Error pattern analysis for data transmission of wireless sensors on rotating industrial structures

Transmission Error Avoidance for IEEE 802.15.4 Wireless Sensors on Rotating Structures

“…Any bits transmitted while the transmitting sensor is within an error region are subject to a high bit error rate (BER). In [12], the error region width was estimated, based on different assumed BERs, to be between 4~23 bit durations (16~92 µsec at 250 kbps data transmission rate, equivalent to 0.2~1.1 degree under 2054 rpm rotation). It is interesting to note that such a seemingly small error region has caused an average PER of 11.8%.…”

“…Obtaining such information and ensuring its accuracy are by themselves challenging, since (1) accurate rotation speed can be obtained only with complex sensor design or be reported by the rotating machine, (2) the estimated or reported rotation speed can still be inaccurate, and (3) the sensor clock can be inaccurate with respect to the clock used for speed measurement. In [12], the impact of inaccurate knowledge of rotation speed was studied. Inaccurate time can potentially be resolved with a time synchronization or calibration function.…”

“…Given the distribution, the center and width of each error region with PER above a chosen threshold is identified. In [12], it was shown that the error region width depends on the assumed BER for the error region. The higher the assumed BER, the narrower the width, while the more chances an error could occur outside the defined error region, posing a trade off between energy efficiency and data throughput.…”

“…As later shown in this paper, PERs can easily reach as high as 50% under certain configuration and channel characteristics. In [12], the temporal distribution and size of error regions were analyzed based on recorded time stamps and bit contents of failed probing transmissions. To reduce such transmission errors for enhancing data throughput and energy efficiency, this paper studies an error-avoidant transmission scheme utilizing such error region distribution.…”

Average Delay Estimation in Discrete-Time Systems with Periodically Varying Parameters

“…In sensor networks, wireless channel quality variation may also be periodic due to the specific properties of the environment [8]. This is particularly true in several specialized applications, such as given by [9], where periodic variation in the signal strength is due to rotating radio transmitters. Finally, cognitive radio networks also contribute to the set of potential applications for our model where the occupancy of a channel is sometimes found to be completely periodic [10].…”