Adaptive distributed SDN controllers: Application to Content-Centric Delivery Networks

Bannour, Fetia; Souihi, Sami; Mellouk, Abdelhamid

doi:10.1016/j.future.2020.05.032

Cited by 19 publications

(7 citation statements)

References 21 publications

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“…Table 2 shows the key parameters that were studied, such as RSSI (highest −80 dBm, lowest −100 dBm), transmission power (−20 dBm), modulation level in bits 256QAM, transmission rate (6 Mbps), deviation in RSSI (7 dB), Channel Bandwidth (10 MHz), maximum transmission power (25 mW), total time (1560 s), average distance between CPS nodes (2 m), and reliability (95%). Finally, we provide simulation and experimental results [ 28 , 31 , 32 ] to validate the effectiveness of proposed FISRA with possible future recommendations. To validate the packet loss probability (around 10 −5 –10 −7 in 6G) of an IoT device, the system needs to send 100–109 packets.…”

Section: Methodsmentioning

confidence: 99%

“…Authors in [ 20 , 21 , 22 , 23 , 24 ] examined and optimized the impact of the energy of TPC and the lifetime of WSNs and WPT systems, respectively, while they do not concentrate on the power management schemes in BSNs. The authors of [ 30 , 31 , 32 ] developed the resource allocation, TPC-based techniques for the cognitive radio, and wireless power transfer networks, but their work does not focus on the power management for smart healthcare. Authors in [ 33 , 34 , 35 ] developed the MAC layer based QoS aware energy efficient technique but do not consider the fair power allocation and management methods.…”

Section: Existing Workmentioning

confidence: 99%

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AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications

Sodhro

Zahid

2021

Sensors

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Artificial Intelligence (AI) is the revolutionary paradigm to empower sixth generation (6G) edge computing based e-healthcare for everyone. Thus, this research aims to promote an AI-based cost-effective and efficient healthcare application. The cyber physical system (CPS) is a key player in the internet world where humans and their personal devices such as cell phones, laptops, wearables, etc., facilitate the healthcare environment. The data extracting, examining and monitoring strategies from sensors and actuators in the entire medical landscape are facilitated by cloud-enabled technologies for absorbing and accepting the entire emerging wave of revolution. The efficient and accurate examination of voluminous data from the sensor devices poses restrictions in terms of bandwidth, delay and energy. Due to the heterogeneous nature of the Internet of Medical Things (IoMT), the driven healthcare system must be smart, interoperable, convergent, and reliable to provide pervasive and cost-effective healthcare platforms. Unfortunately, because of higher power consumption and lesser packet delivery rate, achieving interoperable, convergent, and reliable transmission is challenging in connected healthcare. In such a scenario, this paper has fourfold major contributions. The first contribution is the development of a single chip wearable electrocardiogram (ECG) with the support of an analog front end (AFE) chip model (i.e., ADS1292R) for gathering the ECG data to examine the health status of elderly or chronic patients with the IoT-based cyber physical system (CPS). The second proposes a fuzzy-based sustainable, interoperable, and reliable algorithm (FSIRA), which is an intelligent and self-adaptive decision-making approach to prioritize emergency and critical patients in association with the selected parameters for improving healthcare quality at reasonable costs. The third is the proposal of a specific cloud-based architecture for mobile and connected healthcare. The fourth is the identification of the right balance between reliability, packet loss ratio, convergence, latency, interoperability, and throughput to support an adaptive IoMT driven connected healthcare. It is examined and observed that our proposed approaches outperform the conventional techniques by providing high reliability, high convergence, interoperability, and a better foundation to analyze and interpret the accuracy in systems from a medical health aspect. As for the IoMT, an enabled healthcare cloud is the key ingredient on which to focus, as it also faces the big hurdle of less bandwidth, more delay and energy drain. Thus, we propose the mathematical trade-offs between bandwidth, interoperability, reliability, delay, and energy dissipation for IoMT-oriented smart healthcare over a 6G platform.

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Section: Methodsmentioning

confidence: 99%

Section: Existing Workmentioning

confidence: 99%

AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications

Sodhro

Zahid

2021

Sensors

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“…Communication networks weave the fabric of the current connected world. SDN is a novel paradigm of network management aiming at separating data and control planes to manage network components flexibly [28]. The controller in SDN acts as the brain of the whole network performing most of the computations.…”

Section: Software-defined Networkingmentioning

confidence: 99%

“…Currently, Internet architecture faces challenges to scale due to the content-centric requirements of the underlying applications [28]. A vital aspect of the future Internet is that it supports a content-driven paradigm rather than a connection-driven.…”

mentioning

confidence: 99%

Complementing IoT Services Using Software-Defined Information Centric Networks: A Comprehensive Survey

Rafique

Hafid

Cherkaoui

2022

IEEE Internet Things J.

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IoT connects a large number of physical objects with the Internet that capture and exchange real-time information for service provisioning. Traditional network management schemes face challenges to manage vast amounts of network traffic generated by IoT services. Software-Defined Networking (SDN) and Information-Centric Networking (ICN) are two complementary technologies that could be integrated to solve the challenges of different aspects of IoT service provisioning. ICN offers a clean-slate design to accommodate continuously increasing network traffic by considering content as a network primitive. It provides a novel solution for information propagation and delivery for large-scale IoT services. On the other hand, SDN allocates overall network management responsibilities to the central controller, where network elements act merely as traffic forwarding components. An SDN-enabled network flexibly supports ICN without deploying ICN-capable hardware. Therefore, the integration of SDN and ICN provides benefits for large-scale IoT services. This paper provides a comprehensive survey on Software-Defined Information-Centric Internet of Things (SDIC-IoT) for IoT services provisioning. We present critical enabling technologies of SDIC-IoT, discuss its architecture, and elaborate its benefits for IoT services provisioning. We elaborate on key IoT service provisioning requirements and discuss how SDIC-IoT supports different aspects of IoT services. We develop different taxonomies of SDIC-IoT literature based on various performance parameters. Furthermore, an extensive discussion on different use cases, synergies, and advances is presented to envision the SDIC-IoT concept. Finally, we provide current challenges, causes, and future research directions of IoT services provisioning using SDIC-IoT.

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“…Despite the existence of many solutions for energy-efficiency on SDN, only a few of those consider the control-plane consumption. The justification for this is that, in the early days of SDN, it was common to have one controller tied to many switches so the data-plane energy consumption was much higher than the control-plane, thus deserving more attention; however, now, as many works have demonstrated [17][18][19], the recommendation is to have many controllers load-balancing the control of switches. Furthermore, many solutions add tasks to the controller without accounting for the impact on energy consumption of the now much more busy components.…”

Section: Sdn On Data Center Network (Dcn)mentioning

confidence: 99%

Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers

2021

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Software-defined networks have become more common in data centers. The programmability of these networks is a great feature that allows innovation to be deployed fast, following the increasing number of new applications. This growth comes with a cost of more processing power and energy consumption. Many researchers have tackled this issue using existing routing techniques to dynamically adjust the network forwarding plane to save energy. On the control-plane, researchers have found algorithms for positioning the controller in a way to reduce the number of used links, thus reducing energy. These strategies reduce energy consumption at the expense of processing power of the controllers. This paper proposes a novel approach to energy efficiency focused on the network’s control-plane, which is complementary to the many already existing data-plane solutions. It takes advantage of the parallel processing capabilities of modern off-the-shelf multicore processors to split the many tasks of the controller among the cores. By dividing the tasks among homogeneous cores, one can lower the frequency of operations, lowering the overall energy consumption while keeping the same quality of service level. We show that a multicore controller can use an off-the-shelf multicore processor to save energy while keeping the level of service. We performed experiments based on standard network measures, namely latency and throughput, and standard energy efficiency metrics for data centers such as the Communication Network Energy Efficiency (CNEE) metric. Higher energy efficiency is achieved by a parallel implementation of the controller and lowering each core’s frequency of operation. In our experiments, we achieved a drop of 28% on processor energy use for a constant throughput scenario when comparing with the single-core approach.

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Adaptive distributed SDN controllers: Application to Content-Centric Delivery Networks

Cited by 19 publications

References 21 publications

AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications

AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications

Complementing IoT Services Using Software-Defined Information Centric Networks: A Comprehensive Survey

Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers

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