Energy Efficient
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Anju Gupta ◽  
R K Bathla

With so many people now wearing mobile devices with sensors (such as smartphones), utilizing the immense capabilities of these business mobility goods has become a prospective skill to significant behavioural and ecological sensors. A potential challenge for pervasive context assessment is opportunistic sensing, has been effectively used to a wide range of applications. The sensor cloud combines cloud technology with a wireless sensor, resulting in a scalable and cost-effective computing platform for real-time applications. Because the sensor's battery power is limited and the data centre’s servers consume a significant amount of energy to supply storage, a sensor cloud must be energy efficient. This study provides a Fog-based semantic for enabling these kinds of technologies quickly and successfully. The suggested structure is comprised of fundamental algorithms to help set up and coordinate the fog sensing jobs. It creates effective multihop routes for coordinating relevant devices and transporting acquired sensory data to fog sinks. It was claimed that energy-efficient sensor cloud approaches were categorized into different groups and that each technology was examined using numerous characteristics. The outcomes of a series of thorough test simulation in NS3 to define the practicality of the created console, as well as the proportion of each parameter utilized for each technology, are computed.

2022 ◽  
Vol 70 (3) ◽  
pp. 5991-6005
Zilong Jin ◽  
Chengbo Zhang ◽  
Kan Yao ◽  
Dun Cao ◽  
Seokhoon Kim ◽  

2022 ◽  
Vol 70 (3) ◽  
pp. 5929-5948
Gulzar Mehmood ◽  
Muhammad Zahid Khan ◽  
Muhammad Fayaz ◽  
Mohammad Faisal ◽  
Haseeb Ur Rahman ◽  

2021 ◽  
Vol 248 ◽  
pp. 114796
Yaxian Zhao ◽  
Yingjie Zhao ◽  
Qun Yi ◽  
Ting Li ◽  
Jiancheng Wang ◽  

A. V. Mayakkannan ◽  
Selvakumar Rajendran ◽  
Srihari Kannan ◽  
Arvind Chakrapani ◽  
V. K. Shanmuganathan

2021 ◽  
Vol 53 (11) ◽  
Amit Kumar Garg ◽  
Sanjeev Kumar Metya ◽  
Ghanshyam Singh ◽  
Vijay Janyani ◽  
Moustafa H. Aly ◽  

Yu Cheng ◽  
Jiateng Yin ◽  
Lixing Yang

2021 ◽  
Shuo Shi ◽  
Meng Wang ◽  
Shushi Gu ◽  
Zhong Zheng

Rita Carvalho Veloso ◽  
Andrea Souza ◽  
Joana Maia ◽  
Nuno Manuel Monteiro Ramos ◽  
João Ventura

2021 ◽  
Claudio Battiloro ◽  
Paolo Di Lorenzo ◽  
Mattia Merluzzi ◽  
Sergio Barbarossa

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient adaptive federated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs stochastic gradient-based algorithms to perform continuous learning and adaptation. Hinging on Lyapunov stochastic optimization tools, we dynamically optimize radio parameters (e.g., set of transmitting devices, transmit powers, bits, and rates) and computation resources (e.g., CPU cycles at devices and at server) in order to strike the best trade-off between power, latency, and performance of the federated learning task. The framework admits both a model-based implementation, where the learning performance metrics are available in closed-form, and a data-driven approach, which works with online estimates of the learning performance of interest. The method is then customized to the case of federated least mean squares (LMS) estimation, and federated training of deep convolutional neural networks. Numerical results illustrate the effectiveness of our strategy to perform energy-efficient, low-latency, adaptive federated learning at the wireless network edge.

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