publications

2025

1. Demo: Hardware to unleash novel energy sources for outdoor sensor networks

   Levy, Alec; Madden, John; Melo, Mirella; Josephson, Colleen

   In Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems 2025

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   We present a demonstration of a wireless sensor network system to measure power output from nontraditional low-power energy sources. Our system has the necessary measurement fidelity for logging power traces and environmental data to a web based measurement portal and facilitates transition from in-lab experiments to the field. Our demonstration shows different use cases of our system by logging various energy sources: a soil-based microbial fuel cell, a prickly pear cactus, and a thermoelectric generator. A large screen will be used to continuously display live data traces.

2. Towards Microbial Fuel Cell Powered Backscatter Tags for Low-Cost In-Ground Soil Moisture Sensing

   Vetha, Eric; Darbonne, Ava; Josephson, Colleen

   In Proceedings of the 13th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems 2025

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   This poster presents Water Radar (WaDAR), a soil moisture sensing system that aims to harness energy from soil microbial fuel cells (SMFCs). Using SMFCs, WaDAR will offer a low-cost, energy-efficient alternative to traditional soil moisture sensors. Lab testing results demonstrate an average soil moisture measurement error of just 2.7%, comparable to more expensive capacitive commercial sensors. This combination of affordability, accuracy and energy efficiency suggests that SMFC energy harvesting could help facilitate the widespread adoption of soil moisture sensors in agriculture, leading to significant water conservation and promoting sustainable farming practices.

3. Greenhouse Sensing using Chirp-Based VLC with a Solar Panel for Energy Harvesting and Data Downlink

   Guo, Yawen; Lin, Jack; Vafadari, Firouz; Morton, Tyler; Obraczka, Katia; Josephson, Colleen

   In Proceedings of the 13th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems 2025

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   This demo presents an energy-harvesting IoT system for greenhouses, with a particular focus on showcasing the communication downlink. The downlink is a chirp-based downlink Visible Light Communication (VLC) system that is deployed in and especially designed for the noise environment in the greenhouse. Our chirp-based VLC enhances noise immunity and reduces error rates, making it well-suited for robust VLC communication. We demonstrate that our VLC system operates reliably in high-noise environment where abundant sunlight and various lighting sources are present. Additionally, we show that the communication module can operate using energy-harvesting modules, enabling a sustainable and self-powered system.

4. Performance Analysis and Resource Allocation for Intelligent Solar Aware Cellular Base Stations

   Naderi, Sonia; Guo, Yawen; Josephson, Colleen

   IEEE Transactions on Green Communications and Networking 2025

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5. Time-series forecasting of microbial fuel cell energy generation using deep learning

   Hess-Dunlop, Adam; Kakani, Harshitha; Taylor, Stephen; Louie, Dylan; Eshraghian, Jason; Josephson, Colleen

   Frontiers in Computer Science 2025

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   Soil microbial fuel cells (SMFCs) are an emerging technology which offer clean and renewable energy in environments where more traditional power sources, such as chemical batteries or solar, are not suitable. With further development, SMFCs show great promise for use in robust and affordable outdoor sensor networks, particularly for farmers. One of the greatest challenges in the development of this technology is understanding and predicting the fluctuations of SMFC energy generation, as the electro-generative process is not yet fully understood. Very little work currently exists attempting to model and predict the relationship between soil conditions and SMFC energy generation, and we are the first to use machine learning to do so. In this paper, we train Long Short Term Memory (LSTM) models to predict the future energy generation of SMFCs across timescales ranging from 3 min to 1 h, with results ranging from 2.33 to 5.71% Mean Average Percent Error (MAPE) for median voltage prediction. For each timescale, we use quantile regression to obtain point estimates and to establish bounds on the uncertainty of these estimates. When comparing the median predicted vs. actual values for the total energy generated during the testing period, the magnitude of prediction errors ranged from 2.29 to 16.05%. To demonstrate the real-world utility of this research, we also simulate how the models could be used in an automated environment where SMFC-powered devices shut down and activate intermittently to preserve charge, with promising initial results. Our deep learning-based prediction and simulation framework would allow a fully automated SMFC-powered device to achieve a median 100+% increase in successful operations, compared to a naive model that schedules operations based on the average voltage generated in the past.

2024

1. Towards Deep Learning for Predicting Microbial Fuel Cell Energy Output

   Hess-Dunlop, Adam; Kakani, Harshitha; Josephson, Colleen

   In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies 2024

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   Soil microbial fuel cells (SMFCs) are an emerging technology which offer clean and renewable energy in environments where more traditional power sources, such as chemical batteries or solar, are not suitable. With further development, SMFCs show great promise for use in robust and affordable outdoor sensor networks, particularly for farmers. One of the greatest challenges in the development of this technology is understanding and predicting the fluctuations of SMFC energy generation, as the electro-generative process is not yet fully understood. Very little work currently exists attempting to model and predict the relationship between soil conditions and SMFC energy generation, and we are the first to use machine learning to do so. In this paper, we train Long Short Term Memory (LSTM) models to predict the future energy generation of SMFCs across timescales ranging from 3 minutes to 1 hour, with results ranging from 2.33% to 5.71% MAPE for median voltage prediction. For each timescale, we use quantile regression to obtain point estimates and to establish bounds on the uncertainty of these estimates. When comparing the median predicted vs. actual values for the total energy generated during the testing period, the magnitude of prediction errors ranged from 2.29% to 16.05%. To demonstrate the real-world utility of this research, we also simulate how the models could be used in an automated environment where SMFC-powered devices shut down and activate intermittently to preserve charge, with promising initial results. Our deep learning-based prediction and simulation framework would allow a fully automated SMFC-powered device to achieve a median 100+% increase in successful operations, compared to a naive model that schedules operations based on the average voltage generated in the past.

2. Is it time to start moving soil microbial fuel cell research out of the lab and into the field?

   Taylor, Stephen; Jaliff, Laura; Wells, George; Josephson, Colleen

   Science of The Total Environment 2024

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   Soil microbial fuel cells (SMFCs) function as bioelectrochemical energy harvesters that convert electrons stored in soil organic matter into useful electrical energy. Broadly, an SMFC comprises three essential components: an anode buried in the soil (the negative terminal), a colony of exoelectrogenic microorganisms residing on this anode, and a cathode (the positive terminal). As the exoelectrogens respire, they release electrons to the anode, which acts as an external receptor. These released electrons then flow through a load (e.g. a resistor), connecting the anode and cathode. Though minuscule, the electrical power produced by SMFCs has a number of potential applications such as sustaining low-power embedded electronics, pollutant remediation, or as a bio-sensing proxy for soil qualities and microbial activity. This discussion aims to emphasize the potential of SMFCs in addressing real-world environmental issues and to generate interest in the larger scientific community for broad interdisciplinary research efforts, particularly in field deployments.

3. NeRF-accelerated ecological monitoring in mixed-evergreen Redwood forest

   Korycki, Adam; Yeaton, Cory; Gilbert, Gregory S; Josephson, Colleen; McGuire, Steve

   Oct 2024

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   Forest mapping provides critical observational data needed to understand the dynamics of forest environwments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide sequestration. Manual methods of forest mapping are labor intensive and time consuming, a bottleneck for large-scale mapping efforts. Automated mapping relies on acquiring dense forest reconstructions, typically in the form of point clouds. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) generate point clouds using expensive LiDAR sensing, and have been used successfully to estimate tree diameter. Neural radiance fields (NeRFs) are an emergent technology enabling photorealistic, vision-based reconstruction by training a neural network on a sparse set of input views. In this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. In addition, we propose an improved DBH-estimation method using convex-hull modeling. Using this approach, we achieved 1.68 cm RMSE, which consistently outperformed standard cylinder modeling approaches. Our code contributions and forest datasets are freely available at https://github.com/harelab-ucsc/RedwoodNeRF.

2023

1. Soil-Powered Computing: The Engineer’s Guide to Practical Soil Microbial Fuel Cell Design

   Yen, Bill; Jaliff, Laura; Gutierrez, Louis; Sahinidis, Philothei; Bernstein, Sadie; Madden, John; Taylor, Stephen; Josephson, Colleen; Pannuto, Pat; Shuai, Weitao; Wells, George; Arora, Nivedita; Hester, Josiah

   In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Dec 2023

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   Human-caused climate degradation and the explosion of electronic waste have pushed the computing community to explore fundamental alternatives to the current battery-powered, over-provisioned ubiquitous computing devices that need constant replacement and recharging. Soil Microbial Fuel Cells (SMFCs) offer promise as a renewable energy source that is biocompatible and viable in difficult environments where traditional batteries and solar panels fall short. However, SMFC development is in its infancy, and challenges like robustness to environmental factors and low power output stymie efforts to implement real-world applications in terrestrial environments. This work details a 2-year iterative process that uncovers barriers to practical SMFC design for powering electronics, which we address through a mechanistic understanding of SMFC theory from the literature. We present nine months of deployment data gathered from four SMFC experiments exploring cell geometries, resulting in an improved SMFC that generates power across a wider soil moisture range. From these experiments, we extracted key lessons and a testing framework, assessed SMFC’s field performance, contextualized improvements with emerging and existing computing systems, and demonstrated the improved SMFC powering a wireless sensor for soil moisture and touch sensing. We contribute our data, methodology, and designs to establish the foundation for a sustainable, soil-powered future.

2022

1. Early Characterization of Soil Microbial Fuel Cells

   Marcano, Gabriel; Josephson, Colleen; Pannuto, Pat

   In IEEE International Symposium on Circuits and Systems (ISCAS) Special Session on Smart Agriculture May 2022

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   This paper discusses experiments on soil-based microbial fuel cells (MFCs) as energy scavenging sources. We explain the mechanism of operation for MFCs, perform controlled laboratory experiments of MFCs, and deploy a small-scale in-situ pilot in an active farm. We find that traditional energy harvester ICs draw power too aggressively, which reduces overall energy capture. We show that isolated MFCs can be combined in series or parallel to improve the voltage or current output of the harvesting source. Lastly, we observe that under a real-world, drip-irrigated agricultural setting, MFC output is appreciably lower, but consistent at 0.5-2 microwatts.

2. The Future of Clean Computing May Be Dirty

   Josephson, Colleen; Shuai, Weitao; Marcano, Gabriel; Pannuto, Pat; Hester, Josiah; Wells, George

   GetMobile: Mobile Comp. and Comm. Oct 2022

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   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.

3. Hardware to enable large-scale deployment and observation of soil microbial fuel cells

   Madden, John; Marcano, Gabriel; Taylor, Stephen; Pannuto, Pat; Josephson, Colleen

   In Proceedings of the Tenth ACM International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems Nov 2022

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   Soil microbial fuel cells are a promising source of energy for outdoor sensor networks. These biological systems are sensitive to environ- mental conditions, therefore more data is needed on their behavior “in the wild” to enable the creation of an energy system capable of being widely deployed. Prior work on early characterization of microbial fuel cells relied on extremely accurate, but expensive, logging hardware. To scale up the number of deployment sites, we present custom logging hardware, specially designed to accurately monitor the behavior of microbial fuel cells at low cost. This paper describes the design and evaluation of the board, which is open source and freely available on GitHub.

2021

1. Low-Cost In-Ground Soil Moisture Sensing with Radar Backscatter Tags

   Josephson, Colleen; Kotaru, Manikanta; Winstein, Keith; Katti, Sachin; Chandra, Ranveer

   In ACM SIGCAS Conference on Computing and Sustainable Societies Nov 2021

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   Despite decades of research confirming the benefits, most farms do not incorporate soil moisture sensing into their irrigation practices. Soil moisture sensing can be broken into two broad approaches, both of which have drawbacks. In situ sensors are installed in the ground, tend to be difficult to deploy and maintain, and have high costs. Remote-sensing based approaches use radars to infer soil moisture from surface reflection properties. While completely wireless, remote sensing suffers from lower resolution and accuracy compared to in situ sensing. We propose a hybrid approach that combines the advantages of both. This paper introduces the idea of using inexpensive in situ backscatter tags with above-ground radars, which enables completely wireless soil moisture measurements with high-accuracy and high-resolution. Our key idea is introducing a simple, power efficient modulation scheme that enables commodity radars to easily detect and range the underground tag. We have benchmarked our approach against oven-based, industry-standard ground-truth measurements and demonstrated that, at a realistic depth and across several types of soil, we achieve a 90th percentile error of 3.4%, which is the same accuracy as state-of-the-art in situ sensors. We also demonstrate that our approach works with similar accuracy at a real farm.

2020

1. Time-of-Flight Soil Moisture Estimation Using RF Backscatter Tags

   Josephson, Colleen; Barnhart, Bradley; Winstein, Keith; Katti, Sachin; Chandra, Ranveer

   In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Sep 2020

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   Agricultural soil moisture measurement is usually done with extensive in situ sensor deployments. These sensor networks are often difficult to install and maintain. Ground penetrating radars have also been used to do fine-grained moisture measurements in farm fields, but usually require the radar to be in or near contact with the soil. In this paper we propose a hybrid approach that combines an in situ backscatter reflector with an ultra-wideband radar that is small enough to be handheld or mounted to a drone. The underground backscatter reflector allows for accurate time-of-flight measurements. The time-of-flight is determined by the permittivity of the soil, which is influenced primarily by the soil water content for non-saline soils. We performed both laboratory and in situ measurements, achieving an average accuracy within 0.013 cm³/ cm³ of the ground truth with a 90th percentile of 0.034 cm³/cm³. This demonstrates the feasibility of our approach.

2. Farming Electrons: Galvanic Versus Microbial Energy in Soil Batteries

   Josephson, Colleen; Jackson, Neal; Pannuto, Pat

   IEEE Sensors Letters Dec 2020

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   Recent work shows the exciting potential for soil microbes as a renewable energy harvesting source. However, the choice of materials in microbial fuel cells (MFCs) significantly impacts where the energy comes from. MFCs with metallic anodes draw energy from both renewable bacterial activity and nonrenewable galvanic corrosion of cell components. Previous studies do not analyze these two power sources separately. This letter clarifies the behavior of metallic MFCs by characterizing galvanic activity separately from biological activity. We find that the majority of energy attained from prior designs is most likely galvanic, not bacterial, and as a consequence is nonrenewable.

3. Demo Abstract: RF Soil Moisture Sensing via Radar Backscatter Tags

   Josephson, Colleen; Barnhart, Bradley; Katti, Sachin; Winstein, Keith; Chandra, Ranveer

   In 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Apr 2020

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   We present a sensing system that determines soil moisture via RF using backscatter tags paired with a commodity ultra-wideband RF transceiver. Despite decades of research confirming the benefits, soil moisture sensors are still not widely adopted on working farms for three key reasons: the high cost of sensors, the difficulty of deploying and maintaining these sensors, and the lack of reliable internet access in rural areas. We seek to address some of these obstacles by designing a low-cost soil moisture sensing system that uses a hybrid approach of pairing completely wireless backscatter tags with a mobile reader.(p)(/p)We designed and built a backscatter tag prototype and tested our system both in laboratory and in situ at an organic farm field. Our backscatter tag has a projected battery lifetime of up to 15 years on 4xAA batteries, and can operate at a depth of at least 30cm and up to 75cm. It achieves an average accuracy within 0.01-0.03cm³/cm³ of the ground truth with a 90th percentile of 0.034cm³/cm³, which is comparable to state-of-the-art commercial soil sensors, at an order of magnitude lower cost.

4. Exploring low-power ubiquitous sensing using RF backscatter

   Apr 2020

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2019

1. Wireless Computer Vision Using Commodity Radios

   Josephson, Colleen; Yang, Lei; Zhang, Pengyu; Katti, Sachin

   In 2019 18th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Apr 2019

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   We introduce the design and implementation of BackCam, a low-power wireless camera sensor platform that supports continuous realtime vision applications, all using commodity radios. In the lowest power mode, our camera board consumes only 9.7mW and continuously transmits images for over one month on two AA batteries. We introduce a novel power management system that incorporates input from the camera itself to increase battery life up to 62%. Using images and system metadata as input, we designed a feedback system between the sensor and the gateway. This allows dynamic vision application requirements to be met while consuming as little power as possible. For example, our system can temporarily increase the resolution after an object of interest is detected, then reduce it again after it has disappeared. This increases the accuracy of simplistic facial recognition by at least 25% compared to operating constantly in the lowest power mode. We implement communications using a full-duplex WiFi backscatter radio, ensuring compatibility with commodity WiFi devices. We also designed an efficient data streaming and compression pipeline straight from the camera to the backscatter transmitter, allowing us to minimize latency and avoid expensive memory writes. We deployed BackCam in a real office environment, and as a proof-of-concept, implemented basic realtime face detection and recognition.

2. RF Soil Moisture Sensing via Radar Backscatter Tags

   Josephson, Colleen; Barnhart, Bradley; Katti, Sachin; Winstein, Keith; Chandra, Ranveer

   Apr 2019

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2017

1. FreeRider: Backscatter Communication Using Commodity Radios

   Zhang, Pengyu; Josephson, Colleen; Bharadia, Dinesh; Katti, Sachin

   In Proceedings of the 13th International Conference on Emerging Networking EXperiments and Technologies Apr 2017

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   We introduce the design and implementation of FreeRider, the first system that enables backscatter communication with multiple commodity radios, such as 802.11g/n WiFi, ZigBee, and Bluetooth, while these radios are simultaneously used for productive data communication. Furthermore, we are, to our knowledge, the first to implement and evaluate a multi-tag system. The key technique used by FreeRider is codeword translation, where a tag can transform a codeword present in the original excitation signal into another valid codeword from the same codebook during backscattering. In other words, the backscattered signal is still a valid WiFi, ZigBee, or Bluetooth signal. Therefore, commodity radios decode the backscattered signal and extract the tag’s embedded information. More importantly, FreeRider does codeword translation regardless of the data transmitted by these radios. Therefore, these radios can still do productive data communication. FreeRider accomplishes codeword translation by modifying one or more of the three dimensions of a wireless signal — amplitude, phase and frequency. A tag ensures that the modified signal is still comprised of valid codewords that come the same codebook as the original excitation signal. We built a hardware prototype of FreeRider, and our empirical evaluations show a data rate of  60kbps in single tag mode, 15kbps in multi-tag mode, and a backscatter communication distance up to 42m when operating on 802.11g/n WiFi.