Efficient collection of sensor data via a new accelerated random walk

Authors: Angelopoulos, C.M., Nikoletseas, S.E., Patroumpa, D. and Raptopoulos, C.

http://eprints.bournemouth.ac.uk/24077/

http://dx.doi.org/10.1002/cpe.3108

Journal: Concurrency and Computation: Practice and Experience

Volume: 28

Pages: 1796-1811

DOI: 10.1002/cpe.3108

This data was imported from Scopus:

Authors: Angelopoulos, C.M., Nikoletseas, S., Patroumpa, D. and Raptopoulos, C.

http://eprints.bournemouth.ac.uk/24077/

Journal: Concurrency Computation

Volume: 28

Issue: 6

Pages: 1796-1811

eISSN: 1532-0634

ISSN: 1532-0626

DOI: 10.1002/cpe.3108

Copyright © 2013 John Wiley & Sons, Ltd. Motivated by the problem of efficiently collecting data from wireless sensor networks via a mobile sink, we present an accelerated random walk on random geometric graphs (RGG). Random walks in wireless sensor networks can serve as fully local, lightweight strategies for sink motion that significantly reduce energy dissipation but introduce higher latency in the data collection process. In most cases, random walks are studied on graphs like Gn,p and grid. Instead, we here choose the RGG model, which abstracts more accurately spatial proximity in a wireless sensor network. We first evaluate an adaptive walk (the random walk with inertia) on the RGG model; its performance proved to be poor and led us to define and experimentally evaluate a novel random walk that we call γ-stretched random walk. Its basic idea is to favour visiting distant neighbours of the current node towards reducing node overlap and accelerate the cover time. We also define a new performance metric called proximity cover time that, along with other metrics such as visit overlap statistics and proximity variation, we use to evaluate the performance properties and features of the various walks.

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

Authors: Angelopoulos, C.M., Nikoletseas, S., Patroumpa, D. and Raptopoulos, C.

http://eprints.bournemouth.ac.uk/24077/

Journal: CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

Volume: 28

Issue: 6

Pages: 1796-1811

eISSN: 1532-0634

ISSN: 1532-0626

DOI: 10.1002/cpe.3108

The data on this page was last updated at 05:16 on February 19, 2020.