Signal-Processing-Driven Integrated Circuits for Energy Constrained Microsystems.

The exponential growth in IC technology has enabled low-cost and increasingly capable wireless sensor nodes which provide a promising way forward to realize the vision of a trillion connected sensors in the next decade. However there are still many design challenges ahead to make these sensor nodes small,low-cost,secure,reliable and energy-efficient to name a few. Since the wireless nodes are expected to operate on a limited energy source or in some cases on harvested energy, the energy consumption of each building block is of prime importance to prolong the life of a sensor node. It has been found that the radio communication when active has been one of the highest power consuming modules on a sensor node. Low-energy protocols, e.g. processing the raw sensor data on-node, are more energy efficient for some applications as compared to transmitting the raw data over a wireless channel to a cloud server. In this thesis we explore signal processing techniques to realize a low power radio solution for wireless communication. Two prototype chips have been designed and their performance has been evaluated. The first prototype chip exploits compressed sensing for Ultra-Wide-Band (UWB) communication. UWB signals typically require a high ADC sampling rate in the receiver which results in high power consumption. Compressed sensing is demonstrated to relax the ADC sampling rate to save power. The second prototype chip exploits the sensitivity vs. power trade-off in a radio receiver to achieve iso-performance at lower power consumption and the time-varying wireless channel characteristics are used to adapt the sampling frequency of the receiver based on the SNR/Link quality of the communication channel, saving power, while maintaining the desired system performance. It is envisioned that embedded machine learning will play a key role in the integration of sensory data with prior knowledge for distributed intelligent sensing which might enable reduced wireless network traffic to a cloud server. A Near-Threshold hardware accelerator for arbitrary Bayesian network was designed for clique-tree message passing algorithm used for probabilistic inference. The hardware accelerator was benchmarked by the mid-size ALARM Bayesian network with total energy consumption of 76nJ for 250µS execution time.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107130/1/oukhan_1.pd

Ultra Low Power Communication Protocols for UWB Impulse Radio Wireless Sensor Networks

This thesis evaluates the potential of Ultra Wideband Impulse Radio for wireless sensor network applications. Wireless sensor networks are collections of small electronic devices composed of one or more sensors to acquire information on their environment, an energy source (typically a battery), a microcontroller to control the measurements, process the information and communicate with its peers, and a radio transceiver to enable these communications. They are used to regularly collect information within their deployment area, often for very long periods of time (up to several years). The large number of devices often considered, as well as the long deployment durations, makes any manual intervention complex and costly. Therefore, these networks must self-configure, and automatically adapt to changes in their electromagnetic environment (channel variations, interferers) and network topology modifications: some nodes may run out of energy, or suffer from a hardware failure. Ultra Wideband Impulse Radio is a novel wireless technology that, thanks to its extremely large bandwidth, is more robust to frequency dependent propagation effects. Its impulsional nature makes it robust to multipath fading, as the short duration of the pulses leads most multipath components to arrive isolated. This technology should also enable high precision ranging through time of flight measurements, and operate at ultra low power levels. The main challenge is to design a system that reaches the same or higher degree of energy savings as existing narrowband systems considering all the protocol layers. As these radios are not yet widely available, the first part of this thesis presents Maximum Pulse Amplitude Estimation, a novel approach to symbol-level modeling of UWB-IR systems that enabled us to implement the first network simulator of devices compatible with the UWB physical layer of the IEEE 802.15.4A standard for wireless sensor networks. In the second part of this thesis, WideMac, a novel ultra low power MAC protocol specifically designed for UWB-IR devices is presented. It uses asynchronous duty cycling of the radio transceiver to minimize the power consumption, combined with periodic beacon emissions so that devices can learn each other's wake-up patterns and exchange packets. After an analytical study of the protocol, the network simulation tool presented in the first part of the thesis is used to evaluate the performance of WideMac in a medical body area network application. It is compared to two narrowband and an FM-UWB solutions. The protocol stack parameters are optimized for each solution, and it is observed that WideMac combined to UWB-IR is a credible technology for such applications. Similar simulations, considering this time a static multi-hop network are performed. It is found that WideMac and UWB-IR perform as well as a mature and highly optimized narrowband solution (based on the WiseMAC ULP MAC protocol), despite the lack of clear channel assessment functionality on the UWB radio. The last part of this thesis studies analytically a dual mode MAC protocol named WideMac-High Availability. It combines the Ultra Low PowerWideMac with the higher performance Aloha protocol, so that ultra low power consumption and hence long deployment times can be combined with high performance low latency communications when required by the application. The potential of this scheme is quantified, and it is proposed to adapt it to narrowband radio transceivers by combining WiseMAC and CSMA under the name WiseMAC-HA