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1. PACT: Scalable, Long-Range Communication for Monitoring and Tracking Systems Using Battery-less Tags

   https://doi.org/10.1145/3569471  

   Sangar, Yaman; Biradavolu, Yoganand; Pederson, Kai; Ranganathan, Vaishnavi; Krishnaswamy, Bhuvana (December 2022, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   The food and drug industry is facing the need to monitor the quality and safety of their products. This has made them turn to low-cost solutions that can enable smart sensing and tracking without adding much overhead. One such popular low-power solution is backscatter-based sensing and communication system. While it offers the promise of battery-less tags, it does so at the cost of a reduced communication range. In this work, we propose PACT - a scalable communication system that leverages the knowledge asymmetry in the network to improve the communication range of the tags. Borrowing from the backscatter principles, we design custom PACT Tags that are battery-less but use an active radio to extend the communication range beyond standard passive tags. They operate using the energy harvested from the PACT Source. A wide-band Reader is used to receive multiple Tag responses concurrently and upload them to a cloud server, enabling real-time monitoring and tracking at a longer range. We identify and address the challenges in the practical design of battery-less PACT Tags using an active radio and prototype them using off-the-shelf components. We show experimentally that our Tag consumes only 23μJ energy, which is harvested from an excitation Source that is up to 24 meters away from the Tag. We show that in outdoor deployments, the responses from an estimated 520 Tags can be received by a Reader concurrently while being 400 meters away from the Tags. 

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2. A 65nm Resonant Electro-Quasistatic 5-240uW Human Whole-Body Powering and 2.19uW Communication SoC with Automatic Maximum Resonant Power Tracking

   https://doi.org/10.1109/CICC51472.2021.9431456  

   Modak, Nirmoy; Das, Debayan; Nath, Mayukh; Chatterjee, Baibhab; Gaurav Kumar, K; Maity, Shovan; Sen, Shreyas (April 2021, 2021 IEEE Custom Integrated Circuits Conference (CICC))

   Applications like Connected Healthcare through physiological signal monitoring and Secure Authentication using wearable keys can benefit greatly from battery-less operation. Low power communication along with energy harvesting is critical to sustain such perpetual battery-less operation. Previous studies have used techniques such as Tribo-Electric, Piezo-Electric, RF energy harvesting for Body Area Network devices, but they are restricted to on-body node placements. Human body channel is known to be a promising alternative to wireless radio wave communication for low power operation [1-4], through Human Body Communication, as well as very recently as a medium for power transfer through body coupled power transfer [5]. However, channel length (L) dependency of the received power makes it inefficient for L>40cm. There have also been a few studies for low power communication through the human body, but none of them could provide sustainable battery-less operation. In this paper, we utilize Resonant Electro Quasi-Static Human Body Communication (Res-EQS HBC) with Maximum Resonance Power Tracking (MRPT) to enable channel length independent whole-body communication and powering (Fig.1). We design the first system to simultaneously transfer Power and Data between a HUB device and a wearable through the human body to enable battery-less operation. Measurement results show 240uW, 28uW and 5uW power transfer through the body in a MachineMachine (large devices with strong ground connection) Tabletop (small devices kept on a table, as in [5]) and Wearable-Wearable (small form factor battery operated devices) scenario respectively, independent of body channel length, while enabling communication with a power consumption of only 2.19uW. This enables >25x more power transfer with >100x more efficiency compared to [5] for Tabletop and 100cm Body distance by utilizing the benefits of EQS. The MRPT loop automatically tracks device and posture dependent resonance point changes to maximize power transfer in all cases. 

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3. LiTEfoot: Ultra-low-power Localization using Ambient Cellular Signals

   https://doi.org/10.1145/3666025.3699356  

   Garg, Nakul; Ghosh, Aritrik; Roy, Nirupam (November 2024, ACM)

   In this paper, we introduce a low-power wide-area cellular localization system, called LiTEfoot. The core architecture of the radio carefully applies non-linear transform of the entire cellular spectrum to obtain a systematic superimposition of the synchronization signals at the baseband. The system develops methods to simultaneously identify all the base stations that are active at any cellular band from the transformed signal. The radio front end uses a simple envelop detector to realize the non-linear transformation. We build on this low-power radio to implement a self-localization system leveraging ambient 4G-LTE signals. We show that the core system can also be extended to other cellular technologies like 5G-NR and NB-IoT. The prototype achieves a median localization error of 22 meters in urban areas and 50 meters in rural areas. It can sense a 3GHz wideband LTE spectrum in 10ms using non-linear intermodulation while consuming 0.9 mJ of energy for a PCB-based implementation and 40 𝜇J for CMOS simulation. In other words, LiTEfoot tags can last for 11 years on a coin cell while continuously estimating location every 5 seconds. We believe that LiTEfoot will have widespread implications in city-scale asset tracking and other location-based services. The radio architecture can be useful beyond low-power self-localization and can find application in synchronization and communication on battery-less platforms. 

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4. tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices

   https://doi.org/10.48550/arXiv.2202.09297  

   Basaklar, Toygun; Tuncel, Yigit; Ogras, Umit Y. (March 2022, TinyML Symposium)

   Advances in low-power electronics and machine learning techniques lead to many novel wearable IoT devices. These devices have limited battery capacity and computational power. Thus, energy harvesting from ambient sources is a promising solution to power these low-energy wearable devices. They need to manage the harvested energy optimally to achieve energy-neutral operation, which eliminates recharging requirements. Optimal energy management is a challenging task due to the dynamic nature of the harvested energy and the battery energy constraints of the target device. To address this challenge, we present a reinforcement learning-based energy management framework, tinyMAN, for resource-constrained wearable IoT devices. The framework maximizes the utilization of the target device under dynamic energy harvesting patterns and battery constraints. Moreover, tinyMAN does not rely on forecasts of the harvested energy which makes it a prediction-free approach. We deployed tinyMAN on a wearable device prototype using TensorFlow Lite for Micro thanks to its small memory footprint of less than 100 KB. Our evaluations show that tinyMAN achieves less than 2.36 ms and 27.75 μJ while maintaining up to 45% higher utility compared to prior approaches. 

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5. Battery-Free Wireless Video Streaming Camera System

   https://doi.org/10.1109/RFID.2019.8719264  

   Saffari, Ali; Hessar, Mehrdad; Naderiparizi, Saman; Smith, Joshua R. (April 2019, 2019 IEEE International Conference on RFID (RFID))

   We design and prototype the first battery-free video streaming camera that harvests energy from both ambient light and RF signals. The RF signals are emitted by a nearby access point. The camera collects energy from both sources and backscatters up to 13 frames per second (fps) video at a distance of up to 150 ft in both outdoor and indoor environments. Compared to a single harvester powered by either ambient light or RF, our dual harvester design improves the camera's frame rate. Also, the dual harvester design maintains a steady 3 fps at distances beyond the RF energy harvesting range. To show efficacy of our battery-free video streaming camera for real applications such as surveillance and monitoring, we deploy our camera for a day-long experiment, from 8 AM to 4 PM, in an outdoor environment. Our results show that on a sunny day, our camera can provide a frame rate of up to 9 fps using a 4.5 cm×2.2 cm solar cell. 

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