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SWM-PnR: Ontology-Based Context-Driven Knowledge Representation for IoT-Enabled Waste Management

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Internet of Things, Smart Spaces, and Next Generation Networks and Systems (ruSMART 2017, NsCC 2017, NEW2AN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10531))

Abstract

Using knowledge-based and semantic technologies in IoT is a very active research and promising area. This paper proposes a method of ontology-based context-driven knowledge representation for IoT-enabled hard waste management as part of a wider international project that aims at building IoT ecosystems for smart cities. The paper presents the development of the waste management ontology, rules, and proposes a multistage data processing method that allows extracting knowledge about specific nontrivial situations on its basis. The paper describes implementation of the proposed system as a web application, where the content types are based on ontology, and data processing occurs according to the proposed algorithm. Benefits of the proposed knowledge-based system are discussed and demonstrated. The proposed approach will significantly improve monitoring and management of waste collection, route planning, and problem reporting.

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Acknowledgements

Part of this work has been carried out in the scope of the project bIoTope which is co-funded by the European Commission under Horizon-2020 program, contract number H2020-ICT-2015/688203 – bIoTope. The research has been carried out with the financial support of the Ministry of Education and Science of the Russian Federation under grant agreement RFMEFI58716X0031.

Author information

Authors and Affiliations

  1. Department of Infocommunication Technologies, ITMO University, Kronverkskiy pr., 49, St. Petersburg, Russia

    Inna Sosunova, Arkady Zaslavsky, Theodoros Anagnostopoulos, Petr Fedchenkov & Oleg Sadov

  2. Digital Data61, CSIRO, Box 312, Clayton South, VIC, 3169, Australia

    Arkady Zaslavsky

  3. Research and Education, Ordnance Survey, SO16 0AS, Southampton, UK

    Theodoros Anagnostopoulos

  4. Caulfield School of Information Technology, Monash University, Melbourne, Australia

    Alexey Medvedev

Authors
  1. Inna Sosunova
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  2. Arkady Zaslavsky
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  3. Theodoros Anagnostopoulos
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  4. Petr Fedchenkov
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  5. Oleg Sadov
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  6. Alexey Medvedev
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Corresponding author

Correspondence to Inna Sosunova .

Editor information

Editors and Affiliations

  1. Electronics and Communication Engineering, Tampere University of Technology, Tampere, Finland

    Olga Galinina

  2. Electronics and Communication Engineering, Tampere University of Technology, Tampere, Finland

    Sergey Andreev

  3. FRUCT Oy, Helsinki, Finland

    Sergey Balandin

  4. Tampere University of Technology, Tampere, Finland

    Yevgeni Koucheryavy

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Sosunova, I., Zaslavsky, A., Anagnostopoulos, T., Fedchenkov, P., Sadov, O., Medvedev, A. (2017). SWM-PnR: Ontology-Based Context-Driven Knowledge Representation for IoT-Enabled Waste Management. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. ruSMART NsCC NEW2AN 2017 2017 2017. Lecture Notes in Computer Science(), vol 10531. Springer, Cham. https://doi.org/10.1007/978-3-319-67380-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-67380-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67379-0

  • Online ISBN: 978-3-319-67380-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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