Optimized Analytics Query Allocation at the Edge of the Network

  • Anna Karanika
  • Madalena Soula
  • Christos Anagnostopoulos
  • Kostas KolomvatsosEmail author
  • George Stamoulis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11874)


The new era of the Internet of Things (IoT) provides the space where novel applications will play a significant role in people’s daily lives through the adoption of multiple services that facilitate everyday activities. The huge volumes of data produced by numerous IoT devices make the adoption of analytics imperative to produce knowledge and support efficient decision making. In this setting, one can identify two main problems, i.e., the time required to send the data to Cloud and wait for getting the final response and the distributed nature of data collection. Edge Computing (EC) can offer the necessary basis for storing locally the collected data and provide the required analytics on top of them limiting the response time. In this paper, we envision multiple edge nodes where data are stored being the subject of analytics queries. We propose a methodology for allocating queries, defined by end users or applications, to the appropriate edge nodes in order to save time and resources in the provision of responses. By adopting our scheme, we are able to ask the execution of queries only from a sub-set of the available nodes avoiding to demand processing activities that will lead to an increased response time. Our model envisions the allocation to specific epochs and manages a batch of queries at a time. We present the formulation of our problem and the proposed solution while providing results of an extensive evaluation process that reveals the pros and cons of the proposed model.


Internet of Things Edge Computing Large scale data Queries management 



This research received funding from the European’s Union Horizon 2020 research and innovation programme under the grant agreement No. 745829 & the Greek Secretariat for Research Funding under the project ENFORCE.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anna Karanika
    • 1
  • Madalena Soula
    • 1
  • Christos Anagnostopoulos
    • 2
  • Kostas Kolomvatsos
    • 2
    Email author
  • George Stamoulis
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of ThessalyVolosGreece
  2. 2.School of Computing ScienceUniversity of GlasgowGlasgowUK

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