Exploring Control-Message Quenching in SDN-based Management of 6LoWPANs

Ndiaye, Musa; Abu‐Mahfouz, Adnan M.; Hancke, Gerhard P.; Silva, Bruno

doi:10.1109/indin41052.2019.8972067

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Additionally, in edge computing, a typical orchestrator is used to manage resource allocation. Although several works in the literature have used this technology to address a variety of problems, orchestration still faces issues with QoS estimation and matchmaking [72]. Consequently, the authors in [73] introduced an intelligent-based architecture for IoTbased applications that combines orchestration (used between the cloud and the edge) and AI techniques (which provides for the intelligence capability of an architecture).…”

Section: Key Enabling Technologies For Edge Computingmentioning

confidence: 99%

Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

Molokomme

Onumanyi

Abu‐Mahfouz

2022

JSAN

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The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs.

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Section: Key Enabling Technologies For Edge Computingmentioning

confidence: 99%

Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

Molokomme

Onumanyi

Abu‐Mahfouz

2022

JSAN

Self Cite

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“…In [21], Ndiaye et al proposed a solution called FR-CMQ running on the IT-SDN architecture. This solution reduces the number of 'flow rule request' messages to the controller.…”

Section: Reducing the Production Of Control Messagesmentioning

confidence: 99%

Energy management techniques in Software-Define Wireless Sensor Network

Boulou¹,

Yélémou²,

Go³

et al. 2022

Proceedings of the 4th Edition of the Computer Science Research Days, JRI 2021, 11-13 November 2021, Bobo-Dioulasso, Burkina Fa

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Software Defined Wireless Sensor Network (SDWSN) is composed of sensors nodes in which routing processes are entrusted to a controller in a centralized mode. In an SDWSN architecture, the control plane is decoupled from the data plane. Sensor nodes are easily reconfigurable and are even more flexible than in traditional WSNs. Resource management becomes easy. However, this centralized management of routing processes requires a large amount of control messages. This induces additionnal energy consumption. In this paper, first, we analyse different techniques used to face this energy consumption problem and we highlight limits of the used methods. Then, we provide some perspectives.

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“…Results showed that the required security protocols increased in latency and traffic overhead by about three (3) times. However, solutions to similar latency and overhead traffic problems have been provided with the use of distributed controllers [63]- [65]. Keeping in with this distributed approach, microgrid attack resiliency has been achieved by using distributed adaptive observers [66].…”

Section: ) Security Managementmentioning

confidence: 99%

Software-Defined Power Grids: A Survey on Opportunities and Taxonomy for Microgrids

et al. 2021

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Future generation power grids will require the introduction and deployment of distributed energy resources to meet modern day load requirements. Consequently, we expect to see a rise in microgrids (MGs) existing as part of the main grid (grid-connected) or independent (islanded). Contained in these microgrids are a combination of energy resources such as solar, wind and fossil-fuels coupled with storage devices, electric vehicles and smart devices supporting prosumer operation. However, the addition of renewable energy resources would mean fluctuations in energy supply which would cause power system instability if not managed effectively. Hence, to maximize the management flexibility of MGs, the concept of microgrid software-definition is introduced. A concept that can be looked at as giving the microgrid an operating system to improve operation response and event detection by maintaining a global view of the network. This paper therefore critically analyses what this entails by presenting an architecture for Software-Defined Microgrids and discussing the management opportunities that softwarization of the MG introduces. We also highlight the design requirements and associated challenges in implementing and deploying SDMGs.INDEX TERMS Energy balancing, grid resiliency, microgrids, power grid management, software-defined networking, smart grids.

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Exploring Control-Message Quenching in SDN-based Management of 6LoWPANs

Cited by 9 publications

References 15 publications

Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

Energy management techniques in Software-Define Wireless Sensor Network

Software-Defined Power Grids: A Survey on Opportunities and Taxonomy for Microgrids

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