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

Abstract : The emergence of Internet of Things (IoT) is participating to the increase of data- and energy-hungry applications. As connected devices do not yet offer enough capabilities for sustaining these applications, users perform computation offloading to the cloud. To avoid network bottlenecks and reduce the costs associated to data movement, edge cloud solutions have started being deployed, thus improving the Quality of Service. In this paper, we advocate for leveraging on-site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved QoS compared to core cloud solutions. We 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. This model is validated on our application use-case that deals with video stream analysis from vehicle cameras.
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Yunbo Li, Anne-Cécile Orgerie, Ivan Rodero, Manish Parashar, Jean-Marc Menaud. Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT. CCGrid 2017 - IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing , May 2017, Madrid, Spain. pp.186-195. ⟨hal-01472358⟩

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