Journal article
Sensor Network Configuration Learning for Maximizing Application Performance
Sensors (Basel, Switzerland), Vol.18(6), p.1771
06/01/2018
Handle:
https://hdl.handle.net/2376/103826
PMCID: PMC6021864
PMID: 29865149
Abstract
Numerous applications rely on data obtained from a wireless sensor network where application performance is of utmost importance. However, energy usage is also important, and oftentimes, a subset of sensors can be selected to maximize application performance. We cast the problem of sensor selection as a local search optimization problem and solve it using a variant of stochastic hill climbing extended with novel heuristics. This paper introduces sensor network configuration learning, a feedback-based heuristic algorithm that dynamically reconfigures the sensor network to maximize the performance of the target application. The proposed algorithm is described in detail, along with experiments conducted and a scalability study. A quick method for launching the algorithm from a better starting point than random is also detailed. The performance of the algorithm is compared to that of two other well-known algorithms and randomness. Our simulation results obtained from running sensor network configuration learning on a number of scenarios show the effectiveness and scalability of our approach.
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Details
- Title
- Sensor Network Configuration Learning for Maximizing Application Performance
- Creators
- Joel Helkey - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA. jhelkey@wsu.eduLawrence Holder - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA. holder@wsu.edu
- Publication Details
- Sensors (Basel, Switzerland), Vol.18(6), p.1771
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- Switzerland
- Identifiers
- 99900547065101842
- Language
- English
- Resource Type
- Journal article