<div>Energy-efficient communication has remained the primary bottleneck in achieving fully energy-autonomous IoT nodes. Several scenarios including In-Sensor-Analytics (ISA), Collaborative Intelligence (CI) and Context-Aware-Switching (CAS) of the cluster-head during CI have been explored to trade-off the energies required for communication and computation in a wireless sensor network deployed in a mesh for multi-sensor measurement. A real-time co-optimization algorithm was developed for minimizing the energy consumption in the network for maximizing the overall battery lifetime of individual nodes.</div><div><br></div><div>The difficulty of achieving the design goals of lifetime, information accuracy, transmission distance, and cost, using traditional battery powered devices has driven significant research in energy-harvested wireless sensor nodes. This challenge is further amplified by the inherent power intensive nature of long-range communication when sensor networks are required to span vast areas such as agricultural fields and remote terrain. Solar power is a common energy source is wireless sensor nodes, however, it is not reliable due to fluctuations in power stemming from the changing seasons and weather conditions. This paper tackles these issues by presenting a perpetually-powered, energy-harvesting sensor node which utilizes a minimally sized solar cell and is capable of long range communication by dynamically co-optimizing energy consumption and information transfer, termed as Energy-Information Dynamic Co-Optimization (EICO). This energy-information intelligence is achieved by adaptive duty cycling of information transfer based on the total amount of energy available from the harvester and charge storage element to optimize the energy consumption of the sensor node, while employing event driven communication to minimize loss of information. We show results of continuous monitoring across 1Km without replacing the battery and maintaining an information accuracy of at least 95%.</div><div><br></div><div>Decades of continuous scaling in semiconductor technology has resulted in a drastic reduction in the cost and size of unit computing. This has enabled the design and development of small form factor wearable devices which communicate with each other to form a network around the body, commonly known as the Wireless Body Area Network (WBAN). These devices have found significant application for medical purposes such as reading surface bio-potential signals for monitoring, diagnosis, and therapy. One such device for the management of oropharyngeal swallowing disorders is described in this thesis. Radio wave transmission over air is the commonly used method of communication among these devices, but in recent years Human Body Communication has shown great promise to replace wireless communication for information exchange in a WBAN. However, there are very few studies in literature, that systematically study the channel loss of capacitive HBC for <i>wearable devices</i> over a wide frequency range with different terminations at the receiver, partly due to the need for <i>miniaturized wearable devices</i> for an accurate study. This thesis also measures and explores the channel loss of capacitive HBC from 100KHz to 1GHz for both high-impedance and 50Ohm terminations using wearable, battery powered devices; which is mandatory for accurate measurement of the HBC channel-loss, due to ground coupling effects. The measured results provide a consistent wearable, wide-frequency HBC channel loss data and could serve as a backbone for the emerging field of HBC by aiding in the selection of an appropriate operation frequency and termination.</div><div><br></div><div>Lastly, the power and security benefits of human body communication is demonstrated by extending it to animals (animal body communication). A sub-inch^3, custom-designed sensor node is built using off the shelf components which is capable of sensing and transmitting biopotential signals, through the body of the rat at significantly lower powers compared to traditional wireless transmissions. In-vivo experimental analysis proves that ABC successfully transmits acquired electrocardiogram (EKG) signals through the body with correlation accuracy >99% when compared to traditional wireless communication modalities, with a 50x reduction in power consumption.</div>

<div>The huge gamut of today’s internet-connected embedded devices has led to increasing concerns regarding the security and confidentiality of data. To address these requirements, most embedded devices employ cryptographic algorithms, which are computationally secure. Despite such mathematical guarantees, as these algorithms are implemented on a physical platform, they leak critical information in the form of power consumption, electromagnetic (EM) radiation, timing, cache hits and misses, and so on, leading to side-channel analysis (SCA) attacks. Non-profiled SCA attacks like differential/correlational power/EM analysis (DPA/CPA/DEMA/CEMA) are direct attacks on a single device to extract the secret key of an encryption algorithm. On the other hand, profiled attacks comprise of building an offline template (model) using an identical device and the attack is performed on a similar device with much fewer traces.</div><div><br></div><div>This thesis focusses on developing efficient side-channel attacks and circuit-level low-overhead generic countermeasures. A cross-device deep learning-based profiling power side-channel attack (X-DeepSCA) is proposed which can break the secret key of an AES-128 encryption engine running on an Atmel microcontroller using just a single power trace, thereby increasing the threat surface of embedded devices significantly. Despite all these advancements, most works till date, both attacks as well as countermeasures, treat the crypto engine as a black box, and hence most protection techniques incur high power/area overheads.</div><div><br></div><div>This work presents the first white-box modeling of the EM leakage from a crypto hardware, leading to the understanding that the critical correlated current signature should not be passed through the higher metal layers. To achieve this goal, a signature attenuation hardware (SAH) is utilized, embedding the crypto core locally within the lower metal layers so that the critical correlated current signature is not passed through the higher metals, which behave as efficient antennas and its radiation can be picked up by a nearby attacker. Combination of the 2 techniques – current-domain signature suppression and local lower metal routing shows >350x signature attenuation in measurements on our fabricated 65nm test chip, leading to SCA resiliency beyond 1B encryptions, which is a 100x improvement in both EM and power SCA protection over the prior works with comparable overheads. Moreover, this is a generic countermeasure and can be utilized for any crypto core without any performance degradation.</div><div><br></div><div>Next, backed by our physics-level understanding of EM radiation, a digital library cell layout technique is proposed which shows >5x reduction in EM SCA leakage compared to the traditional digital logic gate layout design. Further, exploiting the magneto-quasistatic (MQS) regime of operation for the present-day CMOS circuits, a HFSS-based framework is proposed to develop a pre-silicon EM SCA evaluation technique to test the vulnerability of cryptographic implementations against such attacks during the design phase itself.</div><div><br></div><div>Finally, considering the continuous growth of wearable and implantable devices around a human body, this thesis also analyzes the security of the internet-of-body (IoB) and proposes electro-quasistatic human body communication (EQS-HBC) to form a covert body area network. While the traditional wireless body area network (WBAN) signals can be intercepted even at a distance of 5m, the EQS-HBC signals can be detected only up to 0.15m, which is practically in physical contact with the person. Thus, this pioneering work proposing EQS-HBC promises >30x improvement in private space compared to the traditional WBAN, enhancing physical security. In the long run, EQS-HBC can potentially enable several applications in the domain of connected healthcare, electroceuticals, augmented and virtual reality, and so on. In addition to these physical security guarantees, side-channel secure cryptographic algorithms can be augmented to develop a fully secure EQS-HBC node.</div>

Human body is a complex system organized at different levels such as cells, tissues and organs, which contributes to 11 important organ systems. The functional efficiency of this complex system is evaluated as health. Traditional healthcare is unable to accommodate everyone's need due to the ever-increasing population and medical costs. With advancements in technology and medical research, traditional healthcare applications are shaping into smart healthcare solutions. Smart healthcare helps in continuously monitoring our body parameters, which helps in keeping people health-aware. It provides the ability for remote assistance, which helps in utilizing the available resources to maximum potential. The backbone of smart healthcare solutions is Internet of Things (IoT) which increases the computing capacity of the real-world components by using cloud-based solutions. The basic elements of these IoT based smart healthcare solutions are called "things." Things are simple sensors or actuators, which have the capacity to wirelessly connect with each other and to the internet. The research for this dissertation aims in developing architectures for these things, focusing on IoT-based smart healthcare solutions. The core for this dissertation is to contribute to the research in smart healthcare by identifying applications which can be monitored remotely. For this, application-specific thing architectures were proposed based on monitoring a specific body parameter; monitoring physical health for family and friends; and optimizing the power budget of IoT body sensor network using human body communications. The experimental results show promising scope towards improving the quality of life, through needle-less and cost-effective smart healthcare solutions.

The aim of this thesis is to optimize the design of the Fat-IBC-based communication of a novel neuroprosthetic system in which a brain-machine interface is used to control a prosthetic arm. Fat-based intra-body communication (Fat-IBC) uses the fat tissue inside the body of the bearer as a transmission medium for low-power microwaves. Future projects will use the communication system and investigate ways to control the prosthetic arm directly from the brain. The finished system was able to individually control all movable joints of multiple prosthesis prototypes using information that was received wirelessly through Fat-IBC. Simultaneous transmission in the other direction was possible, with the control data then being replaced by sensor readings from the prosthesis. All data packets were encoded with the COBS/R algorithm and the wireless communication was handled by Digi Xbee 3 radio modules using the IEEE 802.15.4 protocol at a frequency of 2.45 GHz. The Fat-IBC communication was evaluated with the help of so-called "phantoms" which emulated the conditions of the human body fat channel. During said testing, packet loss measurements were performed for various combinations of packet sizes and time intervals between packets. The packet loss measurements showed that the typical amount of transmitted data could be handled well by the fat channel test setup. Although the transmission system was found to be well-functioning in its current state, increasing the packet size to achieve a higher granularity of the movement was perceived to be viable considering the findings from the packet loss measurements.

<p>The last few decades have witnessed unprecedented growth in multiple areas of electronics spanning low-power sensing, intelligent computing and high-speed wireless connectivity. In the foreseeable future, there would be hundreds of billions of computing devices, sensors, things and people, wherein the technology will become intertwined with our lives through continuous interaction and collaboration between humans and machines. Such human-centric ideas give rise to the concept of internet of bodies (IoB), which calls for novel and energy-efficient techniques for sensing, processing and secure communication for resource-constrained IoB nodes.As we have painfully learnt during the pandemic, point-of-care diagnostics along with continuous sensing and long-term connectivity has become one of the major requirements in the healthcare industry, further emphasizing the need for energy-efficiency and security in the resource-constrained devices around us.</p> <p>  </p> <p>  With this vision in mind, I’ll divide this dissertation into the following chapters. The first part (Chapter 2) will cover time-domain sensing techniques which allow inherent energy-resolution scalability, and will show the fundamental limits of achievable resolution. Implementations will include 1) a radiation sensing system for occupational dosimetry in healthcare and mining applications, which can achieve 12-18 bit resolution with 0.01-1 µJ energy dissipation, and 2) an ADC-less neural signal acquisition system with direct Analog to Time Conversion at 13pJ/Sample. The second part (Chapters 3 and 4) of this dissertation will involve the fundamentals of developing secure energy-efficient electro-quasistatic (EQS) communication techniques for IoB wearables as well as implants, and will demonstrate  2 examples: 1) Adiabatic Switching for breaking the αCV^2f limit of power consumption in capacitive voltage mode human-body communication (HBC), and 2) Bi-Phasic Quasistatic Brain Communication (BP-QBC) for fully wireless data transfer from a sub-6mm^3, 2 µW brain implant. A custom modulation scheme, along with adiabatic communication enables wireline-like energy efficiencies (<5pJ/b) in HBC-based wireless systems, while the BP-QBC node, being fully electrical in nature, demonstrates sub-50pJ/b efficiencies by eliminating DC power consumption, and by avoiding the transduction losses observed in competing technologies, involving optical, ultrasound and magneto-electric modalities. Next in Chapter 5, we will show an implementation of a reconfigurable system that would include 1) a human-body communication transceiver and 2) a traditional wireless (MedRadio) transceiver on the same integrated circuit (IC), and would demonstrate methods to switch between the two modes by detecting the placement of the transmitter and receiver devices (on-body/away from the body). Finally, in Chapter 6, we shall show a technique of augmenting security in resource-constrained devices through authentication using the Analog/RF properties of the transmitter, which are usually discarded as non-idealities in a digital transceiver chain. This method does not require any additional hardware in the transmitter, making it an extremely promising technique to augment security in highly resource-constrained scenarios. Such energy-efficient intelligent sensing and secure communication techniques, when combined with intelligent in-sensor-analytics at the resource-constrained nodes, can potentially pave the way for perpetual, and even batteryless systems for next-generation IoT, IoB and healthcare applications.</p>