Edge-Centric Efficient Regression Analytics

Harth, Natascha; Anagnostopoulos, Christos

doi:10.1109/edge.2018.00020

Cited by 30 publications

(35 citation statements)

References 19 publications

(46 reference statements)

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“…The authors in [20,21] proposed a privacy scheme called BalancePIC which aims at preserving a balance between privacy, data integrity and computational cost in edge-assisted IoT devices. In addition to this, researchers in [2] introduced a methodology for parametric regression analysis at the network edge where they defined algorithms for building regression models at the edge and selectively forwarding the parameters and other statistics to the gateway and/or cloud to support decision making during query evaluation. Such methods provide solutions to the problems associated with the traditional IoT architectures that directly forwards the contextual data to the cloud.…”

Section: Related Workmentioning

confidence: 99%

“…It detailed all the components involved and their functions in the system emphasizing the need for edge analysis in the mix. However, model evaluation and updating schemes were not included in the proposed system as suggested in [2]. This is a very important aspect of an IoT system as multidimensional conceptual data suffers from data bursty and statistical transiency.…”

Section: Related Workmentioning

confidence: 99%

“…This research focuses on regression analytic problems [2] by learning the relationship between the inputs ( ) and their corresponding output ( ) in a ( + 1) dimensional data space ( , ) ∈ ( ) using ANN. The ANN model can take in any amount of inputs = ([ , , … ])-which can refer to any physical quantity (light, relative humidity etc.)…”

Section: Model Creation and Deploymentmentioning

confidence: 99%

“…Conventionally, in IoT systems, the sensors gather data that is used for creating decision-making models for the IoT solution applications. The application then uses these decision-making models to resolve queries emanating from the application domain [2,25]. However, deploying the model alone is not enough for the provision of the IoT solution.…”

Section: Model Evaluation and Updatementioning

confidence: 99%

“…The collected data is then analyzed to derive useful information that can improve their operational efficiency. However, it is clear that the traditional approach to this process is not effective except with the introduction of the edge computing techniques which aim at displacing as many of the analytics as possible towards the edge of the network [1,2]. This study is therefore focused on applying edge computing techniques to process IoT data on smartphones.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Smartphone Architecture for Edge-Centric IoT Analytics

Marah

Jing

et al. 2020

Sensors

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The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloud/core for analysis and data storage. This research, therefore, focuses on formulating an edge-centric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in real-time. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Model Creation and Deploymentmentioning

confidence: 99%

Section: Model Evaluation and Updatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Smartphone Architecture for Edge-Centric IoT Analytics

Marah

Jing

et al. 2020

Sensors

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Internet of Things and data analytics: A current review

Mohindru

Mondal

Banka

2019

WIREs Data Min & Knowl

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With the advent of Internet and computing, we entered into an era with more people exchanging information over the Internet using devices like desktops, laptops, tablets, mobile phones, and similar data transmitting and receiving gadgets. This was a host centric communication approach. Internet of Things (IoT) is the next stage of technological advancement in computation, networking and communication with physical objects around the world getting connected to the network and exchanging data. This is an information centric approach. Thus it can be defined as an expanding physical network of dynamically increasing physical objects. The objects share information derived from their environments, reliably, and securely over the communication medium leveraging multiple protocols. The protocols involved are getting standardized to address the compatibility and interoperability issues. Each object connected is uniquely identified and controlled in the network. IoT finds its applications in many fields as environment monitoring, logistics, health care, automobile, controlled industrial environment, smart cities, and many more. As the devices, their cardinality and alignment, the data type, the data rate and specifics of the domain of the IoT applications vary; there is also a need to define architectures that incorporate the devices, communication mediums, storage, and analysis capabilities and the consumers of the derived output. Advancements in data analytics, machine learning, and digital technologies offer possibilities of derive meaningful intelligence for actionable output and in creating useful and context aware applications. This article is categorized under: Algorithmic Development > Web Mining Application Areas > Internet Application Areas > Data Mining Software Tools Application Areas > Industry Specific Applications

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A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge

Kolomvatsos

Papadopoulou

Anagnostopoulos

et al. 2019

Lecture Notes in Computer Science

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Current applications developed for the Internet of Things (IoT) usually involve the processing of collected data for delivering analytics and support efficient decision making. The basis for any processing mechanism is data analysis, usually having as an outcome responses in various analytics queries defined by end users or applications. However, as already noted in the respective literature, data analysis cannot be efficient when missing values are present. The research community has already proposed various missing data imputation methods paying more attention of the statistical aspect of the problem. In this paper, we study the problem and propose a method that combines machine learning and a consensus scheme. We focus on the clustering of the IoT devices assuming they observe the same phenomenon and report the collected data to the edge infrastructure. Through a sliding window approach, we try to detect IoT nodes that report similar contextual values to edge nodes and base on them to deliver the replacement value for missing data. We provide the description of our model together with results retrieved by an extensive set of simulations on top of real data. Our aim is to reveal the potentials of the proposed scheme and place it in the respective literature.

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Edge-Centric Efficient Regression Analytics

Cited by 30 publications

References 19 publications

Smartphone Architecture for Edge-Centric IoT Analytics

Smartphone Architecture for Edge-Centric IoT Analytics

Internet of Things and data analytics: A current review

A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge

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