Reconfigurable Sampling for Enhanced Energy Efficiency in Power-Constrained Wireless Systems

<p> <p>Advancement in microelectronics and wireless communications has nurtured the development of low power and low cost wireless systems such as sensor networks. Each node is typically driven by a battery, which has a limited energy capacity that directly constrains node, and network lifetime. This presents a bottleneck for the widespread exploitation of wireless sensing technology. Therefore, improving the energy efficiency of wireless sensing devices is of critical importance and has attracted the attention of the research community. </p> <p>In this work, a data-driven method has been proposed and systematically investigated. Specifically, this technique reconfigures, in real time, the rate of data sampling based on the information content of the data acquired in the preceding step. Through this technique, significant reduction in energy usage and data load can be achieved, as is analytically and experimentally demonstrated in this work. </p> <p>The problem of reconfigurable data sampling as opposed to the traditional fixed-rate sampling is analytically and numerically evaluated. An algorithm that efficiently tracks the signal spectral properties has been developed, utilizing a novel filtering scheme based on the wavelet packet transform (WPT). Key elements of the algorithm include selective computation of WPT coefficients for sampling rate selection. Additionally, a signal reconstruction procedure has been derived for the case of non-uniform sampling, which allows the signal to be retrieved from the adaptively acquired samples. A post-analysis has also been formulated to quantify error in sampling rate adjustment. Simulation and experimental studies indicate significant data reduction leading to decreased energy consumption, while maintaining signal fidelity.</p> </p>

Name thesis:degree_name

Master of Science

Discipline thesis:degree_discipline

Mechanical Engineering

Year dc:date.available

2011

Author dc:creator

• Kurp, Timothy

Contributors dc:contributor

• Jiong Tang; Nejat Olgac
• Robert X. Gao

• wireless sensing
• reconfigurable
• adaptive sampling
• energy efficiency
• signal reconstruction
• wavelet transform

Repository record dc:identifier

https://digitalcommons.lib.uconn.edu/gs_theses/125

OAI identifier oai:identifier

oai:digitalcommons.lib.uconn.edu:gs_theses-1141