IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i11p3024-d370394.html
   My bibliography  Save this article

An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation

Author

Listed:
  • Carolina Del-Valle-Soto

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

  • Ramiro Velázquez

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

  • Leonardo J. Valdivia

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

  • Nicola Ivan Giannoccaro

    (Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy)

  • Paolo Visconti

    (Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy)

Abstract

The continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought about many challenges that need to be addressed, such as excess energy consumption. Accordingly, this paper presents and analyzes wireless network energy models using five different communication protocols: Ad Hoc On-Demand Distance Vector (AODV), Multi-Parent Hierarchical (MPH), Dynamic Source Routing (DSR), Low Energy Adaptive Clustering Hierarchy (LEACH) and Zigbee Tree Routing (ZTR). First, a series of metrics are defined to establish a comparison and determine which protocol exhibits the best energy consumption performance. Then, simulations are performed and the results are compared with real scenarios. The energy analysis is conducted with three proposed sleeping algorithms: Modified Sleeping Crown (MSC), Timer Sleeping Algorithm (TSA), and Local Energy Information (LEI). Thereafter, the proposed algorithms are compared by virtue of two widely used wireless technologies, namely Zigbee and WiFi. Indeed, the results suggest that Zigbee has a better energy performance than WiFi, but less redundancy in the topology links, and this study favors the analysis with the simulation of protocols with different nature. The tested scenario is implemented into a university campus to show a real network running.

Suggested Citation

  • Carolina Del-Valle-Soto & Ramiro Velázquez & Leonardo J. Valdivia & Nicola Ivan Giannoccaro & Paolo Visconti, 2020. "An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation," Energies, MDPI, vol. 13(11), pages 1-31, June.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:3024-:d:370394
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/11/3024/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/11/3024/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roberto de Fazio & Donato Cafagna & Giorgio Marcuccio & Paolo Visconti, 2020. "Limitations and Characterization of Energy Storage Devices for Harvesting Applications," Energies, MDPI, vol. 13(4), pages 1-18, February.
    2. Roberto de Fazio & Donato Cafagna & Giorgio Marcuccio & Alessandro Minerba & Paolo Visconti, 2020. "A Multi-Source Harvesting System Applied to Sensor-Based Smart Garments for Monitoring Workers’ Bio-Physical Parameters in Harsh Environments," Energies, MDPI, vol. 13(9), pages 1-33, May.
    3. C. SriVenkateswaran & D. Sivakumar, 2019. "Secure cluster-based data aggregation in wireless sensor networks with aid of ECC," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 31(2), pages 153-169.
    4. Babayo, Aliyu Aliyu & Anisi, Mohammad Hossein & Ali, Ihsan, 2017. "A Review on energy management schemes in energy harvesting wireless sensor networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1176-1184.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Paolo Visconti & Nicola Ivan Giannoccaro & Roberto de Fazio, 2021. "Special Issue on Electronic Systems and Energy Harvesting Methods for Automation, Mechatronics and Automotive," Energies, MDPI, vol. 14(23), pages 1-5, December.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paolo Visconti & Laura Bagordo & Ramiro Velázquez & Donato Cafagna & Roberto De Fazio, 2022. "Available Technologies and Commercial Devices to Harvest Energy by Human Trampling in Smart Flooring Systems: A Review," Energies, MDPI, vol. 15(2), pages 1-38, January.
    2. Roberto De Fazio & Roberta Proto & Carolina Del-Valle-Soto & Ramiro Velázquez & Paolo Visconti, 2022. "New Wearable Technologies and Devices to Efficiently Scavenge Energy from the Human Body: State of the Art and Future Trends," Energies, MDPI, vol. 15(18), pages 1-37, September.
    3. Sijing Zhu & Zheng Fan & Baoquan Feng & Runze Shi & Zexin Jiang & Ying Peng & Jie Gao & Lei Miao & Kunihito Koumoto, 2022. "Review on Wearable Thermoelectric Generators: From Devices to Applications," Energies, MDPI, vol. 15(9), pages 1-27, May.
    4. Yao, Yao & Shen, Zhicheng & Wang, Qiliang & Du, Jiyun & Lu, Lin & Yang, Hongxing, 2023. "Development of an inline bidirectional micro crossflow turbine for hydropower harvesting from water supply pipelines," Applied Energy, Elsevier, vol. 329(C).
    5. Tan, Ting & Yan, Zhimiao & Zou, Hongxiang & Ma, Kejing & Liu, Fengrui & Zhao, Linchuan & Peng, Zhike & Zhang, Wenming, 2019. "Renewable energy harvesting and absorbing via multi-scale metamaterial systems for Internet of things," Applied Energy, Elsevier, vol. 254(C).
    6. Mahdi Zareei & Cesar Vargas-Rosales & Mohammad Hossein Anisi & Leila Musavian & Rafaela Villalpando-Hernandez & Shidrokh Goudarzi & Ehab Mahmoud Mohamed, 2019. "Enhancing the Performance of Energy Harvesting Sensor Networks for Environmental Monitoring Applications," Energies, MDPI, vol. 12(14), pages 1-14, July.
    7. H Ufuk Gökçe & K Umut Gökçe, 2021. "Intelligent energy optimization system development and validation for German building types [The governance of the European Energy Union: efficiency, effectiveness and acceptance of the Winter Pack," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 16(4), pages 1299-1316.
    8. Wang, Feng & Sun, Xiuting & Xu, Jian, 2018. "A novel energy harvesting device for ultralow frequency excitation," Energy, Elsevier, vol. 151(C), pages 250-260.
    9. Wang, Lu & Fei, Zhenxuan & Duan, Congsheng & Han, Xiangguang & Li, Min & Gao, Wendi & Xia, Yong & Jia, Chen & Lin, Qijing & Zhao, Yihe & Li, Zhikang & Zhao, Libo & Jiang, Zhuangde & Maeda, Ryutaro, 2024. "Self-sustained and self-wakeup wireless vibration sensors by electromagnetic-piezoelectric-triboelectric hybrid energy harvesting," Applied Energy, Elsevier, vol. 355(C).
    10. Moudgil, Vipul & Hewage, Kasun & Hussain, Syed Asad & Sadiq, Rehan, 2023. "Integration of IoT in building energy infrastructure: A critical review on challenges and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    11. Paolo Visconti & Francesco Iaia & Roberto De Fazio & Nicola Ivan Giannoccaro, 2021. "A Stake-Out Prototype System Based on GNSS-RTK Technology for Implementing Accurate Vehicle Reliability and Performance Tests," Energies, MDPI, vol. 14(16), pages 1-22, August.
    12. Irene Cappelli & Stefano Parrino & Alessandro Pozzebon & Alessio Salta, 2021. "Providing Energy Self-Sufficiency to LoRaWAN Nodes by Means of Thermoelectric Generators (TEGs)-Based Energy Harvesting," Energies, MDPI, vol. 14(21), pages 1-17, November.
    13. Ahmed Redha Mahlous, 2017. "SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach," Future Internet, MDPI, vol. 9(4), pages 1-18, December.
    14. Yujia Ge & Yurong Nan & Xianhai Guo, 2021. "Maximizing network throughput by cooperative reinforcement learning in clustered solar-powered wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 17(4), pages 15501477211, April.
    15. Roberto de Fazio & Donato Cafagna & Giorgio Marcuccio & Alessandro Minerba & Paolo Visconti, 2020. "A Multi-Source Harvesting System Applied to Sensor-Based Smart Garments for Monitoring Workers’ Bio-Physical Parameters in Harsh Environments," Energies, MDPI, vol. 13(9), pages 1-33, May.
    16. Ruiying Li & Wenting Ma & Ning Huang & Rui Kang, 2017. "Deployment-based lifetime optimization for linear wireless sensor networks considering both retransmission and discrete power control," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-19, November.
    17. Kansha, Yasuki & Ishizuka, Masanori, 2019. "Design of energy harvesting wireless sensors using magnetic phase transition," Energy, Elsevier, vol. 180(C), pages 1001-1007.
    18. Roberto De Fazio & Vincenzo Mariano Mastronardi & Matteo Petruzzi & Massimo De Vittorio & Paolo Visconti, 2022. "Human–Machine Interaction through Advanced Haptic Sensors: A Piezoelectric Sensory Glove with Edge Machine Learning for Gesture and Object Recognition," Future Internet, MDPI, vol. 15(1), pages 1-42, December.
    19. 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.
    20. Paolo Visconti & Nicola Ivan Giannoccaro & Roberto de Fazio, 2021. "Special Issue on Electronic Systems and Energy Harvesting Methods for Automation, Mechatronics and Automotive," Energies, MDPI, vol. 14(23), pages 1-5, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:3024-:d:370394. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.