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Differential Data Processing for Energy Efficiency of Wireless Sensor Networks

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Advances in Computer Science and Ubiquitous Computing (CUTE 2018, CSA 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 536))

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Abstract

Wireless sensor networks use many types of wireless sensors to configure network. However batteries in wireless sensor nodes are energy limited and consume considerable energy for data transmission. Therefore, data merging is used as a means to increase energy efficiency in data transmission. In this paper, we propose Differential Data Processing (DDP), which reduces the size of data transmitted from the sensor node to increase the energy efficiency of the wireless sensor network. Experimental results show that processing the differential temperature data reduces the average data size of the sensor node by 30%.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Graduate School, Korea National Open University, Seoul, South Korea

    Kwang Kyu Lim, Byeong Rae Lee & Jin Gon Shon

  2. Division of General Studies, Kyonggi University, Suwon, South Korea

    JiSu Park

Authors
  1. Kwang Kyu Lim
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  2. JiSu Park
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  3. Byeong Rae Lee
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  4. Jin Gon Shon
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Corresponding author

Correspondence to Jin Gon Shon .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)

    James J. Park

  2. Department of Computer Software Engineering, Soon Chun Hyang University, Asan, Korea (Republic of)

    Doo-Soon Park

  3. Department of Multimedia Engineering, Dongguk University, Seoul, Korea (Republic of)

    Young-Sik Jeong

  4. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Yi Pan

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Lim, K.K., Park, J., Lee, B.R., Shon, J.G. (2020). Differential Data Processing for Energy Efficiency of Wireless Sensor Networks. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_25

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  • DOI: https://doi.org/10.1007/978-981-13-9341-9_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9340-2

  • Online ISBN: 978-981-13-9341-9

  • eBook Packages: EngineeringEngineering (R0)

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