Machine learning for resource management in smart environments

2012 ◽  
Author(s):  
Christian Fabbricatore ◽  
Harold Boley ◽  
Achim P. Karduck
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Smart Environments

scholarly journals A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3953 ◽  
Author(s):  
Bruno Abade ◽  
David Perez Abreu ◽  
Marilia Curado
Keyword(s):  
Machine Learning ◽  
Environmental Data ◽  
Machine Learning Techniques ◽  
Smart Environments ◽  
Prototype System ◽  
Indoor Environments ◽  
Process Data ◽  
Indoor Space ◽  
Occupancy Detection ◽  
Real Scenario

Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user’s experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments.

The flexible resource management in optical data center networks based on machine learning and SDON

2020 ◽  
Vol 39 ◽  
pp. 100594
Author(s):  
Congying Zhi ◽  
Wei Ji ◽  
Rui Yin ◽  
Jinku Feng ◽  
Hongji Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Data Center ◽  
Optical Data ◽  
Data Center Networks ◽  
Flexible Resource

A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

2020 ◽  
pp. 42-52
Author(s):  
Shanthi Thangam Manukumar ◽  
Vijayalakshmi Muthuswamy
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Mobile Devices ◽  
Service Providers ◽  
Learning Algorithm ◽  
Fog Computing ◽  
Mobile Network ◽  
Resource Provisioning ◽  
Management Framework ◽  
Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

scholarly journals Machine Learning Adaptive Computational Capacity Prediction for Dynamic Resource Management in C-RAN

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 89130-89142
Author(s):  
Rolando Guerra-Gomez ◽  
Silvia Ruiz-Boque ◽  
Mario Garcia-Lozano ◽  
Joan Olmos Bonafe
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Computational Capacity ◽  
Dynamic Resource Management ◽  
Dynamic Resource ◽  
Capacity Prediction
Sign in / Sign up

Export Citation Format

Share Document