Machine Learning Routing Protocol in Mobile IoT based on Software-Defined Networking

Samadi, Raheleh; Seitz, Joehen

doi:10.1109/nfv-sdn56302.2022.9974791

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…The combination of ML algorithms and network softwarization enables intelligent routing for cluster-based IoT networks. These algorithms process the data centralized by the SDN controllers to form and manage clusters [9], and perform optimized routing, mainly based on Q-learning RL technique, which adjusts routing decisions to meet energy consumption constraints and QoS requirements [29] [61]. It also enables intelligent VNF management with consideration of system cost (mainly using DRL), which enables self-driven network service orchestration for complex large-scale IoT NFV-enabled networks [1] [4].…”

Section: Intelligent Iot Network Softwarizationmentioning

confidence: 99%

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Review and analysis of recent advances in intelligent network softwarization for the Internet of Things

Zormati,

Lakhlef,

Ouni

2024

Computer Networks

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Section: Intelligent Iot Network Softwarizationmentioning

confidence: 99%

“…In [61], the authors study an ML routing protocol in mobile SDN-based IoT. The authors note that RPL, one of the prominent routing protocols in IoT, assumes that…”

mentioning

confidence: 99%

Review and analysis of recent advances in intelligent network softwarization for the Internet of Things

Zormati,

Lakhlef,

Ouni

2024

Computer Networks

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“…To enhance the flexibility and practicality of the proposed architecture, we weight objective values by 𝛽 t , 𝛽 e and 𝛽 c , respectively, calculating the processing time according to Equations ( 7), ( 9) and (11) as follows:…”

Section: Processing Timementioning

confidence: 99%

“…The execution time of tasks coupled with others in smart factory was reduced by the collaboration mode of cloud computing, MEC and ISDs [10]. Software‐defined networking (SDN), which includes the data layer, control layer, and application layer, can comprehensively monitor the entire network [11]. Located in the control layer, SDN controller is able to regulate the virtualised resources of the network [12].…”

Section: Introductionmentioning

confidence: 99%

Multi‐layer task scheduling and resource allocation schemes considering idle resource and task priority in IoT networks

Wang,

Guo,

Sun

et al. 2023

IET Communications

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With more and more interconnected smart devices (ISDs) accessing the Internet of Things (IoT), massive and diverse tasks need to be transformed and computed. Mobile edge computing enables the offloading of tasks to nearby servers to enhance processing efficiency, which makes ISDs idle, causing resource waste and failing to satisfy the high real‐time requirements of tasks. Besides, when tasks with different priorities are processed in the order they are generated, it will be difficult for IoT to guarantee a timely response to high‐priority tasks. To address the aforementioned issues, we establish an edge‐terminal‐local architecture by software‐defined networking to centrally manage idle ISD resource (2ISDR). Then the proposed two‐step scheduling mechanism with preemptive priority queue ensures the real‐time responses to high‐priority tasks, and the minimum resource allocation coefficients make offloading effective. Finally, we also propose a modified NSGA‐III algorithm named MNSGA‐III, which is designed to make decisions about offloading and solve resource allocation for tasks, and we correct infeasible solutions by a two‐step correction function to ensure the feasibility of MNSGA‐III. Experimental results show that the method can ensure a timely response to high‐priority tasks and optimize processing time, energy consumption, and economic cost through the utilization of 2ISDR.

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“…Although many research efforts have been made to investigate these three technologies and their combination, existing solutions generally consider SDN and omit NFV [7] [8], even though they are mutually reinforcing. We also note the lack of smart softwarization solutions for IoT networks [1] [3].…”

Section: Introductionmentioning

confidence: 99%

Enabling Architecture for Distributed Intelligent Network Softwarization for the Internet of Things

Zormati,

Lakhlef

2023

2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)

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The Internet of Things (IoT) is becoming a part of everyday life through its various sensing devices that collect valuable information. The huge number of interconnected heterogeneous IoT devices poses immense challenges, and network softwarization techniques are an adequate solution to these concerns. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two key softwarization techniques that enable the realization of efficient, agile IoT networks, especially when combined with Machine Learning (ML), mainly Federated Learning (FL). Unfortunately, existing solutions do not take advantage of such a combination to strengthen IoT networks in terms of efficiency and scalability. In this paper, we propose a novel architecture to achieve distributed intelligent network softwarization for IoT, in which SDN, NFV, and ML combine forces to enhance IoT constrained networks.

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Machine Learning Routing Protocol in Mobile IoT based on Software-Defined Networking

Cited by 11 publications

References 23 publications

Review and analysis of recent advances in intelligent network softwarization for the Internet of Things

Review and analysis of recent advances in intelligent network softwarization for the Internet of Things

Multi‐layer task scheduling and resource allocation schemes considering idle resource and task priority in IoT networks

Enabling Architecture for Distributed Intelligent Network Softwarization for the Internet of Things

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