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Efficient collection of sensor data via a new accelerated random walk.

Angelopoulos, C.M., Nikoletseas, S., Patroumpa, D. and Raptopoulos, C., 2016. Efficient collection of sensor data via a new accelerated random walk. Concurrency and Computation, 28 (6), 1796 - 1811.

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sigproc_camera.pdf - Accepted Version
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DOI: 10.1002/cpe.3108


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.

Item Type:Article
Uncontrolled Keywords:wireless sensor networks; random walks; data collection; sink mobility
Group:Faculty of Science & Technology
ID Code:24077
Deposited By: Symplectic RT2
Deposited On:21 Jun 2016 09:12
Last Modified:14 Mar 2022 13:56


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