications

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

Privacy-Preserving Cloud Data Security: Integrating the Novel Opacus Encryption and Blockchain Key Management

Swarm Intelligence with a Chaotic Leader and a Salp algorithm: HDFS optimization for reduced latency and enhanced availability

Contact Info

Product

Resources

About