Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

Li, Yunbo; Orgerie, Anne‐Cécile; Rodero, Iván; Parashar, Manish; Menaud, Jean-Marc

doi:10.1109/ccgrid.2017.92

Cited by 19 publications

(13 citation statements)

References 26 publications

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“…In this paper, different frameworks of data stream were proposed to improve the consistency of the data stream. Yunbo Li et al, 2017 [14] detailed explanation on the production of the on-site renewable energy in different edge clouds for improving solution in IOT systems. In this paper, a model was proposed for computing the objects to the edge which depends on the availability of the renewable energy.…”

Section: Literature Survey Daniel Puschmann Et Al 2017[4]mentioning

confidence: 99%

Improved Novel Clustering Technique for Diverse and Self-motivated Traffic Data Stream for IoT Scenario

Nadimpalli¹,

Shankar²,

K³

et al. 2019

IJITEE

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Technologies are changing day by day and IoT is worldwide data and may of great business important to various users. sTo create such reasonable data, majority adaptive and K-mediod clustering techniques are employed in data mining. In research work, it focus on comparing adaptive, K-medisod and novel clustering technique to internet-of-things data collection in ITSs (Intelligence Traffic System). In traffic DataStream is composed form online site, it challenges of 30,000 instances with 9 attributes, clusters formed after evaluation and number of clusters is identified after the evaluation. Proposed techniques are significant too easy than some other clustering techniques with respect to all computation recall and precision parameters. In traffic databases depends on the data separation and cluster enhancement that is quality of clusters. To resolve the major issues that over load the system or Centre’s in IoT which consequences the huge kind of data on internet. It evaluated a set of consequences experiments using token and manufacture data from traffic use case view where the traffic considerations from the city monitor. Comparison of clustering methods that helps in determining suitable clustering approach for the offer internet of things database which results in optimal performance metrics.

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Section: Literature Survey Daniel Puschmann Et Al 2017[4]mentioning

confidence: 99%

Improved Novel Clustering Technique for Diverse and Self-motivated Traffic Data Stream for IoT Scenario

Nadimpalli¹,

Shankar²,

K³

et al. 2019

IJITEE

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“…Multiple devices are sending continuous flows of data to a collection point that sends them to the Cloud. For instance, it represents a camera-based monitoring service like a road traffic analyzer [17]. Continuous data flows are sent from the camera to the Cloud hosting the IoT service.…”

Section: Application Characteristicsmentioning

confidence: 99%

“…Yet, the increasing number of devices produces a scale effect and causes also a non negligible impact on Cloud infrastructures that provide the computing power required by IoT devices to offer services [15]. To cope with the traffic increase caused by IoT devices, Cloud computing infrastructures start to explore the newly proposed distributed architectures, and in particular edge Cloud architectures where small data centers are located at the edge of the Cloud, typically in Internet Service Providers' (ISP) edge infrastructures [16,17].…”

Section: Introductionmentioning

confidence: 99%

End-to-end energy models for Edge Cloud-based IoT platforms: Application to data stream analysis in IoT

Orgerie

Rodero³

et al. 2018

Future Generation Computer Systems

Self Cite

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Internet of Things (IoT) is bringing an increasing number of connected devices that have a direct impact on the growth of data and energy-hungry services. These services are relying on Cloud infrastructures for storage and computing capabilities, transforming their architecture into more a distributed one based on edge facilities provided by Internet Service Providers (ISP). Yet, between the IoT device, communication network and Cloud infrastructure, it is unclear which part is the largest in terms of energy consumption. In this paper, we provide end-to-end energy models for Edge Cloud-based IoT platforms. These models are applied to a concrete scenario: data stream analysis produced by cameras embedded on vehicles. The validation combines measurements on real test-beds running the targeted application and simulations on well-known simulators for studying the scaling-up with an increasing number of IoT devices. Our results show that, for our scenario, the edge Cloud part embedding the computing resources consumes 3 times more than the IoT part comprising the IoT devices and the wireless access point.

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“…Considering the need for the development data analysis tools that are both simple to use and provide the most effective tools for a given data set, Ahrabian et al introduce data analysis tools as web service that enables the data consumer to make a simple HTTP request for processing data over the internet. Li et al leverage on‐site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved Quality of Service (QoS) compared to core cloud solutions. They propose an analytic model to decide whether to offload computation from the objects to the edge or to the core cloud, depending on the renewable energy availability and the desired application QoS.…”

Section: Related Workmentioning

confidence: 99%

“…Some researchers focus on specific IoT data analysis technology. [21][22][23][24] Onal et al 21 propose an extended IoT framework with semantics, big data, and machine learning and use the framework in a weather data analysis use case. They implement weather clustering and sensor anomaly detection using a publicly available data set.…”

Section: Iot Data Analysismentioning

confidence: 99%

FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

Xue

et al. 2018

Concurrency and Computation

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Summary As social networks such as micro‐blogging sites rapidly grow, deciding whom to follow (followee recommendation) becomes a significantly important problem. Most existing works exclusively rely on two traditional factors: the proximity between two users in the network topology or the similarity of the user‐generated contents in the social network, disregarding the effect of users' following behaviors. The challenge of how to effectively combine these two factors remains largely open. Moreover, most research studies simply sort the scores to find top‐k users, which is time‐consuming, especially for large‐scale networks. In this paper, we propose the idea that “predict users' following behaviors by following behaviors themselves.” We consider a user's following to others as a normal process of dynamic and coherent behavior, and we model the potential propagation of the users' following behaviors. Furthermore, based on our previous research on top‐k selection problem, we propose an effective top‐k followee recommendation algorithm, called FRFB. FRFB has low complexity and high scalability and, moreover, good adaptability to real‐life dynamic social networks. We conduct extensive experiments, with two real social network data sets (Wiki and Twitter), which show that FRFB outperforms the well‐known topology‐based followee recommendation algorithms.

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Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

Cited by 19 publications

References 26 publications

Improved Novel Clustering Technique for Diverse and Self-motivated Traffic Data Stream for IoT Scenario

Improved Novel Clustering Technique for Diverse and Self-motivated Traffic Data Stream for IoT Scenario

End-to-end energy models for Edge Cloud-based IoT platforms: Application to data stream analysis in IoT

FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

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