Accelerated collection of sensor data by mobility-enabled topology ranks

Authors: Angelopoulos, C.M. and Nikoletseas, S.E.

http://dx.doi.org/10.1016/j.jss.2010.07.035

Journal: Journal of Systems and Software

Volume: 83

Pages: 2471-2477

DOI: 10.1016/j.jss.2010.07.035

This data was imported from Scopus:

Authors: Angelopoulos, C.M. and Nikoletseas, S.

Journal: Journal of Systems and Software

Volume: 83

Issue: 12

Pages: 2471-2477

ISSN: 0164-1212

DOI: 10.1016/j.jss.2010.07.035

We study the problem of fast and energy-efficient data collection of sensory data using a mobile sink, in wireless sensor networks in which both the sensors and the sink move. Motivated by relevant applications, we focus on dynamic sensory mobility and heterogeneous sensor placement. Our approach basically suggests to exploit the sensor motion to adaptively propagate information based on local conditions (such as high placement concentrations), so that the sink gradually "learns" the network and accordingly optimizes its motion. Compared to relevant solutions in the state of the art (such as the blind random walk, biased walks, and even optimized deterministic sink mobility), our method significantly reduces latency (the improvement ranges from 40% for uniform placements, to 800% for heterogeneous ones), while also improving the success rate and keeping the energy dissipation at very satisfactory levels. © 2010 Elsevier Inc.

This data was imported from Web of Science (Lite):

Authors: Angelopoulos, C.M. and Nikoletseas, S.

Journal: JOURNAL OF SYSTEMS AND SOFTWARE

Volume: 83

Issue: 12

Pages: 2471-2477

eISSN: 1873-1228

ISSN: 0164-1212

DOI: 10.1016/j.jss.2010.07.035

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