Public Defence: Arian Nowbahari

Arian Nowbahari is defending his dissertation for the degree philosophiae doctor (PhD) at the University of South-Eastern Norway.

The doctoral work has been carried out at the Faculty of Technology, Natural Sciences and Maritime Sciences.

Thesis: Low Power Circuits and Architectures for Wireless Sensor Networks 

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Theme: State-of-the-Art circuits for low power (underwater) communication systems

You are welcome to follow the trial lecture and the public defence. 

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Summary:

This thesis reports State-of-the-Art circuits for (underwater) communication systems. Novel energy-efficient architectures have been designed and experimentally validated. Furthermore, mathematical models of low power circuits have been derived.  

The achieved results can be used to improve the energy efficiency of wireless sensor networks (WSNs), which represent one of the main enabling technologies of the Internet-of-Things (IoT) paradigm. These networks, consisting of sensor nodes characterized by processing and transmitting capabilities, are implemented in various fields such as oceanography, disaster prevention, the oil and gas industry, health, commercial, and military applications. A critical challenge in designing WSNs is optimizing the sensor node’s power consumption, which determines the network lifetime. 

One of the most efficient energy-saving approaches consists of integrating Wake-Up Receivers (WuRxs), which allow selective activation of the sensor nodes on demand. This thesis reports three novel WuRx architectures and six low power circuit implementations. Two of the implementations present the lowest power consumption reported for an underwater acoustic WuRx. Their performances have been experimentally validated through Integrated Circuits by decoding signals transmitted underwater. At simulation level, one of the designed circuit presents the lowest power consumption reported for an acoustic WuRx.

The thesis also reports the derivation of analytical models for the subthreshold operation of two Schmitt triggers (STs), which are extensively implemented in sensor node architectures. The derived expressions have been experimentally validated, and provide physical insight into the behavior of the circuits, by relating the hysteresis voltages to the transistors’ geometrical parameters. Furthermore, the models can be used to predict the effect of supply voltage and temperature variations on the characteristics. 

Overall, the proposed circuits and architectures can be used to extend the lifetime of wireless sensor networks for (underwater) IoT.