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

Smart Flow Steering Agent for End-to-End Delay Improvement in Software-Defined Networks

لضمان الإستجابة للخطأ والإدارة الموزعة، يتم استخدام البروتوكولات الموزعة كأحد المفاهيم المعمارية الرئيسية التي تتضمنها شبكة الإنترنت. ومع ذلك، يمكن التغلب على عدم الكفاءة وعدم الاستقرار والقصور بمساعدة بنية الشبكات الجديدة التي تسمى الشبكات المعرفة بالبرمجيات SDN. الخاصية الرئيسية لهذه المعمارية هي فصل مستوى التحكم عن مستوى البيانات. إن تقليل التصادم سيؤدي إلى تحسين سرعة الإستجابة وزيادة البيانات المرسلة بصورة صحيحة، لهذا السبب يجب أن يكون هناك توزيع متجانس للحمل المروري عبر مسارات الشبكة المختلفة. تقدم هذه الورقة البحثية أداة توجيه ذكية SFSA لتوجيه تدفق البيانات بناءاً على ظروف الشبكة الحالية. لتحسين الإنتاجية وتقليل زمن الوصول، فإن الخوارزمية المقترحة SFSA تقوم بتوزيع حركة مرور البيانات داخل الشبكة على مسارات مناسبة ، بالإضافة إلى الإشراف على الإرتباطات التشعبية وحمل مسارات نقل البيانات. تم استخدام سيناريو خوارزمية توجيه شجرة الامتداد الدنياMST وأخرى مع خوارزمية التوجيه المعروفة بفتح أقصر مسار أولاً OSPF لتقييم جودة الخوارمية المقترحة SFSA . على سبيل المقارنة ، بالنسبة لخوارزميات التوجيه المذكروة آنفاً ، فقد حققت استراتيجيةSFSA المقترحة انخفاضاً بنسبة 2٪ في معدل ضياع حزم البيانات PDR ، وبنسبة تتراوح بين 15-45٪ في سرعة إستلام البيانات من المصدر إلى الالوجهة النهائية لحزمة البيانات وكذلك انخفاض بنسبة 23 ٪ في زمن رحلة ذهاب وعودة RTT . تم استخدام محاكي Mininet ووحدة التحكم POX لإجراء المحاكاة. ميزة أخرى من SFSA على MST و OSPF هي أن وقت التنفيذ والاسترداد لا يحمل تقلبات. يتقوم أداة التوجيه الذكية المقترحة في هذه الورقة البحثية من فتح أفقاً جديداً لنشر أدوات ذكية جديدة في شبكة SDN تعزز قابلية برمجة الشبكات وإدارتها .To ensure fault tolerance and distributed management, distributed protocols are employed as one of the major architectural concepts underlying the Internet. However, inefficiency, instability and fragility could be potentially overcome with the help of the novel networking architecture called software-defined networking (SDN). The main property of this architecture is the separation of the control and data planes. To reduce congestion and thus improve latency and throughput, there must be homogeneous distribution of the traffic load over the different network paths. This paper presents a smart flow steering agent (SFSA) for data flow routing based on current network conditions. To enhance throughput and minimize latency, the SFSA distributes network traffic to suitable paths, in addition to supervising link and path loads. A scenario with a minimum spanning tree (MST) routing algorithm and another with open shortest path first (OSPF) routing algorithms were employed to assess the SFSA. By comparison, to these two routing algorithms, the suggested SFSA strategy determined a reduction of 2% in packets dropped ratio (PDR), a reduction of 15-45% in end-to-end delay according to the traffic produced, as well as a reduction of 23% in round trip time (RTT). The Mininet emulator and POX controller were employed to conduct the simulation. Another advantage of the SFSA over the MST and OSPF is that its implementation and recovery time do not exhibit fluctuations. The smart flow steering agent will open a new horizon for deploying new smart agents in SDN that enhance network programmability and management

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

End-to-End Delay Enhancement in 6LoWPAN Testbed Using Programmable Network Concepts

This paper introduces a proof-of-concept 6LoWPAN testbed to study the integration of programmable network technologies in relaxed throughput and low-power IoT devices. Open source software and hardware platforms are used in the implemented testbed to increase the possibility of future network extension. The proposed architecture offers end-to-end connectivity via the 6LoWPAN gateway to integrate IPv6 hosts and the low data rate devices directly. Nowadays, SoftwareDefined Networking (SDN) and Network Function Virtualization (NFV) are the most promising technologies for dealing with the massive increase in M2M devices and achieving agile traffic. The developed approach in this paper is entitled tailored Software Defined-Network Function Virtualization (SD-NFV), which is aimed at reducing the end-to-end delay and improving the energy depletion in sensor nodes. Experimental scenarios of the implemented testbed are conducted using a simple sensing application and the obtained results indicate that the introduced approach is appropriate for constrained IoT devices. By utilizing SD-NFV scheme in 6LoWPAN network, the data delivery ratio increased by 5-14%, the node operational time prolonged by 70%, the end-to-end latency for gathering sensor data minimized by ≈160%, and the latency for transmitting control messages to a specified node diminished by ≈63% when compared to a traditional (non SDN-enabled) 6LoWPAN network

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

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

Guidelines and mindlines: why do clinical staff over-diagnose malaria in Tanzania? A qualitative study

BACKGROUND: Malaria over-diagnosis in Africa is widespread and costly both financially and in terms of morbidity and mortality from missed diagnoses. An understanding of the reasons behind malaria over-diagnosis is urgently needed to inform strategies for better targeting of antimalarials. METHODS: In an ethnographic study of clinical practice in two hospitals in Tanzania, 2,082 patient consultations with 34 clinicians were observed over a period of three months at each hospital. All clinicians were also interviewed individually as well as being observed during routine working activities with colleagues. Interviews with five tutors and 10 clinical officer students at a nearby clinical officer training college were subsequently conducted. RESULTS: Four, primarily social, spheres of influence on malaria over-diagnosis were identified. Firstly, the influence of initial training within a context where the importance of malaria is strongly promoted. Secondly, the influence of peers, conforming to perceived expectations from colleagues. Thirdly, pressure to conform with perceived patient preferences. Lastly, quality of diagnostic support, involving resource management, motivation and supervision. Rather than following national guidelines for the diagnosis of febrile illness, clinician behaviour appeared to follow 'mindlines': shared rationales constructed from these different spheres of influence. Three mindlines were identified in this setting: malaria is easier to diagnose than alternative diseases; malaria is a more acceptable diagnosis; and missing malaria is indefensible. These mindlines were apparent during the training stages as well as throughout clinical careers. CONCLUSION: Clinicians were found to follow mindlines as well as or rather than guidelines, which incorporated multiple social influences operating in the immediate and the wider context of decision making. Interventions to move mindlines closer to guidelines need to take the variety of social influences into account

Optimised cloud-based 6LoWPAN network using SDN/NFV concepts for energy-aware IoT applications

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of Things (IoT) concept has been realised with the advent of Machineto-Machine (M2M) communication through which the vision of future Internet has been revolutionised. IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) provides feasible IPv6 connectivity to previously isolated environments, e.g. wireless M2M sensors and actuator networks. This thesis’s contributions include a novel mathematical model, energy-efficient algorithms, and a centralised software controller for dynamic consolidation of programmability features in cloud-based M2M networks. A new generalised joint mathematical model has been proposed for performance analysis of the 6LoWPAN MAC and PHY layers. The proposed model differs from existing analytical models as it precisely adopts the 6LoWPAN specifications introduced by the Internet Engineering Task Force (IETF) working group. The proposed approach is based on Markov chain modelling and validated through Monte-Carlo simulation. In addition, an intelligent mechanism has been proposed for optimal 6LoWPAN MAC layer parameters set selection. The proposed mechanism depends on Artificial Neural Network (ANN), Genetic Algorithm (GA), and Particles Swarm Optimisation (PSO). Simulation results show that utilising the optimal MAC parameters improve the 6LoWPAN network throughput by 52-63% and reduce end-to-end delay by 54-65%. This thesis focuses on energy-efficient data extraction and dissemination in a wireless M2M sensor network based on 6LoWPAN. A new scalable and self-organised clustering technique with a smart sleep scheduler has been proposed for prolonging M2M network’s lifetime and enhancing network connectivity. These solutions succeed in overcoming performance degradation and unbalanced energy consumption problems in homogeneous and heterogeneous sensor networks. Simulation results show that by adopting the proposed schemes in multiple mobile sink sensory field will improve the total aggregated packets by 38-167% and extend network lifetime by 30-78%. Proof-of-concept real-time hardware testbed experiments are used to verify the effectiveness of Software-Defined Networking (SDN), Network Function Virtualisation (NFV) and cloud computing on a 6LoWPAN network. The implemented testbed is based on open standards development boards (i.e. Arduino), with one sink, which is the M2M 6LoWPAN gateway, where the network coordinator and the customised SDN controller operated. Experimental results indicate that the proposed approach reduces network discovery time by 60% and extends the node lifetime by 65% in comparison with the traditional 6LoWPAN network. Finally, the thesis is concluded with an overall picture of the research conducted and some suggestions for future work.Iraqi Ministry of Higher Education and Scientific Researc

Optimized Artificial Intelligence Model for DDoS Detection in SDN Environment

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