A Conceptual Architecture in Decentralizing Computing, Storage, and Networking Aspect of IoT Infrastructure

Oktian, Yustus Eko; Witanto, Elizabeth Nathania; Lee, Sang-Gon

doi:10.3390/iot2020011

Cited by 22 publications

(8 citation statements)

References 32 publications

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“…With the advanced emergent of information divergence, mechanism of cloud storage is becoming a popular and necessary one in day-to-day life. e cloud storage aids in enabling the individuals or enterprises for attaining cloud data anywhere and at any time, from which this kind of feature brings out excellent convenience for living [1]. As with the quantity of Internet of ings (IoT) devices increases, it is more vital than ever to provide a safe framework for storing as well as using IoT data either in or out of Smart nodes.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Consensus Blockchain and IPFS-Based Data Aggregation for Efficient Data Storage Scheme

Subathra

Antonidoss

Singh

2022

Security and Communication Networks

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By the development and advancement of blockchain technique, Internet of Things (IoT) proliferation driven devices and the application of blockchain-enabled IoT alter the view and operating infrastructure of the smart networks. The blockchain is responsible for supporting decentralized systems and offers secured means of authentication, management, and access to IoT system thereby deploying smart contracts offered by Ethereum. The increasing demand and the blockchain expansion generate huge volume of sensitive data. The growing demand and expansion of blockchain-IoT systems is generating large volume of sensitive data. Furthermore, distributed denial-of-service (DDoS) attacks are regarded as the most promising threats for smart contracts in the blockchain-based systems. Therefore, there is a need to detect and classify the attack type and the data should be stored in server more securely with the use of blockchain and data aggregation method. For this purpose, this presented technique aims at introducing decentralized consensus blockchain and Interplanetary file system (IPFS) based data aggregation for effective classification and data storage. The attack is detected using meta-hyperparameter random forest (MHP-RF) classifier. Once the attack is detected, the transaction information is stored in server securely by means of smart contract-based blockchain system. The transaction handling stage classifies the transaction type as normal or abnormal one which then followed by execution of business logic by smart contract thereby appending the transaction of blockchain in the network cloud. The consensus blockchain technique is employed with the use of PoW-enabled scheme integrated with Elgamal-based data aggregation. Therefore, the system security is improved and the intrusion is prevented greatly. The performance analysis of the system is analyzed in terms of accuracy, precision, recall, F-score, Encryption time, decryption time, execution time, and space complexity. The attained outcomes are compared with traditional approaches to prove the effectiveness of proposed strategy. The proposed system is said to be effective in time consumption, classifier performance, and in overcoming space complexity issues.

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Section: Introductionmentioning

confidence: 99%

Decentralized Consensus Blockchain and IPFS-Based Data Aggregation for Efficient Data Storage Scheme

Subathra

Antonidoss

Singh

2022

Security and Communication Networks

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“…Network and Firewall Settings: Depending on the cloud provider, organizations might need to adjust network and firewall settings to allow their Python application to access the MySQL database; hence, this could involve setting up security groups, firewall rules or configuring networking settings in the cloud platform's dashboard [31][32][33][34][35].…”

Section: Installmentioning

confidence: 99%

Advancing Cloud Technology Security: Leveraging High-Level Coding Languages like Python and SQL for Strengthening Security Systems and Automating Top Control Processes

Olabanji

2023

JSRR

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In today's dynamic business environment, staying ahead of competitors requires the integration of cutting-edge technologies into organizational processes. Cloud Computing, a transformative technological advancement, offers a promising avenue for achieving operational efficiency and innovation. This paper explores the integration of Cloud Computing with two powerful coding languages, Python and SQL, to enhance cloud security and automate control processes. Cloud Computing's adoption has revolutionized resource management through virtualization and diverse computing models. However, it also introduces security challenges like data breaches and unauthorized access. Python and SQL emerge as essential tools for addressing these challenges and automating various control processes. Python's versatility empowers organizations to establish sophisticated security protocols and automate tasks such as intrusion detection, anomaly detection, real-time monitoring, and computer vision. On the other hand, SQL's role involves automating control processes like resource provisioning, scaling, backup, recovery, access control, and database management. Integrating Python and SQL offers a holistic approach to cloud security enhancement. However, challenges such as skill set requirements, code quality, integration, maintenance, scalability, monitoring, and data privacy must be addressed. Fortunately, solutions like Snowpark, dbt, Hex, and Dataiku provide platforms that unify various programming languages, fostering collaboration and streamlining tasks. This convergence of Cloud Computing with Python and SQL presents numerous benefits. Automation enhances efficiency, reduces human error, and ensures consistent control process execution. This synergy allows organizations to achieve scalability, cost savings, improved security, and comprehensive monitoring and reporting. As institutions increasingly d on Cloud Computing to drive innovation and competitiveness, the importance of fortifying these systems against evolving threats cannot be overstated. Integrating Python and SQL represents a pivotal juncture in achieving this goal. By harnessing their combined power, organizations can create robust security mechanisms, streamline operations, and promote cross-functional collaboration. As the digital landscape evolves, embracing this approach is crucial for sustaining success in a rapidly changing environment.

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“…According to Statista, the number of IoT-connected devices will reach 30 billion by 2030 [ 1 ]. Despite its rapid expansion, the IoT still faces major computational, storage, and networking challenges [ 2 ]. Most of these are mainly due to the centralized structure of the current IoT architecture.…”

Section: Introductionmentioning

confidence: 99%

“…However, it became clear that sending large amounts of data over the network to a server is not practical due to bandwidth and privacy constraints. Thus, distributed IoT architecture has aroused researcher interest as an alternative to the outmoded centralized paradigm and which may be able to revolutionize the IoT infrastructure [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems

Firouzi

Rahmani

2022

Sensors

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Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds.

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A Conceptual Architecture in Decentralizing Computing, Storage, and Networking Aspect of IoT Infrastructure

Cited by 22 publications

References 32 publications

Decentralized Consensus Blockchain and IPFS-Based Data Aggregation for Efficient Data Storage Scheme

Decentralized Consensus Blockchain and IPFS-Based Data Aggregation for Efficient Data Storage Scheme

Advancing Cloud Technology Security: Leveraging High-Level Coding Languages like Python and SQL for Strengthening Security Systems and Automating Top Control Processes

5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems

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