Abstract:
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.
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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.
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Data centers consume roughly 1–2% of the world’s electricity, with the majority of it attributed to compute, making the computing industry a substantial source of greenhouse gas emissions. Resources in data centers typically focus on providing high performance and availability,but the question of sustainability in managing these distributed resources often goes unnoticed over these other metrics. This problem will only exacerbate as the data center computing demand continues to increase.
In this paper, we address the sustainability aspect of load balancing in VMware’s Avi Global Server Load Balancer (GSLB). Our GSLB deployment spans data centers across geographies and clouds and relies on geographical proximity to shift client application requests to the closest data center. In this work, we enhance the GSLB service to additionally consider the real-time carbon intensity at each data center as a factor in making a load-balancing choice. Our carbon-aware prototype shows an average of 21% and a maximum of 51% reduction in carbon emissions while operating with an acceptable latency 51% reduction in carbon emissions while operating with an acceptable latency.
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The Information and Communications Technology (ICT) industry emits as much carbon as the aviation industry, and if we continue business as usual our share of emissions may grow manyfold in the coming decade. At the same time, more and more businesses are making commitments to be zero carbon or carbon neutral by 2050 or sooner. The path to zero carbon ICT has four key pillars: prioritizing renewable energy, using resources like power and water more efficiently, addressing embodied carbon, and removing institutional barriers. In this paper we discuss from an industry perspective the challenges and opportunities within each pillar, as well as the role ICT can play in helping other industries achieve zero carbon goals.
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Soil microbial fuel cells are a promising source of energy for outdoor sensor networks. These biological systems are sensitive to environmental 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.
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Abstract: Globally, renewable energy (solar, wind) generation has grown rapidly over the last decade. Renewable energy has very low carbon intensity and a zero marginal operating cost, and so is a critical component to achieving zero carbon computing and should not be wasted. However, pervasive imbalances in renewable generation and the transmission capability limitations that prevent the grid from transporting energy to where it is needed causes generation to be temporarily curtailed and wasted. Renewable energy curtailment is increasing rapidly in the U.S. and globally. Potential solutions include co-located battery storage and construction of more transmission capacity. But these approaches do not yet scale due to cost, and traditional loads cannot be physically moved. On the other hand, many computation workloads (such as some learning or big data) can be flexible in time (scheduled for delayed execution) and space (transferred across any geographical distance with limited cost). This opens the possibility of shifting workloads in time and space to take advantage in real time of any amount of excess renewable energy, which otherwise would be curtailed and wasted. Initial results show that a single datacenter that time shifts load can reduce its emissions by 19% or more annually. Although conceptually simple, such workload shifting presents significant challenges. This talk will provide an overview of VMware’s approach to sustainability innovation, and a discussion of some of our ongoing work in shifting datacenter workloads in time and geography.
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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 insitu 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.
]]>This event is co-chaired by me, Zerina Kapetanovic (UW) and Vaishnavi Ranganathan (MSR). This lovely workshop is the first workshop in the area of IoT/power harvesting to my awareness that has been chaired by a committee of all women, hooray for DEI progress!
ALSO: I recently chaired the (virtual) ‘Feeding the Next Billion’ session at ISCAS ‘22. We had 5 great talks:
1.) Optimizing a Multispectral-Images-Based DL Model, Through Feature Selection, Pruning and Quantization by Julio Torres-Tello and Seokbum Ko at the University of Saskatchewan
2.) Scheduling Problems for Robotics in Precision Ag. by Stefano Carpin at UC Merced
3.) A Thermoacoustic Imaging System for Non-Invasive and Non-Destructive Root Phenotyping by A. Singhvi, A. Fitzpatrick, J.D. Scharwies, J. Dinneny, A. Arbabian at Stanford
4.) Early Characterization of Soil Microbial Fuel Cells by G. Marcano, C. Josephson, Pat Pannuto and Gabe Marcano at UC San Diego
and
5.) A Bio-Mimetic Leaf Wetness Sensor by B. Nguyen, G. Gilbert, M. Rolandi at UC Santa Cruz
Session details here.
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