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|Title: ||Performance Evaluation of a Wireless Protocol for Mesh Networking under the Influence of Broadband Electromagnetic Noise|
|Authors: ||Woo, Lily Lai Yam|
|Supervisor: ||Kinsner, Witold (Electrical and Computer Engineering) Ferens, Kenneth (Electrical and Computer Engineering)|
|Examining Committee: ||Diamond, Jeff (Electrical and Computer Engineering) Sepehri, Nariman (Mechanical and Manufacturing Engineering)|
|Graduation Date: ||May 2010|
|Keywords: ||communication system performance|
HF radio communication
broadband electromagnetic interference
packet error rate
|Issue Date: ||9-Apr-2010|
|Citation: ||Lily Woo, Witold Kinsner, Ken Ferens, and Jeff Diamond, “Modelling and emulation of multifractal noise in performance evaluation of mesh networks,” in IEEE 2006 Proc. Can. Conf. Electr. and Comput. Eng. (CCECE 2006), Ottawa, ON, May 7–10, 2006, pp. 1646–1651.|
Lily Woo, Witold Kinsner, Ken Ferens, and Jeff Diamond, “Performance results and analysis of ZigBee networks in the presence of multifractal noise,” in IEEE 2007 Proc. Can. Conf. Electr. and Comput. Eng. (CCECE 2007), Vancouver, BC, Apr. 22–25, 2007, pp. 924–927.
Lily Woo, Michael Potter, Witold Kinsner, and Ken Ferens, “Analysis of Modulated Monofractal Noise,” in IEEE 2007 Proc. Can. Conf. Electr. and Comput. Eng. (CCECE 2007), Vancouver, BC, Apr. 22–25, 2007, pp. 884–887.
Lily Woo, Witold Kinsner, and Ken Ferens, “An analysis of captured industrial vehicular noise signals for ZigBee communications,” in IEEE 2008 Proc. Can. Conf. Electr. and Comput. Eng. (CCECE 2008), Niagara Falls, ON, May 4–7, 2008, pp. 1423–1428.
Ken Ferens, Lily Woo, and Witold Kinsner, “Performance of ZigBee networks in the presence of broadband electromagnetic noise,” in IEEE 2009 Proc. Can. Conf. Electr. and Comput. Eng. (CCECE 2009), St. John’s, NL, May 3-6, 2009, pp. 407-410.
Lily Woo, Ken Ferens, and Witold Kinsner “Reliability of ZigBee networks under broadband electromagnetic noise interference,” in IEEE 2010 Proc. Can. Conf. Electr. and Comput. Eng. (CCECE 2010), Calgary, AB, May 2-5, 2010 (accepted).
|Abstract: ||Migrating from a wired to a wireless implementation for communication system used in industrial applications is a logical move to avoid the many shortcomings associated with wires. When operated under harsh environments, those wires can break and could cause not only damage to the system but also endanger human lives. However, it is not well documented how well a wireless protocol can work under such harsh industrial environments. This thesis attempts to answer this research question in the point of view of gauging the performance of a wireless protocol under the influence of electromagnetic noise. More specifically, the type of noise signal that is the focus of this investigation is the random, pulsed type (e.g., discharges caused by sparking) that creates a hyperbolic broadband disturbance in the frequency domain. Consequently, a fractal noise model is used to study noise of this nature. The steps toward achieving this goal include: requirements gathering, wireless technology selection; noise modelling and synthesis; real noise capture and analysis to validate the chosen noise model; high-frequency fractal noise emulation in hardware; the use of a novel noise injection method for empirical work; and the conducting of a controlled synthetic noise-to-wireless node performance evaluation to obtain performance measure in the form of packet error rate (PER). Performance data in terms of PER versus signal-to-noise ratio (SNR) for various nodes separation have been collected. There were three significant findings: the obtained performance curves follow the standard 'S' trend; for a specific desired reliability (denoted by a certain PER), the SNR at the transmitter needs to be boosted as the correlation of the noise being present increases; and the maximum distance between nodes separation for a certain reliability to be achieved depends exponentially with the transmitter‟s SNR. The relationship in the third finding assists in placement of wireless nodes, which in turn can determine the minimum amount of wireless nodes required for an industrial system to reach the desired system reliability, thus boasting network cost saving.|
|Appears in Collection(s):||FGS - Electronic Theses & Dissertations (Public)|
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