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Centralized Energy Prediction in Wireless Sensor Networks Leveraged by Software-Defined Networking

Author

Listed:
  • Gustavo A. Nunez Segura

    (Laboratório de Arquitetura e Redes de Computadores, Escola Politécnica, Universidade de São Paulo, São Paulo 05508-010, Brazil)

  • Cintia Borges Margi

    (Laboratório de Arquitetura e Redes de Computadores, Escola Politécnica, Universidade de São Paulo, São Paulo 05508-010, Brazil)

Abstract

Resource Constraints in Wireless Sensor Networks are a key factor in protocols and application design. Furthermore, energy consumption plays an important role in protocols decisions, such as routing metrics. In Software-Defined Networking (SDN)-based networks, the controller is in charge of all control and routing decisions. Using energy as a metric requires such information from the nodes, which would increase packets traffic, impacting the network performance. Previous works have used energy prediction techniques to reduce the number of packets exchanged in traditional distributed routing protocols. We applied this technique in Software-Defined Wireless Sensor Networks (SDWSN). For this, we implemented an energy prediction algorithm for SDWSN using Markov chain. We evaluated its performance executing the prediction on every node and on the SDN controller. Then, we compared their results with the case without prediction. Our results showed that by running the Markov chain on the controller we obtain better prediction and network performance than when running the predictions on every node. Furthermore, we reduced the energy consumption for topologies up to 49 nodes for the case without prediction.

Suggested Citation

  • Gustavo A. Nunez Segura & Cintia Borges Margi, 2021. "Centralized Energy Prediction in Wireless Sensor Networks Leveraged by Software-Defined Networking," Energies, MDPI, vol. 14(17), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5379-:d:625064
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    Cited by:

    1. Chia-Hung Wang & Qigen Zhao & Rong Tian, 2023. "Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network," Energies, MDPI, vol. 16(11), pages 1-24, May.
    2. Paweł Dymora & Mirosław Mazurek & Krzysztof Smalara, 2021. "Modeling and Fault Tolerance Analysis of ZigBee Protocol in IoT Networks," Energies, MDPI, vol. 14(24), pages 1-21, December.

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