A self-powered single-chip wireless sensor platform

Internet of things” require a large array of low-cost sensor nodes, wireless connectivity, low power operation and system intelligence. On the other hand, wireless biomedical implants demand additional specifications including small form factor, a choice of wireless operating frequencies within the window for minimum tissue loss and bio-compatibility This thesis describes a low power and low-cost internet of things system suitable for implant applications that is implemented in its entirety on a single standard CMOS chip with an area smaller than 0.5 mm2. The chip includes integrated sensors, ultra-low-power transceivers, and additional interface and digital control electronics while it does not require a battery or complex packaging schemes. It is powered through electromagnetic (EM) radiation using its on-chip miniature antenna that also assists with transmit and receive functions. The chip can operate at a short distance (a few centimeters) from an EM source that also serves as its wireless link. Design methodology, system simulation and optimization and early measurement results are presented

Low power CMOS IC, biosensor and wireless power transfer techniques for wireless sensor network application

The emerging field of wireless sensor network (WSN) is receiving great attention due to the interest in healthcare. Traditional battery-powered devices suffer from large size, weight and secondary replacement surgery after the battery life-time which is often not desired, especially for an implantable application. Thus an energy harvesting method needs to be investigated. In addition to energy harvesting, the sensor network needs to be low power to extend the wireless power transfer distance and meet the regulation on RF power exposed to human tissue (specific absorption ratio). Also, miniature sensor integration is another challenge since most of the commercial sensors have rigid form or have a bulky size. The objective of this thesis is to provide solutions to the aforementioned challenges

RF Integrated Circuits for Energy Autonomous Sensor Nodes.

The exponential growth in the semiconductor industry has enabled computers to pervade our everyday lives, and as we move forward many of these computers will have form factors much smaller than a typical laptop or smartphone. Sensor nodes will soon be deployed ubiquitously, capable of capturing information of their surrounding environment. The next step is to connect all these different nodes together into an entire interconnected system. This “Internet of Things” (IoT) vision has incredible potential to change our lives commercially, societally, and personally. The backbone of IoT is the wireless sensor node, many of which will operate under very rigorous energy constraints with small batteries or no batteries at all. It has been shown that in sensor nodes, radio communication is one of the biggest bottlenecks to ultra-low power design. This research explores ways to reduce energy consumption in radios for wireless sensor networks, allowing them to run off harvested energy, while maintaining qualities that will allow them to function in a real world, multi-user environment. Three different prototypes have been designed demonstrating these techniques. The first is a sensitivity-reduced nanowatt wake-up radio which allows a sensor node to actively listen for packets even when the rest of the node is asleep. CDMA codes and interference rejection reduce the potential for energy-costly false wake-ups. The second prototype is a full transceiver for a body-worn EKG sensor node. This transceiver is designed to have low instantaneous power and is able to receive 802.15.6 Wireless Body Area Network compliant packets. It uses asymmetric communication including a wake-up receiver based on the previous design, UWB transmitter and a communication receiver. The communication receiver has 10 physical channels to avoid interference and demodulates coherent packets which is uncommon for low power radios, but dictated by the 802.15.6 standard. The third prototype is a long range transceiver capable of >1km communication range in the 433MHz band and able to interface with an existing commercial radio. A digitally assisted baseband demodulator was designed which enables the ability to perform bit-level as well as packet-level duty cycling which increases the radio's energy efficiency.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110432/1/nerobert_1.pd

Optimisation of vibration monitoring nodes in wireless sensor networks

This PhD research focuses on developing a wireless vibration condition monitoring (CM) node which allows an optimal implementation of advanced signal processing algorithms. Obviously, such a node should meet additional yet practical requirements including high robustness and low investments in achieving predictive maintenance. There are a number of wireless protocols which can be utilised to establish a wireless sensor network (WSN). Protocols like WiFi HaLow, Bluetooth low energy (BLE), ZigBee and Thread are more suitable for long-term non-critical CM battery powered nodes as they provide inherent merits like low cost, self-organising network, and low power consumption. WirelessHART and ISA100.11a provide more reliable and robust performance but their solutions are usually more expensive, thus they are more suitable for strict industrial control applications. Distributed computation can utilise the limited bandwidth of wireless network and battery life of sensor nodes more wisely. Hence it is becoming increasingly popular in wireless CM with the fast development of electronics and wireless technologies in recent years. Therefore, distributed computation is the primary focus of this research in order to develop an advanced sensor node for realising wireless networks which allow high-performance CM at minimal network traffic and economic cost. On this basis, a ZigBee-based vibration monitoring node is designed for the evaluation of embedding signal processing algorithms. A state-of-the-art Cortex-M4F processor is employed as the core processor on the wireless sensor node, which has been optimised for implementing complex signal processing algorithms at low power consumption. Meanwhile, an envelope analysis is focused on as the main intelligent technique embedded on the node due to the envelope analysis being the most effective and general method to characterise impulsive and modulating signatures. Such signatures can commonly be found on faulty signals generated by key machinery components, such as bearings, gears, turbines, and valves. Through a preliminary optimisation in implementing envelope analysis based on fast Fourier transform (FFT), an envelope spectrum of 2048 points is successfully achieved on a processor with a memory usage of 32 kB. Experimental results show that the simulated bearing faults can be clearly identified from the calculated envelope spectrum. Meanwhile, the data throughput requirement is reduced by more than 95% in comparison with the raw data transmission. To optimise the performance of the vibration monitoring node, three main techniques have been developed and validated: 1) A new data processing scheme is developed by combining three subsequent processing techniques: down-sampling, data frame overlapping and cascading. On this basis, a frequency resolution of 0.61 Hz in the envelope spectrum is achieved on the same processor. 2) The optimal band-pass filter for envelope analysis is selected by a scheme, in which the complicated fast kurtogram is implemented on the host computer for selecting optimal band-pass filter and real-time envelope analysis on the wireless sensor for extracting bearing fault features. Moreover, a frequency band of 16 kHz is analysed, which allows features to be extracted in a wide frequency band, covering a wide category of industrial applications. 3) Two new analysis methods: short-time RMS and spectral correlation algorithms are proposed for bearing fault diagnosis. They can significantly reduce the CPU usage, being over two times less and consequently much lower power consumptio