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Energy efficient approximate self-adaptive data collection in wireless sensor networks

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Abstract

To extend the lifetime of wireless sensor networks, reducing and balancing energy consumptions are main concerns in data collection due to the power constrains of the sensor nodes. Unfortunately, the existing data collection schemesmainly focus on energy saving but overlook balancing the energy consumption of the sensor nodes. In addition, most of them assume that each sensor has a global knowledge about the network topology. However, in many real applications, such a global knowledge is not desired due to the dynamic features of the wireless sensor network. In this paper, we propose an approximate self-adaptive data collection technique (ASA), to approximately collect data in a distributed wireless sensor network. ASA investigates the spatial correlations between sensors to provide an energyefficient and balanced route to the sink, while each sensor does not know any global knowledge on the network.We also show that ASA is robust to failures. Our experimental results demonstrate that ASA can provide significant communication (and hence energy) savings and equal energy consumption of the sensor nodes.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Bin Wang, Xiaochun Yang, Guoren Wang & Ge Yu

  2. Department of Accounting, Computing and Finance, Texas A&M University at San Antonio, San Antonio, TX, 78363, USA

    Wanyu Zang

  3. Computer Science Department, The University of Texas at San Antonio, San Antonio, TX, 78249, USA

    Meng Yu

Authors
  1. Bin Wang
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  2. Xiaochun Yang
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  3. Guoren Wang
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  4. Ge Yu
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  5. Wanyu Zang
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  6. Meng Yu
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Corresponding author

Correspondence to Xiaochun Yang.

Additional information

Bin Wang received the PhD degree in computer science from Northeastern University, China in 2008. He is currently an associate professor in school of Computer Science and Enginering at Northeastern University. His research interests include design and analysis of algorithms, databases, data quality, and distributed systems. He is a member of the CCF.

Xiaochun Yang received the PhD degree in computer science from Northeastern University, China in 2001. She has been a professor at Northeastern University since 2008. Her research interests include data quality, data privacy, and distributed data management. She has received a China Program Award for New Century Excellent Talents in Universities. She is a member of the ACM, the IEEE Computer Society, and a senior member of the CCF.

Guoren Wang received the PhD degree from Northeastern University, China in 1996. He is currently a professor in School of Computer Science and Enginering at Northeastern University. His research interests include XML data management, query processing and optimization, bioinformatics, high-dimensional indexing, parallel database systems, and P2P data management. He is a senior member of the CCF.

Ge Yu received the BE and ME degrees in computer science from Northeastern University, China in 1982 and 1986, respectively, and the PhD degree in computer science from Kyushu University, Japan in 1996. He has been a professor at Northeastern University since 1996. His research interests include database theory and technology, distributed and parallel systems, embedded software, and network information security. He is a member of the IEEE, the ACM, and a fellow of the CCF.

Wanyu Zang joined Texas A&M University at San Antonio, USA as an assistant professor in 2015. She graduated with a PhD in computer science from the Nanjing University, China in 2001. Prior to joining Texas A&M University at San Antonio, She worked at Virginia Commonwealth University, USA and Western Illinois University, USA as an assistant professor and Pennsylvania State University as a postdoctoral researcher. Her research interests are in network security and cloud security, especially the security in multi-channel multi-interface network and virtual machine placement, which is supported by Army Research Office (ARO).

Meng Yu joined Department of Computer Science at University of Texas at San Antonio, USA in 2015 as an associate professor. He graduated with a PhD in computer science from the Nanjing University, China in 2001. Prior to joining Texas A&M University at San Antonio, he worked at Virginia Commonwealth University, USA as an associate professor, Western Illinois University, USA and Monmouth University, USA as an assistant professor, and Pennsylvania State University, USA as a postdoctoral researcher. His research interests include cloud computing security and system recovery. His research has been supported by multiple National Science Foundation (NSF) and Army Research Office (ARO) grants.

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Wang, B., Yang, X., Wang, G. et al. Energy efficient approximate self-adaptive data collection in wireless sensor networks. Front. Comput. Sci. 10, 936–950 (2016). https://doi.org/10.1007/s11704-016-4525-7

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  • DOI: https://doi.org/10.1007/s11704-016-4525-7

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