Big Data Analytics for Security in Fog Computing

2017 ◽  
pp. 245-262
Author(s):  
Shanhe Yi ◽  
Qun Li
2019 ◽  
pp. 259-290 ◽  
Author(s):  
Farhad Mehdipour ◽  
Bahman Javadi ◽  
Aniket Mahanti ◽  
Guillermo Ramirez-Prado

Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.


2020 ◽  
Vol 114 (4) ◽  
pp. 3395-3418
Author(s):  
Md. Muzakkir Hussain ◽  
M. M. Sufyan Beg ◽  
Mohammad Saad Alam

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Aras R. Dargazany ◽  
Paolo Stegagno ◽  
Kunal Mankodiya

This work introduces Wearable deep learning (WearableDL) that is a unifying conceptual architecture inspired by the human nervous system, offering the convergence of deep learning (DL), Internet-of-things (IoT), and wearable technologies (WT) as follows: (1) the brain, the core of the central nervous system, represents deep learning for cloud computing and big data processing. (2) The spinal cord (a part of CNS connected to the brain) represents Internet-of-things for fog computing and big data flow/transfer. (3) Peripheral sensory and motor nerves (components of the peripheral nervous system (PNS)) represent wearable technologies as edge devices for big data collection. In recent times, wearable IoT devices have enabled the streaming of big data from smart wearables (e.g., smartphones, smartwatches, smart clothings, and personalized gadgets) to the cloud servers. Now, the ultimate challenges are (1) how to analyze the collected wearable big data without any background information and also without any labels representing the underlying activity; and (2) how to recognize the spatial/temporal patterns in this unstructured big data for helping end-users in decision making process, e.g., medical diagnosis, rehabilitation efficiency, and/or sports performance. Deep learning (DL) has recently gained popularity due to its ability to (1) scale to the big data size (scalability); (2) learn the feature engineering by itself (no manual feature extraction or hand-crafted features) in an end-to-end fashion; and (3) offer accuracy or precision in learning raw unlabeled/labeled (unsupervised/supervised) data. In order to understand the current state-of-the-art, we systematically reviewed over 100 similar and recently published scientific works on the development of DL approaches for wearable and person-centered technologies. The review supports and strengthens the proposed bioinspired architecture of WearableDL. This article eventually develops an outlook and provides insightful suggestions for WearableDL and its application in the field of big data analytics.


Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.


2022 ◽  
pp. 287-300
Author(s):  
Chandrima Roy ◽  
Nivedita Das ◽  
Siddharth Swarup Rautaray ◽  
Manjusha Pandey

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