IoT Workload Distribution Impact Between Edge and Cloud Computing in a Smart Grid Application

Carvalho, Otávio; García, Marta E.; Roloff, Eduardo; Carreno, Emmanuell D.; Navaux, Philippe O. A.

doi:10.1007/978-3-319-73353-1_14

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…Various researchers [3,14,16,21,70] have proposed features of edge computing that fit to the requirements of smart grid applications implementation. The following are the features: Support for real-time, that is, reducing latency and responsive time [38,[47][48][49][50][51], Support for Scalability [68], Decentralization [6, 16-18, 51, 52, 68], Consistency [19], Heterogeneity [20][21][22] , Intelligent coordination [23][24][25][26][27] , Optimal design / Resource utilization [28][29][30][31][32][33][34][35][36], Privacy and Security [16,17,37,[39][40][41][42], Data Mining and Storage [43][44][45], Virtualization [46], Energy Efficiency [51,71].…”

Section: Research Question Onementioning

confidence: 99%

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A Critical Review of Edge and Fog Computing for Smart Grid Applications

Gilbert

Naiman

Kimaro

et al. 2019

IFIP Advances in Information and Communication Technology

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Smart grid and cloud computing architectures have been perfectly suiting each other naturally. As a result, over the years cloud computing architectures have dominated the implementations of smart grid applications to address computing needs. However, due to continuing additions of heterogeneous (sensing and actuating) devices, emergence of Internet of Things (IoT), and massive amount of data collected across the grids for analytics, have contributed to the complexity of smart grids, making cloud computing architectures no longer suitable to provide smart grid services effectively. Edge and Fog computing approaches have relieved the cloud computing architectures of problems related to network congestion, latency and locality by shift of control, intelligence and trust to the edge of the network. In this paper, a systematic literature review is used to explore the research trend of the actual implementations of edge and fog computing for smart grid applications. A total of 70 papers were reviewed from the popular digital repositories. The study has revealed that, there is significant increase in the number of smart grid applications that have exploited the use edge and fog computing approaches. The study also shows that, considerable number of the smart grid applications are related to energy optimizations and intelligent coordination of smart grid resources. There are also challenges and issues that hinder smooth adoption of edge and fog computing for smart grid applications, which include security, interoperability and programming models.

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Section: Research Question Onementioning

confidence: 99%

“…Data aggregation [35,42,65]: In smart grid, data aggregation is an important process in power analytics for prediction power consumption, network planning and power pricing. The major challenge in data aggregation is privacy and security of measurements.…”

Section: Research Question Threementioning

confidence: 99%

A Critical Review of Edge and Fog Computing for Smart Grid Applications

Gilbert

Naiman

Kimaro

et al. 2019

IFIP Advances in Information and Communication Technology

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“…The authors present an adaptive workload management mechanism and algorithms to manage resources and effectively mask churn. Finally, in [14], an analysis of the impact of workload distribution in a smart grid application is discussed. The aim is to reveal if we can increase processing rates by leveraging each time more powerful edge node processors.…”

Section: Related Workmentioning

confidence: 99%

A probabilistic model for assigning queries at the edge

Kolomvatsos

Anagnostopoulos

2019

Computing

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Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query/task and a probabilistic decision making model. The proposed scheme matches the characteristics of the incoming queries and edge nodes trying to conclude the optimal allocation. We discuss our mechanism and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results.

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“…The offloading must take into consideration of the dynamic nature of network access requirements, number of edge devices, and available computational resources at the edge devices. We must take into consideration the granularity and hierarchy of edge network topology and how to dynamically partition the application for offloading [12].…”

Section: Distributed Computingmentioning

confidence: 99%

Ensemble of Probabilistic Learning Networks for IoT Edge Intrusion Detection

Jan¹,

Sajeev²

2018

IJCNC

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This paper proposes an intelligent and compact machine learning model for IoT intrusion detection using an ensemble of semi-parametric models with Ada boost. The proposed model provides an adequate realtime intrusion detection at an affordable computational complexity suitable for the IoT edge networks. The proposed model is evaluated against other comparable models using the benchmark data on IoT-IDS and shows comparable performance with reduced computations as required.

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IoT Workload Distribution Impact Between Edge and Cloud Computing in a Smart Grid Application

Cited by 7 publications

References 24 publications

A Critical Review of Edge and Fog Computing for Smart Grid Applications

A Critical Review of Edge and Fog Computing for Smart Grid Applications

A probabilistic model for assigning queries at the edge

Ensemble of Probabilistic Learning Networks for IoT Edge Intrusion Detection

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