An adaptive low-power listening protocol for wireless sensor networks in noisy environments

Dinh, T, Kim, Y, Gu, T and Vasilakos, A 2018, 'An adaptive low-power listening protocol for wireless sensor networks in noisy environments', IEEE System Journal, vol. 12, no. 3, pp. 2162-2173.


Document type: Journal Article
Collection: Journal Articles

Title An adaptive low-power listening protocol for wireless sensor networks in noisy environments
Author(s) Dinh, T
Kim, Y
Gu, T
Vasilakos, A
Year 2018
Journal name IEEE System Journal
Volume number 12
Issue number 3
Start page 2162
End page 2173
Total pages 12
Publisher IEEE
Abstract This paper investigates the energy consumption minimization problem for wireless sensor networks running low-power listening (LPL) protocols in noisy environments. We observe that the energy consumption by false wakeups (i.e., wakeup without receiving any packet) of a node in noisy environments can be a dominant factor in many cases while the false wakeup rate is spatially and temporarily dynamic. Based on this observation, without carefully considering the impact of false wakeups, the energy efficient performance of LPL nodes in noisy environments may significantly deviate from the optimal performance. To address this problem, we propose a theoretical framework incorporating LPL temporal parameters with the false wakeup rate and the data rate. We then formulate an energy consumption minimization problem of LPL in noisy environments and address the problem by a simplified and practical approach. Based on the theoretical framework, we design an efficient adaptive protocol for LPL (APL) in noisy environments. Through extensive experimental studies with Telosb nodes in real environments, we show that APL achieves 20%-40% energy efficient improvement compared to existing LPL protocols under various network conditions.
Subject Networking and Communications
Keyword(s) Adaptive low power listening protocol
energy efficiency
energy optimization
noise environment
scheduling algorithm
wireless sensor network.
DOI - identifier 10.1109/JSYST.2017.2720781
Copyright notice © 2017 IEEE
ISSN 1932-8184
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