Fog Networking: An Enabler for Next Generation Internet of Things

Qaisar, Saad; Riaz, Nida

doi:10.1007/978-3-319-42108-7_27

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…A discussion of techniques related to fog networks is presented in [5], discussing various network applications, ref. [22] with a special view on IoT, or [23], describing possible implementations in next-generation networks. Edge networks are discussed in [24] or [25], related to mobile edge networks.…”

Section: Related Workmentioning

confidence: 99%

Challenges of Network Forensic Investigation in Fog and Edge Computing

Spiekermann,

Keller

2023

Future Internet

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While network forensics has matured over the decades and even made progress in the last 10 years when deployed in virtual networks, network forensics in fog and edge computing is still not progressed to that level despite the now widespread use of these paradigms. By using an approach similar to software testing, i.e., a mixture of systematic and experience, we analyze obstacles specific to forensics in fog and edge computing such as spatial dispersion and possibly incomplete recordings, and derive how far these obstacles can be overcome by adapting processes and techniques from other branches of network forensics, and how new solutions could look otherwise. In addition, we present a discussion of open problems of network forensics in fog and edge environments and discusses the challenges for an investigator.

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Section: Related Workmentioning

confidence: 99%

Challenges of Network Forensic Investigation in Fog and Edge Computing

Spiekermann,

Keller

2023

Future Internet

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“…Futuristic and time critical applications essentially demand the analysis of data be performed nearby its point of generation rather sending the data to cloud every time for analysis and decision making [55,56,57,58]. Consequently, this calls for shifting data management functionalities closer to data generating devices.…”

Section: ) Fog Computing Layermentioning

confidence: 99%

Addressing the Future Data Management Challenges in IoT: A Proposed Framework

Abbasi¹,

Memon²,

Syed³

et al. 2017

ijacsa

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Abstract-Internet of Thing (IoT) has been attracting the interest of researchers in recent years. Traditionally, only handful types of devices had the capability to be connected to internet/intranet, but due to the latest developments in RFID, NFC, smart sensors and communication protocols billions of heterogeneous devices are being connected each year. From smart phones uploading the data regarding location and fitness to smart grids uploading the data regarding energy consumption and distribution, these devices are generating a huge amount of data each passing moment. This research paper proposes a data management framework to securely manage the huge amount of data that is being generated by IoT enabled devices. The proposed framework is divided into nine layers. The framework incorporates layers such as data collection layer, fog computing layer, integrity management layer, security layer, data aggregation layer, data analysis layer, data storage layer, application layer and archiving layer. The security layer has been proposed as a background layer because all layers shall ensure the privacy and security of the data. These layers will help in managing the data from the point where it is generated by an IoT enabled device until the point where the data is archived at the data center.

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“…Thirdly, comprehensive knowledge about the resources and tasks is possessed by SDN. Furthermore, SDN in vehicular networks is responsible for enhancing the routing process and optimizing network resources (Qaisar and Riaz, 2016). The completion of the function within the stipulated time is facilitated by moving task requests from the IoV sensors to the neighboring Edge device.…”

Section: Introductionmentioning

confidence: 99%

Route Optimization using Hybrid GRU Learning Model for SDN and Edge-Based VANET Topology

Kumar¹,

Chinnasamy²

2022

Journal of Computer Science

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Intelligent Transportation System (ITS) offers outstanding features, including security applications and emergency alerts. Unfortunately, ITS limits traffic control services, adaptability, and adjustability due to the traffic volume. Hence, expanding the standard Vehicular Ad hoc Networks (VANET) framework is a requirement. As a result, in the latest days, the concept of Software-Defined Networking based Vehicular Ad hoc Networks (SDN-VANETs) has drawn considerable attention, creating VANETs smarter. The SDN-VANETs design is capable of addressing the aforementioned VANET issues. The integrated (analytically) SDN architecture is customizable and it also contains domain knowledge about the VANET architecture. Packet forwarding is a fundamental challenge in VANET wherein a router, as in the form of an RSU, determines the next hop of every signal in the pipeline to provide it to its recipient as fast as possible. Reinforcement Learning (RL) is used to develop autonomous routing protocol rules; however, the limitation of RL's depth prevents it from representing more comprehensively dynamic network conditions, restricting its true value. In this research, we present a VANET infrastructure based on SDN + EDGE with a new Deep Reinforcement Learning (DRL) route optimization framework, "Gated Recurrent Reinforcement Learning (GRRL)" neural network, whereby each router has its hybrid GRU + Feed Forward Neural Network (NN) for learning and making decisions in an entirely distributed environment. The GRRL collects routing characteristics from valuable information about huge backlog packets and previous operations, substantially approximating the weighting scheme of Q-learning. We also enable every route to connect with its immediate neighbors regularly such that a more comprehensive view of network topology may be integrated. Trial findings demonstrated that the multi-agent GRRL strategy could achieve a delicate balance between congestion awareness and the fastest routes, considerably reducing packet transmission time in general network topologies compared to its competitors.

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Fog Networking: An Enabler for Next Generation Internet of Things

Cited by 8 publications

References 7 publications

Challenges of Network Forensic Investigation in Fog and Edge Computing

Challenges of Network Forensic Investigation in Fog and Edge Computing

Addressing the Future Data Management Challenges in IoT: A Proposed Framework

Route Optimization using Hybrid GRU Learning Model for SDN and Edge-Based VANET Topology

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