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1. On-Body Piezoelectric Energy Harvesters through Innovative Designs and Conformable Structures

   https://doi.org/10.1021/acsbiomaterials.1c00800  

   Fernandez, Sara V. ; Cai, Fiona ; Chen, Sophia ; Suh, Emma ; Tiepelt, Jan ; McIntosh, Rachel ; Marcus, Colin ; Acosta, Daniel ; Mejorado, David ; Dagdeviren, Canan ( October 2021 , ACS Biomaterials Science & Engineering)

   Recent advancements in wearable technology have improved lifestyle and medical practices, enabling personalized care ranging from fitness tracking, to real-time health monitoring, to predictive sensing. Wearable devices serve as an interface between humans and technology; however, this integration is far from seamless. These devices face various limitations such as size, biocompatibility, and battery constraints wherein batteries are bulky, are expensive, and require regular replacement. On-body energy harvesting presents a promising alternative to battery power by utilizing the human body’s continuous generation of energy. This review paper begins with an investigation of contemporary energy harvesting methods, with a deep focus on piezoelectricity. We then highlight the materials, configurations, and structures of such methods for self-powered devices. Here, we propose a novel combination of thin-film composites, kirigami patterns, and auxetic structures to lay the groundwork for an integrated piezoelectric system to monitor and sense. This approach has the potential to maximize energy output by amplifying the piezoelectric effect and manipulating the strain distribution. As a departure from bulky, rigid device design, we explore compositions and microfabrication processes for conformable energy harvesters. We conclude by discussing the limitations of these harvesters and future directions that expand upon current applications for wearable technology. Further exploration of materials, configurations, and structures introduce interdisciplinary applications for such integrated systems. Considering these factors can revolutionize the production and consumption of energy as wearable technology becomes increasingly prevalent in everyday life. 

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2. ARIS: Authentication for Real-Time IoT Systems

   https://doi.org/10.1109/ICC.2019.8761207  

   Behnia, Rouzbeh ; Ozmen, Muslum Ozgur ; Yavuz, Attila A. ( May 2019 , IEEE International Conference on Communications (ICC))

   Efficient authentication is vital for IoT applications with stringent minimum-delay requirements (e.g., energy delivery systems). This requirement becomes even more crucial when the IoT devices are battery-powered, like small aerial drones, and the efficiency of authentication directly translates to more operation time. Although some fast authentication techniques have been proposed, some of them might not fully meet the needs of the emerging delay-aware IoT. In this paper, we propose a new signature scheme called ARIS that pushes the limits of the existing digital signatures, wherein commodity hardware can verify 83,333 signatures per second. ARIS also enables the fastest signature generation along with the lowest energy consumption and end-to-end delay among its counterparts. These significant computational advantages come with a larger storage requirement, which is a favorable trade-off for some critical delay-aware applications. These desirable features are achieved by harnessing message encoding with cover-free families and a special elliptic curve based one-way function. We prove the security of ARIS under the hardness of the elliptic curve discrete logarithm problem in the random oracle model. We provide an open-sourced implementation of ARIS on commodity hardware and an 8-bit AVR microcontroller for public testing and verification. 

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3. A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

   https://doi.org/10.3390/s23042131  

   Biglari, Amin ; Tang, Wei ( February 2023 , Sensors)

   Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness. 

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4. A Ceramic‐Electrolyte Glucose Fuel Cell for Implantable Electronics

   https://doi.org/10.1002/adma.202109075  

   Simons, Philipp ; Schenk, Steven A. ; Gysel, Marco A. ; Olbrich, Lorenz F. ; Rupp, Jennifer L. M. ( May 2022 , Advanced Materials)

   Next‐generation implantable devices such as sensors, drug‐delivery systems, and electroceuticals require efficient, reliable, and highly miniaturized power sources. Existing power sources such as the Li–I₂pacemaker battery exhibit limited scale‐down potential without sacrificing capacity, and therefore, alternatives are needed to power miniaturized implants. This work shows that ceramic electrolytes can be used in potentially implantable glucose fuel cells with unprecedented miniaturization. Specifically, a ceramic glucose fuel cell—based on the proton‐conducting electrolyte ceria—that is composed of a freestanding membrane of thickness below 400 nm and fully integrated into silicon for easy integration into bioelectronics is demonstrated. In contrast to polymeric membranes, all materials used are highly temperature stable, making thermal sterilization for implantation trivial. A peak power density of 43 µW cm⁻², and an unusually high statistical verification of successful fabrication and electrochemical function across 150 devices for open‐circuit voltage and 12 devices for power density, enabled by a specifically designed testing apparatus and protocol, is demonstrated. The findings demonstrate that ceramic‐based micro‐glucose‐fuel‐cells constitute the smallest potentially implantable power sources to date and are viable options to power the next generation of highly miniaturized implantable medical devices.

    

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5. The Future of Clean Computing May Be Dirty

   https://doi.org/10.1145/3568113.3568117  

   Josephson, Colleen ; Shuai, Weitao ; Marcano, Gabriel ; Pannuto, Pat ; Hester, Josiah ; Wells, George ( October 2022 , GetMobile: Mobile Computing and Communications)

   The emergence of the Internet of Things and pervasive sensor networks have generated a surge of research in energy scavenging techniques. We know well that harvesting RF, solar, or kinetic energy enables the creation of battery-free devices that can be used where frequent battery changes or dedicated power lines are impractical. One unusual yet ubiquitous source of power is soil (earth itself) - or more accurately, bacterial communities in soil. Microbial fuel cells (MFCs) are electrochemical cells that harness the activities of microbes that naturally occur in soil, wetlands, and wastewater. MFCs have been a topic of research in environmental engineering and microbiology for decades, but are a relatively new topic in electronics design and research. Most low-power electronics have traditionally opted for batteries, RF energy, or solar cells. This is changing, however, as the limitations and costs of these energy sources hamper our ability to deploy useful systems that last for decades in challenging environments. If large-scale, long-term applications like underground infrastructure monitoring, smart farming, and sensing for conservation are to be possible, we must rethink the energy source. 

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