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Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols

Author

Listed:
  • Carolina Del-Valle-Soto

    (Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan, Jalisco 45010, Mexico)

  • Carlos Mex-Perera

    (ITAM, Rio Hondo 1, Ciudad de México 01080, Mexico)

  • Juan Arturo Nolazco-Flores

    (Tecnologico de Monterrey, Campus Puebla, Vía Atlixcáyotl No. 5718, Reserva Territorial Atlixcáyotl, Puebla 72453, Mexico)

  • Ramiro Velázquez

    (Facultad de Ingeniería, Universidad Panamericana, Josemaría Escrivá de Balaguer 101, Aguascalientes 20290, Mexico)

  • Alberto Rossa-Sierra

    (Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan, Jalisco 45010, Mexico)

Abstract

In this study, a Wireless Sensor Network (WSN) energy model is proposed by defining the energy consumption at each node. Such a model calculates the energy at each node by estimating the energy of the main functions developed at sensing and transmitting data when running the routing protocol. These functions are related to wireless communications and measured and compared to the most relevant impact on an energy standpoint and performance metrics. The energy model is validated using a Texas Instruments CC2530 system-on-chip (SoC), as a proof-of-concept. The proposed energy model is then used to calculate the energy consumption of a Multi-Parent Hierarchical (MPH) routing protocol and five widely known network sensors routing protocols: Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), ZigBee Tree Routing (ZTR), Low Energy Adaptive Clustering Hierarchy (LEACH), and Power Efficient Gathering in Sensor Information Systems (PEGASIS). Experimental test-bed simulations were performed on a random layout topology with two collector nodes. Each node was running under different wireless technologies: Zigbee, Bluetooth Low Energy, and LoRa by WiFi. The objective of this work is to analyze the performance of the proposed energy model in routing protocols of diverse nature: reactive, proactive, hybrid and energy-aware. Experimental results show that the MPH routing protocol consumes 16%, 13%, and 5% less energy when compared to AODV, DSR, and ZTR, respectively; and it presents only 2% and 3% of greater energy consumption with respect to the energy-aware PEGASIS and LEACH protocols, respectively. The proposed model achieves a 97% accuracy compared to the actual performance of a network. Tests are performed to analyze the consumption of the main tasks of a node in a network.

Suggested Citation

  • Carolina Del-Valle-Soto & Carlos Mex-Perera & Juan Arturo Nolazco-Flores & Ramiro Velázquez & Alberto Rossa-Sierra, 2020. "Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols," Energies, MDPI, vol. 13(3), pages 1-33, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:728-:d:317740
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    References listed on IDEAS

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    1. Carolina Del-Valle-Soto & Leonardo J. Valdivia & Ramiro Velázquez & Luis Rizo-Dominguez & Juan-Carlos López-Pimentel, 2019. "Smart Campus: An Experimental Performance Comparison of Collaborative and Cooperative Schemes for Wireless Sensor Network," Energies, MDPI, vol. 12(16), pages 1-23, August.
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    Cited by:

    1. Mohammad Reza Ghaderi & Vahid Tabataba Vakili & Mansour Sheikhan, 2021. "Compressive sensing-based energy consumption model for data gathering techniques in wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(1), pages 83-108, May.
    2. Ala’ Khalifeh & Mai Saadeh & Khalid A. Darabkh & Prabagarane Nagaradjane, 2021. "Radio Frequency Based Wireless Charging for Unsupervised Clustered WSN: System Implementation and Experimental Evaluation," Energies, MDPI, vol. 14(7), pages 1-21, March.
    3. Krzysztof Przystupa & Julia Pyrih & Mykola Beshley & Mykhailo Klymash & Andriy Branytskyy & Halyna Beshley & Daniel Pieniak & Konrad Gauda, 2021. "Improving the Efficiency of Information Flow Routing in Wireless Self-Organizing Networks Based on Natural Computing," Energies, MDPI, vol. 14(8), pages 1-24, April.
    4. Douglas de Farias Medeiros & Cleonilson Protasio de Souza & Fabricio Braga Soares de Carvalho & Waslon Terllizzie Araújo Lopes, 2022. "Energy-Saving Routing Protocols for Smart Cities," Energies, MDPI, vol. 15(19), pages 1-19, October.
    5. Piotr Arabas & Andrzej Sikora & Wojciech Szynkiewicz, 2021. "Energy-Aware Activity Control for Wireless Sensing Infrastructure Using Periodic Communication and Mixed-Integer Programming," Energies, MDPI, vol. 14(16), pages 1-17, August.
    6. 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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