Publication Type
Journal Article
Version
publishedVersion
Publication Date
11-2014
Abstract
Network simulation is an essential tool for the design and evaluation of wireless network protocols, and realistic channel modeling is essential for meaningful analysis. Recently, several network protocols have demonstrated substantial network performance improvements by exploiting the capture effect, but existing models of the capture effect are still not adequate for protocol simulation and analysis. Physical-level models that calculate the signal-to-interference-plus-noise ratio (SINR) for every incoming bit are too slow to be used for large-scale or long-term networking experiments, and link-level models such as those currently used by the NS2 simulator do not accurately predict protocol performance. In this article, we propose a new technique called the capture modeling algorithm (CAMA) that provides the simulation fidelity of physical-level models while achieving the simulation time of link-level models. We confirm the validity of CAMA through comparison with the empirical traces of the experiments conducted by various numbers of CC1000 and CC2420-based nodes in different scenarios. Our results indicate that CAMA can accurately predict the packet reception, corruption, and collision detection rates of real radios, while existing models currently used by the NS2 simulator produce substantial prediction error.
Keywords
Wireless sensor networks, radio interference, packet collisions
Discipline
Computer and Systems Architecture | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ACM Transactions on Sensor Networks
Volume
11
Issue
1
First Page
1
Last Page
43
ISSN
1550-4859
Identifier
10.1145/2629352
Publisher
Association for Computing Machinery (ACM)
Citation
DEZFOULI, Behnam; RADI, Marjan; WHITEHOUSE, Kamin; RAZAK, Shukor Abd; and TAN, Hwee-Pink.
CAMA: Efficient Modeling of the Capture Effect for Low Power Wireless Networks. (2014). ACM Transactions on Sensor Networks. 11, (1), 1-43.
Available at: https://ink.library.smu.edu.sg/sis_research/2964
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1145/2629352