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Location-centric storage and query in wireless sensor networks

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Abstract

Location-centric storage (LCS) is envisioned as a promising scheme for robust and user-friendly on-demand data storage in networking environments such as the roadway sensor networks [Xing K et al. J Parallel Distributed Comput 67:336–345, 2007; Xing K et al. in: IEEE wireless communication and networking conference (WCNC), 2005]. In this paper, we analyze the performance of LCS in terms of storage and query overheads. This study indicates that LCS utilizes network resource efficiently and achieves good scalability. In particular, the storage overhead of sensors is independent of the network size, and is evenly distributed across the network. We also propose two algorithms for data retrieval in LCS-enabled one-dimensional and two-dimensional sensor networks. Our algorithms guarantee that acquiring the stored data of any event only takes a small number of communication hops to query a small number of sensors.

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Notes

  1. We are not going to discuss how to determine the intensity value of an event as it is beyond the scope of this paper. The impacts of intensity values under different application scenarios will be investigated in our future research.

  2. An event is usually detected by multiple sensors simultaneously but one sensor will be designated for reporting the event [2]. We term this sensor the “home sensor” of the event.

  3. Here “close enough” means that this sensor is the closest among its neighboring sensors to one of the ideal locations where the record should be stored.

  4. Although we have only tested on two types of networks, based on the properties aforementioned, we believe that the simulation results can be extended to more general topologies where nodes are deployed at random with arbitrary distributions.

  5. When we talk about the “overhead of query and update”, we actually refer to the communication overhead induced by user query and information update. This is reasonable since in a resource-constrained sensor network, communication is the most aggressive energy consumer, which strongly impacts the network lifetime.

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Authors and Affiliations

  1. Computer Science, The George Washington University, Washington, DC, 20052, USA

    Kai Xing & Xiuzhen Cheng

  2. Systems & Computer Science, Howard University, Washington, DC, 20059, USA

    Jiang Li

  3. Electrical & Computer Engineering, Old Dominion University, Norfolk, VA, 23529, USA

    Min Song

Authors
  1. Kai Xing
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  2. Xiuzhen Cheng
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  3. Jiang Li
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  4. Min Song
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Corresponding author

Correspondence to Kai Xing.

Additional information

The research of Dr. Xiuzhen Cheng is supported by NSF CAREER Award CNS-0347674. The research of Dr. Min Song is supported by NSF CAREER Award CNS-0644247.

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Xing, K., Cheng, X., Li, J. et al. Location-centric storage and query in wireless sensor networks. Wireless Netw 16, 955–967 (2010). https://doi.org/10.1007/s11276-009-0181-2

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