IoT-Enabled Secure and Scalable Cloud Architecture for Multi-User Systems: A Hybrid Post-Quantum Cryptographic and Blockchain-Based Approach Toward a Trustworthy Cloud Computing

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 105479 - 105498

Published: Jan. 1, 2023

Cloud computing has revolutionized organizational operations by providing convenient, on-demand access to resources. The emergence of the Internet Things (IoT) introduced a new paradigm for collaborative computing, leveraging sensors and devices that generate process vast amounts data, thereby resulting in challenges related scalability security, making significance conventional security methods even more pronounced. Consequently, this paper, we propose novel Scalable Secure Architecture (SSCA) integrates IoT cryptographic techniques, aiming develop scalable trustworthy cloud systems, thus enabling multi-user systems facilitating simultaneous resources multiple users. design adopts decentralized approach, utilizing nodes handle user requests efficiently incorporates Multicast Broadcast Rekeying Algorithm (MBRA) ensure privacy confidentiality information, hybrid cryptosystem combines MBRA, Post Quantum Cryptography (PQC) blockchain technology. Leveraging devices, architecture gathers data from distributed sensing ensures collected information through robust MBRA-PQC encryption algorithms, while confidential is stored immutable records. proposed approach applied several datasets effectiveness validated various performance metrics, including response time, throughput, scalability, reliability. results highlight SSCA, showcasing notable reduction time 1.67 seconds 0.97 250 1000 respectively, comparison MHE-IS-CPMT. Likewise, SSCA demonstrated significant improvements AUC values, exhibiting enhancements 6.30%, 6.90%, 7.60%, 7.30% at 25-user level, impressive gains 5.20%, 9.30%, 11.50%, 15.40% 50-user level when compared MHE-IS-CPMT, EAM, SCSS, SHCEF models, respectively.

Language: Английский

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A Hybrid Framework Leveraging Whale Optimization and Deep Learning With Trust-Index for Attack Identification in IoT Networks

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 36296 - 36310

Published: Jan. 1, 2024

The rise of smart cities, homes, and health powered by the Internet Things (IoT) presents significant challenges in design, deployment, security. seamless data processing across a complex network interconnected devices unprotected conditions makes it vulnerable to potential breaches, underscoring need for robust security at various levels network. Traditional methods based on statistics often struggle comprehend patterns provide desired level This work proposes novel hybrid framework that combines Whale Optimization Deep Learning with trust-index identify malicious nodes engaging attacks such as DoS, DDoS, Drop attack, Tamper Attacks, thus enhancing IoT node developed first calculates score drop tamper replay multiple-max attack. Subsequently, utilizes trust index Optimized Neural Network (ONN) model effectively node. neural optimization is achieved through fitness function determines optimal weights using Algorithm. proposed has been tested varying sizes, comprising 100, 500, 1000 nodes. resulting outcomes were evaluated against benchmark Logical regression (LR),Random Forest (RF), Support Vector Machine (SVM), Bayesian models, ANN, Elephant herding (EHO),and Lion algorithm (LA) metrics like specificity, sensitivity, accuracy, precision, False Positive Rate (FPR), Negative (FNR), Discovery (FDR), Error, F1 score, Matthews Correlation Coefficient (MCC), Predictive Value (NPV). results reveal notable enhancement accuracy (26.63%, 13.04%, 17.78%, 30.52%, 22.45%, 4.26%, 2.24%) 100-node when compared methods. Furthermore, consistently demonstrates strong performance even applied larger networks higher count.

Language: Английский

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Anonymous Authentication Scheme Based on Physically Unclonable Function and Biometrics for Smart Cities

Engineering Reports, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 20, 2024

ABSTRACT Smart cities amalgamate technologies such as Internet of Things, big data analytics, and cloud computing to collect analyze large volumes from varied sources which facilitate intelligent surveillance, enhanced energy management systems, environmental monitoring. The ultimate goal these smart is offer city residents with better services, opportunities, quality life. However, the vulnerabilities in underlying technologies, interconnection heterogeneous devices, transfer over open public channels expose networks a myriad security privacy threats. Therefore, many solutions have been presented literature. majority techniques still numerous performance, privacy, challenges that need be addressed. To this end, we present an anonymous authentication scheme for based on physically unclonable function user biometrics. Its formal analysis using Real‐Or‐Random (ROR) model demonstrates robustness negotiated session key against active passive attacks. In addition, informal shows it supports salient functional features mutual authentication, agreement, perfect secrecy, anonymity, untraceability. It also shown withstand typical threats side‐channeling, offline guessing, disclosure, eavesdropping, hijacking, privileged insider, impersonation Moreover, comparative performance incurs lowest computation costs at relatively low communication overheads.

Language: Английский

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Development of Heuristic Strategy With Hybrid Encryption for Energy Efficient and Secure Data Storage Scheme in Blockchain‐Based Mobile Edge Computing

Transactions on Emerging Telecommunications Technologies, Journal Year: 2025, Volume and Issue: 36(2)

Published: Jan. 24, 2025

ABSTRACT Internet of Things (IoT) devices is extensively employed to collect physiological health data and provide diverse services end‐users. Nevertheless, in recent applications, cloud computing‐based IoT proves beneficial for standard storage ensuring high‐security information sharing. Due limitations battery capacity, storage, computing power, are often considered resource‐constrained. Consequently, signing by devices, aimed at integrity authentication, typically demands significant computational resources. Unsafe high latency as the major issues IoT‐based mechanism duplicating misusing while it stored database. Hence, blockchain technologies needed security over data. research implement an efficient blockchain‐based system mobile edge computing, safeguarding from unauthorized access. In this approach, contains four layers that layer, entity block‐chain layer. The user's optimal location where storing find out using proposed Hybrid Battle Royale with Archimedes Optimization Algorithm (HBRAOA). key‐based homomorphic encryption algorithm Elliptic Curve Cryptography (ECC) introduced encrypt most key, secure storage. This method leverages same HBRAOA enhance efficiency. Next, digital signature demonstrated give authorization user, distributed Thus, indexes shared layer avoid fault tolerance tamper‐proofing. Finally, receives valuable encrypted data, authenticated users known keys able access decrypting them. result analysis shows performance developed model, which attains 27%, 98%, 35%, 18% enhanced than Particle Swarm (PSO)‐ECC, Black Widow (BWO)‐ECC, (BRO)‐ECC (AOA)‐ECC. efficiency scheme optimization strategy validated conducting several similarity measures conventional methods.

Language: Английский

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Analyzing Energy Consumption in IoT, Fog, and Blockchain Ecosystems

Advances in environmental engineering and green technologies book series, Journal Year: 2025, Volume and Issue: unknown, P. 127 - 166

Published: April 4, 2025

The rise of Internet Things (IoT) devices, fog computing, and blockchain technologies has reshaped modern distributed systems, but energy consumption poses a critical challenge. This chapter explores patterns in IoT, fog, ecosystems, emphasizing the importance efficiency. It discusses interplay between these usage IoT network protocols, cloud edge computing impacts, needs challenges nodes. also examines implications consensus mechanisms like proof-of-work proof-of-stake, sustainable energy-efficient strategies such as machine learning. Real-world examples highlight successful deployments smart cities green systems. concludes by stressing need for practices designing implementing digital future.

Language: Английский

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Unveiling the Potential Pattern Representation of RNA 5-Methyluridine Modification Sites Through a Novel Feature Fusion Model Leveraging Convolutional Neural Network and Tetranucleotide Composition

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 10023 - 10035

Published: Jan. 1, 2024

The 5-Methyluridine (m5U), predominantly present in RNA and especially enriched transfer (tRNA), significantly enhances translational accuracy protein synthesis by ensuring precise genetic information decoding optimal tRNA functionality within cellular mechanisms. identification of m5U modification sites is crucial, as this has gained significant attention diseases such breast cancer, stress response, viral infections, offering insights into its molecular mechanisms regulatory functions disease contexts. Nevertheless, due to the arduous nature, intricate procedures, reliance on sophisticated expensive instrumentation, need for specialized expertise, conventional biochemical approaches identifying result substantial resource expenditures notable temporal investments. Consequently, pressing a efficient computational method highlights urgency alternative sites. In study, we introduce novel approach called "Deep-m5U," which combines strengths Convolutional Neural Networks (CNNs) tetranucleotide composition accurately identify methyluridine improve overall performance. developed Deep-m5U leverages CNNs detect protein-coding regions capture relevant motifs, while incorporating tetra-nucleotide global compositional characteristics, resulting more robust model that We evaluated two publicly available benchmark datasets: full transcript mature mRNA datasets. Our results showcase superior performance, achieving accuracies 91.26% 95.63% respectively, surpassing current cutting-edge methods. Moreover, open-source code freely accessible at: https://github.com/waleed551/Deep-m5U.

Language: Английский

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Delay-cost computation offloading for on-board emergency tasks in LEO satellite edge computing networks

Future Generation Computer Systems, Journal Year: 2025, Volume and Issue: unknown, P. 107797 - 107797

Published: March 1, 2025

Language: Английский

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A model study of teaching method reform of computer laboratory course integrating internet of things technology

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(4), P. e0298534 - e0298534

Published: April 18, 2024

The Internet of Things (IoT) is gradually changing the way teaching and learning take place in on-campus programs. In particular, face capture services improve student concentration to create an efficient classroom atmosphere by using recognition algorithms that support end devices. However, reducing response latency executing analysis effectively real-time still challenging. For this reason, paper proposed a pedagogical model for IoT devices based on edge computing (TFREC). Specifically, research first service-based algorithm optimize accuracy recognition. addition, service deployment method obtain best strategy reduce algorithm. Finally, comparative experimental results demonstrate TFREC has 98.3% 72 milliseconds terms time. This significant advancing optimization methods school-based courses, meanwhile, providing beneficial insights application field education.

Language: Английский

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A Two-Phase Blockchain-Enabled Framework for Securing Internet of Medical Things Systems

Internet of Things, Journal Year: 2024, Volume and Issue: 28, P. 101335 - 101335

Published: Aug. 24, 2024

The healthcare industry has witnessed a transformative impact due to recent advancements in sensing technology, coupled with the Internet of Medical Things (IoMTs)-based systems. Remote monitoring and informed decision-making have become possible by leveraging an integrated platform for efficient data analysis processing, thereby optimizing management healthcare. However, this is collected, processed, transmitted across interconnected network devices, which introduces notable security risks escalates potential vulnerabilities throughout entire processing pipeline. Traditional approaches rely on computational complexity face challenges adequately securing sensitive against evolving threats, thus necessitating robust solutions that ensure trust, enhance security, maintain confidentiality integrity. To address these challenges, paper two-phase framework integrates blockchain technology IoMT trust computation, resulting secure cluster supports quality-of-service (QoS) data. first phase utilizes decentralized interplanetary file system hashing functions create smart contract device registration, establishing resilient storage encrypts data, improves fault tolerance, facilitates access. In second phase, communication overhead optimized considering power levels, ranges, computing capabilities alongside contract. evaluates index QoS each node facilitate clustering based capabilities. We implemented proposed using OMNeT++ simulator C++ programming language evaluated cutting-edge terms attack detection, energy consumption, packet delivery ratio, throughput, latency. qualitative results demonstrated enhanced detection 6.00%, 18.00%, 20.00%, 27.00%, reduced consumption 6.91%, 8.19%, 12.07%, 17.94%, improved ratio 3.00%, 9.00%, 10.00%, increased throughput 7.00%, 8.00%, 11.00%, 13.00%, decreased latency 4.90%, 8.81%, 11.54%, 20.63%, state-of-the-art methods was supported statistical analysis.

Language: Английский

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Task ordering in multiprocessor embedded system using a novel hybrid optimization model

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: April 23, 2024

Language: Английский

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