SCIoT: A Secure and sCalable End-to-End Management Framework for IoT Devices

2018 ◽  
pp. 595-617 ◽  
Author(s):  
Moreno Ambrosin ◽  
Mauro Conti ◽  
Ahmad Ibrahim ◽  
Ahmad-Reza Sadeghi ◽  
Matthias Schunter
Keyword(s):  
Management Framework ◽  
End To End ◽  
Iot Devices

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 Enabling End-to-End Secure Connectivity for Low-Power IoT Devices with UAVs

2019 ◽  
Author(s):  
Archana Rajakaruna ◽  
Ahsan Manzoor ◽  
Pawani Porambage ◽  
Madhusanka Liyanage ◽  
Mika Ylianttila ◽  
...  
Keyword(s):  
Low Power ◽  
End To End ◽  
Iot Devices ◽  
Secure Connectivity

E2eUberIM: end-to-end service management framework for anything-as-a-service

2016 ◽  
Vol 54 (3) ◽  
pp. 54-60 ◽  
Author(s):  
Katarzyna Wac ◽  
Mark Cummings ◽  
Jayanta Dey
Keyword(s):  
Service Management ◽  
Management Framework ◽  
End To End

End-to-End Network Performance Management Framework Based on Case-Based Reasoning

2015 ◽  
Vol 94 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Buseung Cho ◽  
Seong-Jin Ahn ◽  
Kyong-Ho Choi ◽  
Byung Yun Lee ◽  
Jin-Wook Chung
Keyword(s):  
Performance Management ◽  
Network Performance ◽  
Case Based Reasoning ◽  
Management Framework ◽  
End To End ◽  
Case Based ◽  
Network Performance Management

End-to-end malware detection for android IoT devices using deep learning

2020 ◽  
Vol 101 ◽  
pp. 102098 ◽  
Author(s):  
Zhongru Ren ◽  
Haomin Wu ◽  
Qian Ning ◽  
Iftikhar Hussain ◽  
Bingcai Chen
Keyword(s):  
Deep Learning ◽  
Malware Detection ◽  
End To End ◽  
Iot Devices

Autonomic Trust Management in Cloud-Based and Highly Dynamic IOT Applications

2016 ◽  
Vol 1 (5) ◽  
pp. 26-32
Author(s):  
Suneth Namal ◽  
Hasindu Gamaarachchi ◽  
Gyu Myoung Lee ◽  
Tai-Won Um
Keyword(s):  
Cloud Computing ◽  
Feedback Control ◽  
Internet Of Things ◽  
Life Style ◽  
Trust Management ◽  
Control Loop ◽  
Management Framework ◽  
Iot Applications ◽  
Iot Devices ◽  
The Way

In this paper, we propose an autonomic trust management framework for cloud based and highly dynamic Internet of Things (IoT) applications and services. IoT is creating a world where physical objects are seamlessly integrated in order to provide advanced and intelligent services for humanbeings in their day-to-day life style. Therefore, trust on IoT devices plays an important role in IoT based services and applications. Cloud computing has been changing the way how provides are looking into these issues. Many studies have proposed different techniques to address trust management although non of them addresses autonomic trust management in cloud based highly dynamic IoT systems. To our understanding, IoT cloud ecosystems help to solve many of these issues while enhancing robustness and scalability. On this basis, we came up with an autonomic trust management framework based on MAPE-K feedback control loop to evaluate the level of trust. Finally, we presents the results that verify the effectiveness of this framework.

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