Localization with Graph Diffusion Property
AbstractNode localization is an essential issue in wireless sensor networks (WSNs). Many range-free localization methods have been proposed to satisfy the requirement of low-system cost. However, some range-free methods only depend on network connectivity, and others only utilize the proximity information attached in neighborhood ordering. To employ the strength of the above two aspects, this paper introduces a new metric system called Combined and Weighted Diffusion Distance (CWDD). CWDD is designed to obtain the relative distance among nodes based on both graph diffusion property and neighbor information. We implement our design by embedding CWDD into two well-known localization algorithms and evaluate it by extensive simulations. Results show that our design improves the localization performance in large scale and non-uniform sensor networks, which reduces positioning errors by as much as 26%. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Chen, P.; Yin, Y.; Gao, S.; Niu, Q.; Gu, J. Localization with Graph Diffusion Property. Sensors 2017, 17, 1636.
Chen P, Yin Y, Gao S, Niu Q, Gu J. Localization with Graph Diffusion Property. Sensors. 2017; 17(7):1636.Chicago/Turabian Style
Chen, Pengpeng; Yin, Yuqing; Gao, Shouwan; Niu, Qiang; Gu, Jun. 2017. "Localization with Graph Diffusion Property." Sensors 17, no. 7: 1636.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.