Performance of ZigBee networks in the presence of broadband electromagnetic noise

Abstract

This research project was conducted at TRLabs and the sensor network testbed (SENETBED) at the University of Manitoba, for an international sponsor, Vansco/Parker Inc. The project aimed to determine the impact of electromagnetic noise on the communications performance of a ZigBee sensor network, embedded within a large industrial excavator. The project consisted of five phases. Phase 1 [1] has three deliverables: (i) determined the requirements and chose a wireless technology to replace the wireline technology in the deployment of a sensor control system for industrial machinery; (ii) modeled broadband electromagnetic noise using contemporary fractal theory; and (iii) designed a novel emulation environment for testing the performance of a wireless network under noise. Phase 2 [2] addressed an improved experimental setup, and provided preliminary Packet Error Rate (PER) vs. Signal-to-Noise Ratio (SNR) results, showing the impact of fractal generated noise on ZigBee communications. Phase 3 [3] analyzed the process of modulating a monofractal, and found that the modulated monofractal changed character to a multifractal; however, a small frequency range can be found where the signal is approximately monofractal. Phase 4 [4], (i) captured electromagnetic noise emanated from the starter motor of a large industrial tractor; (ii) performed fractal measurements on the data; and (iii) found that the noise exhibited fractal and multifractal characteristics, verifying that fractal theory is indeed a good model of broadband electromagnetic noise. This paper summarizes the first four phases of this research project, to provide continuity and readability. The results and contributions of Phase 5, the final instalment of this project, include: (i) the SNR was measured more accurately by using a written C#-program, which programmatically captured the ZigBee and noise signals' traces from a spectrum analyzer, and calculated the signal and noise powers directly from the amplitudes. The direct method of SNR measurement improved the accuracy of the PER vs. SNR result; (ii) The SNR measurements were also improved by changing the location of the actual measurements. The SNR measured at the receiver was used to obtain PER vs. SNR data, while the SNR measured at the transmitter SNR was used to obtained maximum node separation data; (iii) noise emulation was improved by injecting the noise into the channel at the receiver; and (iv) the injected noise was tuned to match the character of the captured starter motor noise. In Phase 5, we found that a better model of noise injection was to assume that the noise was uniformly and equally distributed in space, so that the incremental impacts of the noise throughout the channel could be modeled by injecting the noise at the receiver.