Power consumption is one of the most important aspects in mobile and wireless communications. Existing research has shown significant power reduction through limited radio reconfiguration based on the channel conditions, especially for short range sensor network applications. A cognitive radio (CR) is an intelligent wireless communication system which is able to determine the most favorable operating parameters (cognition) based on the radio environment and its own capabilities and characteristics (awareness) and reconfigure the radio accordingly (reconfigurability). This work leverages the advances in cognitive radio technology to dynamically implement favorable trade-offs in radio parameters to achieve more efficient use of radio resource (e.g., minimizing power consumption) on the required Quality of Service (QoS) of an application and channel. A CR-based approach enables us not only to adjust modulation, coding, and radiated power as in a conventional radio, but also to learn and to control component characteristics (e.g., the power amplifier (PA) efficiency characteristic) to minimize power consumption. Significant power savings using this approach are shown in this work for single input single output (SISO) systems and multiple input multiple output (MIMO) systems. This work has a broad potential impact on the research of improving power efficiency of communication systems. It establishes a cognitive radio based methodology for system power consumption optimization. It emphasizes the difference between radiated power (power radiated from the transmit antenna) and the consumed power (power drawn from the power source, such as a battery). It provides a way to connect communication (which usually cares about radiated power, received signal to noise ratio, etc.) to hardware (which focuses on speed, efficiency, power consumption, etc.) and software (which emphasizes complexity, speed, etc.). This design methodology enhances the capability to jointly optimize communication, hardware, and software. In addition, this CR-based framework can be adapted for general radio resource management with various radio operation optimization targets, such as spectrum utilization. / Ph. D.

As an essential and indispensable contributor to pollinating the world's crops and plants, the honey bee is key to the sustainability of humans' and our ecosystems' continued survival. Following in the footsteps of the companies TietoEvry and Beelabs project, this report also works towards monitoring bees during their daily activities. This project aims to investigate the feasibility of using wireless, battery-driven devices inside beehives to detect the sound of bees using machine learning for edge devices. Beelab has focused on measurements in and around the beehive regarding weight, temperature, barometric pressure and humidity. Sound analysis is still in its infancy with few finished working alternatives; therefore, this project will focus on the sound attribute by implementing machine learning and classification algorithms and applying it to a prototype—the progress is thoroughly documented in this report. The device records a snippet of sound and prepares to send it over a wireless transmission medium. By streamlining the code and optimizing the hardware, the device runs continuously for a month using a small, cheap battery.