Modeling Low-power Wireless Communications
Low-power wireless communications have particular characteristics that highly affect the performance of network protocols. However, many of these essential characteristics have not been considered in the existing simulation platforms and analytical performance evaluation models. While this issue inv...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2015
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/2896 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Low-power wireless communications have particular characteristics that highly affect the performance of network protocols. However, many of these essential characteristics have not been considered in the existing simulation platforms and analytical performance evaluation models. While this issue invalidates many of the reported evaluation results, it also impedes pre-deployment performance prediction and parameter adjustment Accordingly, this paper studies, analyzes and proposes models for accurate modeling of low-power wireless communications. Our contributions are six-fold. First, we investigate the essential characteristics of low-power wireless transceivers. Second, we present a classified and detailed study on modeling signal propagation, noise floor, system variations and interference. Third, we highlight the importance and effects of system variations and radio regularity on the real applications of wireless sensor networks. Fourth, we reveal the inaccuracy of the packet reception algorithms used in the existing simulators. Furthermore, we propose an improved packet reception algorithm and we confirm its accuracy through comparison with empirical results. Fifth, we propose an architecture to integrate and implement the models presented in this paper. Finally, we show that the transitional region can be employed by the simulators to confine the propagation range and improve simulation scalability. To the best of our knowledge this is the first work that reveals the essentials of accurate modeling and evaluation of low-power wireless communications. (C) 2014 Elsevier Ltd. All rights reserved. |
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