4 research outputs found
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SD–NFV as an Energy Efficient Approach for M2M Networks Using Cloud–Based 6LoWPAN Testbed
Machine–to–Machine (M2M) communication is the leading technology for realising the Internet–of–Things (IoT). The M2M sensor nodes are characterised by low–power and low–data rates devices which have increased exponentially over the years. IPv6 over Low power Wireless Personal Area Network (6LoWPAN) is the first protocol that provides IPv6 connectivity to the wireless M2M sensor nodes. Having a tremendous number of M2M sensor nodes execute independent control decision leads to difficulty in network control and management. In addition, these ever–growing devices generate massive traffic and cause energy
scarcity which affects the M2M sensor node lifetime. Recently, Software–Defined Networking (SDN) and Network Functioning Virtualisation (NFV) are being used in M2M sensor networks to add programmability and flexibility features in order to adopt the exponential increment in wireless M2M traffic and enable network configuration even after deployment. This paper presents a proof–of–concept implementation which aims to analyse how SDN, NFV, and cloud computing can interact together in the 6LoWPAN gateway to provide simplicity and flexibility in network management. The proposed approach is called customised Software Defined–Network Functioning Virtualisation (SD–NFV), and has been tested and verified by implementing a
real–time 6LoWPAN testbed. The experimental results indicated that the SD–NFV approach reduced the network discovery time by 60% and extended the node’s lifetime by 65% in comparison to the traditional 6LoWPAN network. The implemented testbed has one sink which is the M2M 6LoWPAN gateway where the
network coordinator and the SDN controller are executed. There are many possible ways to implement 6LoWPAN testbed but limited are based on open standards development boards (e.g., Arduino, Raspberry Pi, and Beagle Bones). In the current testbed, the Arduino board is chosen and the SDN controller is customised
and written using C++ language to fit the 6LoWPAN network requirements. Finally, SDN and NFV have been envisioned as the most promising techniques to improve network programmability, simplicity, and management in cloud–based 6LoWPAN gateway
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Optimized Artificial Intelligence Model for DDoS Detection in SDN Environment
© Copyright 2023 The Authors. Distributed denial of service (DDoS) attacks continue to be a major security concern, threatening the availability and reliability of network services. Software-defined networking (SDN) has emerged as a promising solution to address this issue, enabling centralized network control and management. However, conventional SDN-based DDoS mitigation techniques often struggle to detect and mitigate sophisticated attacks due to their limited ability to analyze complex traffic patterns. This paper proposes an innovative and optimized approach that effectively combines mininet, Ryu controller, and one dimensional-convolutional neural network (1D-CNN) to detect and mitigate DDoS attacks in SDN environments. The proposed approach involves training the 1D-CNN model with labeled network traffic data to effectively identify abnormal patterns associated with DDoS attacks. Furthermore, seven hyperparameters of the trained 1D-CNN model were tuned using non-dominated sorting genetic algorithm II (NSGA-II) to achieve the best accuracy with minimum training time. Once the optimized 1D-CNN model detects an attack, the Ryu controller dynamically adapts the network policies and employs appropriate mitigation techniques to protect the network infrastructure. To evaluate the effectiveness of the optimized 1D-CNN model, extensive experiments were conducted using a simulated SDN environment with a realistic DDoS attack dataset. The experimental results demonstrate that the developed approach achieves significantly improved detection accuracy of 99.99% compared to other machine learning (ML) models. The NSGA-II enhances the optimized model accuracy with an improvement rate of 9.5%, 8%, 5.4%, and 2.6% when it is compared to logistic regression (LR), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) optimized models respectively. This research paves the way for future developments in leveraging deep learning (DL) driven techniques and SDN architectures to address evolving cybersecurity challenges
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A New Intelligent Approach for Optimising 6LoWPAN MAC Layer Parameters
Fairness, low latency, and high throughput with low energy consumption are desired attributes for Medium Access Control (MAC) protocols. The IEEE 802.15.4 standard defines the MAC and physical (PHY) layers standard for IPv6 over Low power Personal Area Network (6LoWPAN). When non-appropriate parameter setting is used, the default MAC parame-ters generate excessive collisions, packet losses, and high latency under high traffic when a large number of 6LoWPAN nodes being deployed. A search of the literature revealed few studies which investigate the impact of optimising these parameters to achieve high throughput with minimum latency. This paper proposes a new intelligent approach to select the optimal 6LoWPAN MAC layer parameters set, the introduced mechanism depends on Artificial Neural Networks (ANN), Genetic Algorithm (GA) or Particles Swarm Optimisation (PSO) to select and validate the optimised MAC parameters. The obtained simulation results showed that utilising the optimal MAC parameters improved 6LoWPAN network throughput by 52-63% and reduced the end-to-end delay by 54-65% in which the enhancement percentage depends on the number of deployed sensor nodes in the network
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Self-Powered 6LoWPAN Sensor Node for Green IoT Edge Devices
Copyright © 2020 The Authors. In this paper, a simulation model and practical testbed for green Internet of Things (IoT) edge devices are proposed based on solar harvester with constant voltage-maximum power point tracking (CV-MPPT) technique. Billions of connected edge devices represent the essential part of the IoT through the IP-enabled sensor networks based on IPv6 over Low power Wireless Personal Area Network (6LoWPAN). In traditional IoT edge devices, the stored energy in the non-rechargeable battery determines the node lifetime while it is being depleted with time. Therefore, purchasing billions of such batteries is costly and must be disposed of efficiently. This paper is aimed at simulating and implementing a new class of green IoT edge devices that can report data wirelessly and powered perpetually using clean energy. The developed edge device utilizes solar energy harvesting mechanism through photovoltaic (PV) module, this approach will avoid periodical battery replacement and hence, the energy supplied to the sensor mode is not limited anymore. The implemented testbed is based on open-source hardware and software platforms while the simulation environment is based on MATLAB/SIMULINK 2019a. The effects of temperature and solar irradiance on the performance of the developed approach are examined in order to confirm the leverage of the proposed methodology scheme. The lifetime of the developed green IoT device is predicted based on the device's activities, current consumption, and energy storage capacity. The obtained results showed that the battery lifetime is extended by 38-49% when the edge device runs on an independent power source