Situation-based query generation for performance evaluation of cloud managed IoT applications

Shalmoly Mondal, Prem Prakash Jayaraman, Alireza Hassani, Pari Delir Haghighi, Dimitrios Georgakopoulos

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

With increased deployment of IoT application on cloud platforms, assessing the performance of such application is an open problem. Currently, approaches are limited to legacy database-based applications and does not cater for the needs of IoT applications. This paper proposes, implements and validates a framework namely, IoTQGen, that can generate situation-based queries to conduct performance evaluation of IoT application hosted by cloud IoT middleware platform. The framework comprises: (i) a model to capture the query requirements of IoT applications; (ii) a data generator to generate IoT data based on specified configuration; and (iii) a set of queries designed to represent data analytic IoT applications. The framework supports different query types that can be typically used to represent and address IoT application scenarios. An important functionality of the framework is its ability to issue queries based on dynamic changes in the state of IoT entities (situations). The framework is evaluated based on two smart city use cases to highlight how the framework can be used to generate complex and dynamic queries tailored for IoT application scenarios.

Original languageEnglish
Title of host publicationProceedings - 2023 24th IEEE International Conference on Mobile Data Management, IEEE MDM 2023
EditorsFederico Montori, Zimu Zhou, Edison Tsz Nam Chan
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages352-357
Number of pages6
ISBN (Electronic)9798350341010
ISBN (Print)9798350341027
DOIs
Publication statusPublished - 2023
EventWorkshop on IoT-Crowdsensing for Smart Cities 2023 - Singapore, Singapore
Duration: 3 Jul 20236 Jul 2023
Conference number: 2nd
https://sites.google.com/view/iotsencity2023/home (Website)
https://ieeexplore.ieee.org/xpl/conhome/10214688/proceeding (Proceedings)

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
PublisherIEEE, Institute of Electrical and Electronics Engineers
Volume2023-July
ISSN (Print)1551-6245
ISSN (Electronic)2375-0324

Conference

ConferenceWorkshop on IoT-Crowdsensing for Smart Cities 2023
Abbreviated titleIoTSenCity 2023
Country/TerritorySingapore
CitySingapore
Period3/07/236/07/23
Internet address

Keywords

  • benchmarks
  • cloud platforms
  • IoT
  • IoT middlewares
  • IoT queries
  • performance evaluation
  • situations

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