Results 121 to 130 of about 2,716,493 (376)

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Dynamic Traffic Engineering for Cooperative Fog-Cloud Environment: Trade-off Analysis of Cost and Utilization Under Different Load Conditions

Journal of Communications Software and Systems
Fog computing has become an attractive computing method for different IoT (Internet of Things) applications that require low latency and location awareness.
Md. Rahinur Rahman, Mirza Mohd Shahriar Maswood   +1 more
doaj   +1 more source

From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework

IEEE Access, 2017
Fog computing, an extension of cloud computing services to the edge of the network to decrease latency and network congestion, is a relatively recent research trend.
Opeyemi Osanaiye, Shuo Chen, Zheng Yan, Rongxing Lu, Kim-Kwang Raymond Choo, Mqhele Dlodlo   +5 more
doaj   +1 more source

Fog Data

Proceedings of the ASE BigData & SocialInformatics 2015, 2015
The size of multi-modal, heterogeneous data collected through various sensors is growing exponentially. It demands intelligent data reduction, data mining and analytics at edge devices. Data compression can reduce the network bandwidth and transmission power consumed by edge devices.
Dubey, Harishchandra, Yang, Jing, Constant, Nick, Amiri, Amir Mohammad, Yang, Qing, Mankodiya, Kunal   +5 more
openaire   +3 more sources

μ-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog computing Environments [PDF]

, 2023
Mohammad Goudarzi, Maria A. Rodriguez, Majid Sarvi, Rajkumar Buyya   +3 more
openalex   +1 more source

Astrocyte‐Guided Maturation of Neural Constructs in a Modular Biosynthetic Hydrogel for Biohybrid Neurotechnologies

Advanced Functional Materials, EarlyView.
A modular biosynthetic PVA–gelatin hydrogel crosslinked via visible‐light thiol‐ene chemistry is engineered as a coating for neural electrodes. Optimizing matrix composition and mechanical properties enables the hydrogel to support astrocytic populations that guide neural differentiation and functional maturation.
Martina Genta, Sofia Peressotti, Roberto Portillo‐Lara, Josef Goding, Rylie Green   +4 more
wiley   +1 more source

Fog Computing Resource Optimization: A Review on Current Scenarios and Resource Management

مجلة بغداد للعلوم, 2019
The unpredictable and huge data generation nowadays by smart computing devices like (Sensors, Actuators, Wi-Fi routers), to handle and maintain their computational processing power in real time environment by centralized cloud platform is difficult ...
Dar et al.
doaj   +1 more source

Improving Fog Computing Performance via Fog-2-Fog Collaboration [PDF]

, 2019
In the Internet of Things (IoT) era, a large volume of data is continuously emitted from a plethora of connected devices. The current network paradigm, which relies on centralized data centers (aka Cloudcomputing), has become inefficient to respond to IoT latency concern. To address this concern, fog computing allows data processing and storage \close"
Mohammed Al-Khafajiy, Thar Baker, Hilal Al-Libawy, Zakaria Maamar, Moayad Aloqaily, Yaser Jararweh   +5 more
openaire   +1 more source

Biobased Bionic Spider Silk via Electrostatic Complexation for Simultaneously Harvesting Atmospheric Water and Triboelectric Energy

Advanced Functional Materials, EarlyView.
Bio‐based bionic spider silk and spider web are first prepared using polyelectrolyte complexation technique for the simultaneous harvesting of water and triboelectric energy. The advantage of this process lies on entirely bio‐based materials, fully green water‐processable procedures, extremely high production rate (99.36%), excellent fog harvesting ...
Qin Chen, Araz Rajabi‐Abhari, Tongtong Fu, Haonan Zhang, Siqi Huan, Liang Chen, Jinchao Li, Cheng Hao, Yaping Zhang, Ning Yan   +9 more
wiley   +1 more source

A Comparative Study of Various Machine Learning Algorithms in Fog Computing

, 2021
Urooj Yousuf Khan, Muhammad Alam, U Khan, T Soomro, X Fei, N Shah, N Verba, K.-M Chao, V Sanchez-Anguix, J Lewandowski, A James, Z Usman, U Soomro, P Kumar, U Gandhi, F Haouari, . Faraj, J Alja'am, D Kimovski, H Ijaz, N Saurabh, R Prodan, X An, X Zhou, X L, F Lin, L Yang, A Alli, M Alam, M Lavassani, S Forsstrm, U Jennehag, T Zhang, M Yang, T Zhu, . Liu, . Xiang, W Zhou, D Wu, S Liu, L Zhang, J Terpenny, R Gao, T Kurfess, J Guzzo, Z Tang, . Zhou, F Zhang, W Jia, W Zhao, M Moh, . Raju, I Alrashdi, A Alqazzaz, E Aloufi, R Alharthi, M Zohdy, H Ming, Z Shen, . Yokota, J Jin, . Tagami, T Higashino, D Rahbari, . Nickray, S Memon, M Maheswaran, B Tang, Z Chen, G Hefferman, P Shuyi, T Wei, H He, Q Yang, Y Tu, Y Ruan, S Wagle, C Brinton, C Joe-Wong, K Abdulkareem, . Mohammed, S Gunasekaran, A Mutlag, . Salama, N Ali, J Guevara, R Torres, N Fonseca, A Dhillon, A Singh, H Vohra, C Ellis, B Varghese, S Gil, E Balevi, . Gitlin, A Melloukimag, S Batim, S Zeadally, A Abeshu   +99 more
openalex   +1 more source