Real-Time Edge Computing Services for Internet of Things-based Cloud Networks DOI
Manish Srivastava,

M P Sunil,

Arun Kumar Marandi

et al.

Published: March 15, 2024

The Internet of Things (IoT) has enabled the development real-time edge computing institute for distributed cloud networks. This technology makes it possible devices connected to process elevens and respond problems or requests in a timely manner. Edge provides distributed, low-latency platform processing at network, closer point where bestial collected. Spill result, this reduces cost associated with cloud-based services while also minimizing latency, ensuring fast reliable responsiveness. Furthermore, verging performing complex analytics machine learning tasks reducing burden on institute. allows networks scale easily, reliability scalability maintained through computing.

Language: Английский

Elevating IoT healthcare security using ProSRN and ICOM methodologies for effective threat management DOI

Y. Sowjanya,

S. Gopalakrishnan,

Rakesh Kumar

et al.

International Journal of Information Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

Language: Английский

Citations

0
A novel task offloading model for IoT: enhancing resource utilization with actor-critic-based reinforcement learning DOI

G. Saranya,

K Kumaran,

M. Vivekanandan

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 17, 2025

Language: Английский

Citations

0
Reinforcement learning based Secure edge enabled multi task scheduling model for internet of everything applications DOI Creative Commons

Thiruppathy Kesavan,

R. Venkatesan,

Wai Kit Wong

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 20, 2025

The fast growth of the Internet Everything (IoE) has resulted in an exponential rise network data, increasing demand for distributed computing. Data collection and management with job scheduling using wireless sensor networks are considered essential requirements IoE environment; however, security issues over data on online platform energy consumption must be addressed. Secure Edge Enabled Multi-Task Scheduling (SEE-MTS) model been suggested to properly allocate jobs across machines while considering availability relevant copies. proposed approach leverages edge computing enhance efficiency applications, addressing growing need manage huge generated by devices. system ensures user protection through dynamic updates, multi-key search generation, encryption, verification result accuracy. A MTS mechanism is employed optimize usage, which allocates slots various processing tasks. Energy assessed tasks queues, preventing node overloading minimizing disruptions. Additionally, reinforcement learning techniques applied reduce overall task completion time minimal data. Efficiency have improved due reduced energy, delay, reaction, times. Results indicate that SEE-MTS achieves utilization 4 J, a delay 2s, reaction 4s, at 89%, level 96%. With computation 6s, offers security, reducing times, although real-world implementation may limited number devices incoming

Language: Английский

Citations

0
Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm DOI Creative Commons
Faris Kateb, Mahmoud Ragab, Felwa Abukhodair

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 17, 2025

Language: Английский

Citations

0
6G Cyber Physical System Based Smart Healthcare Modelling by Mobile Edge Network and Artificial Intelligence DOI
Kama Ramudu, Sushil Kumar,

C. K. Shahnazeer

et al.

Wireless Personal Communications, Journal Year: 2024, Volume and Issue: unknown

Published: May 9, 2024

Language: Английский

Citations

3
Edge computing in future wireless networks: A comprehensive evaluation and vision for 6G and beyond DOI Creative Commons
Mustafa Ergen, Bilal Saoud, Ibraheem Shayea

et al.

ICT Express, Journal Year: 2024, Volume and Issue: 10(5), P. 1151 - 1173

Published: Aug. 17, 2024

Language: Английский

Citations

2
Enabling Pandemic-Resilient Healthcare: Edge-Computing-Assisted Real-Time Elderly Caring Monitoring System DOI Creative Commons
Muhammad Zubair Islam, A. S. M. Sharifuzzaman Sagar, Hyung Seok Kim

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(18), P. 8486 - 8486

Published: Sept. 20, 2024

Over the past few years, life expectancy has increased significantly. However, elderly individuals living independently often require assistance due to mobility issues, symptoms of dementia, or other health-related challenges. In these situations, high-quality care systems for aging population innovative approaches guarantee Quality Service (QoS) and Experience (QoE). Traditional remote methods face several challenges, including high latency poor service quality, which affect their transparency stability. This paper proposes an Edge Computational Intelligence (ECI)-based haptic-driven ECI-TeleCaring system caring monitoring people. It utilizes a Software-Defined Network (SDN) Mobile Computing (MEC) reduce enhance responsiveness. Dual Long Short-Term Memory (LSTM) models are deployed at edge enable real-time location-aware activity prediction ensure QoS QoE. The results from simulation demonstrate that proposed is proficient in managing transmission data real time without with recognition model by communication under 2.5 ms (more than 60%) 11∼12 (60∼95%) 10 1000 packets, respectively. also show ensures trade-off between stability QoE perspectives. Moreover, serves as testbed implementing, investigating, elder telecaring services QoS/QoE provisioning. facilitates technological parameters along network delay packet loss, it oversees exchange master domain (human operator) slave (telerobot).

Language: Английский

Citations

2
An optimized deep learning framework to enhance internet of things and fog based health care monitoring paradigm DOI
Vuppala Sukanya,

Prashant Jawade,

M. Jayanthi

et al.

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

Published: March 15, 2024

Language: Английский

Citations

1
An Effective Intrusion Detection System for Edge Computing Using ConvNeXt and ResNet152V2 DOI
Vasavi Sravanthi Balusa,

K. Srinivas

International Journal of Computational Intelligence and Applications, Journal Year: 2024, Volume and Issue: 23(03)

Published: April 25, 2024

The proliferation of edge computing, driven by network applications and wireless devices, increases the vulnerability confidential information to security risks. In this environment, existing intrusion detection algorithms fail satisfy requirements prompt responses, heavy load management, inadequate extraction features, imprecise model classification. work, imbalanced data problem in input dataset is mitigated using Data Augmentation Generative Adversarial Network (DAGAN). Next, an efficient ConvNeXt-based feature method created retrieve key characteristics from for every class. Last, multi-attack achieved through deployment optimized deep learning classifier based on ResNet152V2. Furthermore, simulation experiments are carried out ToN-IoT BoT-IoT datasets, outcomes demonstrate that our suggested performs better than models, with accuracy levels 99.20% 99.31%, respectively. These findings show approach successful building refining large-scale IDS computing framework.

Language: Английский

Citations

1
Era of Sentinel Tech: Charting Hardware Security Landscapes Through Post-Silicon Innovation, Threat Mitigation and Future Trajectories DOI Creative Commons
M. B. Srinivas, E. Konguvel

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 68061 - 68108

Published: Jan. 1, 2024

To meet the demanding requirements of VLSI design, including improved speed, reduced power consumption, and compact architectures, various IP cores from trusted untrusted platforms are often integrated into a single System-on-Chip (SoC). However, this convergence poses significant security challenge, as adversaries can exploit it to extract unauthorized information, compromise system performance, obtain secret keys. Meanwhile, traditional CMOS features have limitations in addressing hardware vulnerabilities threats, so promising post-silicon technologies offer potential solutions. Beyond-CMOS avenues fortify through distinct physical properties nontraditional computing paradigms. These advancements bolster authentication processes, enhance key generation mechanisms, ensure integrity resilience against side-channel attacks, Trojans quantum-resistant cryptography securing systems. This article provides detailed review security, encompassing identification mitigation implementation robust countermeasures, utilization innovative primitives, methodologies offered by emerging resist threats.Moreover, strategies address challenges, explore future directions, outline plans for achieving further research outcomes been put forth field.

Language: Английский

Citations

1