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Campus Access

Document Type


Degree Program

Mechanical Engineering

Degree Type

Master of Science (M.S.)

Year Degree Awarded


Month Degree Awarded



Energy aware systems, Signal compression, Walsh transform, Wireless sensor networks


The operational life of wireless sensor network based distributed sensing systems is limited by the energy provided by a portable battery pack. Owing to the inherently resource constrained nature of wireless sensor networks and nodes, a major research thrust in this field is the search for energy-aware methods of operation. Communication is among the most energy-intensive operations on a wireless device. It is therefore, the focus of our efforts to develop an energy-aware method of communication and to introduce a degree of reconfigurability to ensure autonomous operation of such devices. Given this background, three research tasks have been identified and investigated during the course of this research.

1) Devising an energy-efficient method of communication in a framework of reconfigurable operation: The dependence of the energy consumed during communication on the number of bits transmitted (and received) was identified from prior research work. A novel method of data compression was designed to exploit this dependence. This method uses the time-limited, orthonormal Walsh functions as basis functions for representing signals. The L2 norm of this representation is utilized to further compress the signals. From Parseval’s relation, the square of the L2 norm represents the energy content of a signal. The application of this theorem to our research makes it possible to use the L2 norm as a control knob. The operation of this control knob makes it possible to optimize the number of terms required to represent signals.

The time-limited nature of the Walsh functions was leveraged to inject dynamic behaviour into our coding method. This time-limited nature allows decomposition of finite time-segments, without attendant limitations like loss of resolution that are inherent to derived, discrete transforms like the discrete Fourier transform or the discrete time Fourier transform. This decomposition over successive, finite time-segments, coupled with innovative operation of the previously mentioned control knob on every segment, gives us a dynamic scaling technique. The amount of data to be transmitted is in turn based on the magnitude of the coefficients of decomposition of each time-segment, leading to the realization of a variable word length coding method.

This dynamic coding method can identify evolving changes or events in the quantity being sensed. The coefficients of decomposition represent features present in successive time-segments of signals and therefore enable identification of evolving events. The ability to identify events as they occur enables the algorithm to react to events as they evolve in the system. In other words the data transmission and the associated energy consumption are imparted a reconfigurable, event-driven nature by implementation of the coding algorithm. Performance evaluation of this method via simulations on machine generated (bearing vibration) and biometric (electro-cardio gram) signals shows it be a viable method for energy-aware communication.

2) Developing a framework for reconfigurable triggering: A framework for completely autonomous triggering of the coding method has been developed. This is achieved by estimating correlations of the signal with the representative Walsh functions. The correlation coefficient of a signal segment with a Walsh function gives a picture of the amount of energy localized by the function. This information is used to autonomously tune the abovementioned control knob or, in more proper terms, the degree of thresholding used in compression. Evaluation of this framework on bearing vibration and electro-cardio gram signals has shown results consistent with those of previous simulations.

3) Devising a computationally compact method of feature classification: A method of investigating time series measurements of dynamic systems in order to classify features buried in the signal measurements was investigated. The approach involves discretizing time-series measurements into strings of pre-defined symbols. These strings are transforms of the original time-series measurements and are a representation of the system dynamics. A method of statistically analyzing the symbol strings is presented and its efficacy is studied through representative simulations and experimental investigation of vibration signals recorded from a rolling bearing element. The method is computationally compact because it obviates the need for local signal processing tasks like denoising, detrending and amplification. Results indicate that the method can effectively classify deteriorating machine health, changing operating conditions and evolving defects.

In addition to these major foci, another research task was the design and implementation of a wireless network testbed. This testbed consists of a network of netbooks, connected together wirelessly and was utilized for experimental verification of the variable word length coding method.


First Advisor

Robert X. Gao