Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits

Data Conversion Within Energy Constrained Environments

Within scientific research, engineering, and consumer electronics, there is a multitude of new discrete sensor-interfaced devices. Maintaining high accuracy in signal quantization while staying within the strict power-budget of these devices is a very challenging problem. Traditional paths to solving this problem include researching more energy-efficient digital topologies as well as digital scaling.;This work offers an alternative path to lower-energy expenditure in the quantization stage --- content-dependent sampling of a signal. Instead of sampling at a constant rate, this work explores techniques which allow sampling based upon features of the signal itself through the use of application-dependent analog processing. This work presents an asynchronous sampling paradigm, based off the use of floating-gate-enabled analog circuitry. The basis of this work is developed through the mathematical models necessary for asynchronous sampling, as well the SPICE-compatible models necessary for simulating floating-gate enabled analog circuitry. These base techniques and circuitry are then extended to systems and applications utilizing novel analog-to-digital converter topologies capable of leveraging the non-constant sampling rates for significant sample and power savings

ENERGY HARVESTING TECHNIQUES IN WIRELESS SENSOR NETWORKS

Batteries are the main source of energy for low-power electronics such as micro-electro mechanical systems (MEMS), wireless sensor networks, embedded devices for remote sensing and control, etc. With the limited capacity of finite power sources and the need for supplying energy for the lifetime of a system/device there is a requirement for self-powered devices. Using conventional batteries is not always good design solution because batteries require human intervention to replace them (very often in hard-accessible and harsh-environmental conditions). Therefore, acquiring the electrical power, by using an alternative source of energy that is needed to operate these devices is a major concern. The process of extracting energy from the surrounding environment and converting it into consumable electrical energy is known as energy harvesting or power scavenging. The energy harvesting sources can be used to increase the lifetime and capability of the devices by either replacing or augmenting the battery usage. There are various forms of energy that can be scavenged, like solar, mechanical, thermal, and electromagnetic. Nowadays, there is a big interest in the field of research related to energy harvesting. This paper represents a survey for identifying the sources of energy harvesting and describes the basic operation of principles of the most common energy harvester. As first, we present, in short, the conversion principles of single energy source harvesting systems and point to their benefits and limitations in their usage. After that, hybrid structures of energy harvesters which simultaneously combine scavenged power from different ambient sources (solar, thermoelectric, electromagnetic), with aim to support higher load at the output, are considered

Energy Harvesting Systems for the Internet of Things with Applications to Smart Agriculture

The Internet of Things is the interconnection of everyday objects to the web, with the purpose of exchanging information to enable smarter actions and potentially make a process more efficient. However, how power is provided and stored in remote sensing applications is still one of the main modern electronics challenges of such technology and can become one of the main constraints to prevent its mass adoption. Energy Harvesting is an emerging technology that can transform energy in the environment into usable energy, among such environmental energy are electromagnetic waves, thermal, solar, kinesthetic transducers, fuel cells, to name a few. Because this technology makes use of the available ambient energy, it has the potential to increase the power readiness for battery-operated electronics and more importantly, it can become the technology that fully powers the next generation of internet-enabled agricultural solutions. This dissertation centers around the design and development of high-efficient power management systems for AC and DC energy harvesting sources. The proposed architectures not only consider circuits, systems and algorithms that make a more efficient power extraction but also focuses on providing inherent sensing functionalities at no extra system complexity, which in turn not only achieves the goal of extending the battery life of proposed smart sensor applications but also proposes new charge extraction methods to permanently power an electronic device. The work presented in this dissertation demonstrates that energy harvesting, and internet of things devices can be implemented in multiple smart agriculture scenarios by proposing algorithms, circuits and systems capable of performing energy harvesting operations while providing reliable data to the end user. The analysis of the design of such proof-of-concept prototypes are provided in this dissertation along with its implementation and testing. The first part of this dissertation proposes novel algorithms for maximum power extraction and new power measurement techniques. The second part focuses on front-end circuits for AC energy harvesting sources and circuits that can provide sensing capabilities along with energy harvesting operations