Investigating the effects of climate change and global warming caused by GHG emissions have been a key concern worldwide. These emissions are largely contributed to by the production, use and disposal of consumer products. Thus, it is important to build tools to estimate the environmental impact of consumer goods, an essential part of which is conducting Life Cycle Assessments (LCAs). LCAs specify and account for the appropriate processes involved with the production, use, and disposal of the products. We present SpiderGen, an LLM-based workflow which integrates the taxonomy and methodology of traditional LCA with the reasoning capabilities and world knowledge of LLMs to generate graphical representations of the key procedural information used for LCA, known as Product Category Rules Process Flow Graphs (PCR PFGs). We additionally evaluate the output of SpiderGen by comparing it with 65 real-world LCA documents. We find that SpiderGen provides accurate LCA process information that is either fully correct or has minor errors, achieving an F1-Score of 65% across 10 sample data points, as compared to 53% using a one-shot prompting method. We observe that the remaining errors occur primarily due to differences in detail between LCA documents, as well as differences in the ``scope" of which auxiliary processes must also be included. We also demonstrate that SpiderGen performs better than several baselines techniques, such as chain-of-thought prompting and one-shot prompting. Finally, we highlight SpiderGen's potential to reduce the human effort and costs for estimating carbon impact, as it is able to produce LCA process information for less than $1 USD in under 10 minutes as compared to the status quo LCA, which can cost over $25000 USD and take up to 21-person days.

Cold start delays are a main pain point for today's FaaS (Function-as-a-Service) platforms. A widely used mitigation strategy is keeping recently invoked function containers alive in memory to enable warm starts with minimal overhead. This paper identifies new challenges that state-of-the-art FaaS keep-alive policies neglect. These challenges are caused by concurrent function invocations, a common FaaS workload behavior. First, concurrent requests present a tradeoff between reusing busy containers (delayed warm starts) versus cold-starting containers. Second, concurrent requests cause imbalanced evictions of containers that will be reused shortly thereafter. To tackle the challenges, we propose a novel serverless function container orchestration algorithm called CIDRE. CIDRE makes informed decisions to speculatively choose between a delayed warm start and a cold start under concurrency-driven function scaling. CIDRE uses both fine-grained container-level and coarse-grained concurrency information to make balanced eviction decisions. We evaluate CIDRE extensively using two production FaaS workloads. Results show that CIDRE reduces the cold start ratio and the average invocation overhead by up to 75.1% and 39.3% compared to state-of-the-art function keep-alive policies.

State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy. By integrating Kalman filtering, optimization, and learning-based modalities, we propose a hybrid solution that combines proprioception and exteroceptive information for estimating the state of the robot’s trunk. Leveraging joint encoder and IMU measurements, our Kalman filter is enhanced through a single-rigid body model that incorporates ground reaction force control outputs from convex Model Predictive Control optimization. The estimation is further refined through Gated Recurrent Units, which also considers semantic insights and robot height from a Vision Transformer autoencoder applied on depth images. This framework not only furnishes accurate robot state estimates, including uncertainty evaluations, but can minimize the nonlinear errors that arise from sensor measurements and model simplifications through learning. The proposed methodology is evaluated in hardware using a quadruped robot on various terrains, yielding a 65% improvement on the Root Mean Squared Error compared to our VIO SLAM baseline. Code example: https://github.com/AlexS28/OptiState

Accurate fuel condition assessment is crucial for predicting fire behavior, enhancing operational decision support, and improving overall fire management. Our approach utilizes diverse data sources, such as Landsat-8 optical imagery, Sentinel-1 (C-band) SAR imagery, PALSAR (L-band) SAR imagery, and terrain features, to estimate time-lag fuel loadings (1 hour, 10 hours, and 100 hours). Optical data mainly captures the characteristics of leaf and forest canopy, while SAR data is more sensitive to forest vertical structures due to its strong penetrability. An ensemble model was trained on the Forest Inventory and Analysis (FIA) plots and spectral indices. Followed by, feature importance analysis and the inclusion of polynomial features were undertaken. The ensemble strategy, involving neural networks, decision trees, gradient boosting, and ensemble methods, achieved R2values of 0.72, 0.70, and 0.60 for 1-hour, 10-hour, and 100-hour fuel loads. Extensive experimentation in the 2021 Dixie Fire incident validates the effectiveness of our approach, emphasizing the value of leveraging multimodal data and ensemble machine learning models for real-time fuel load estimation.

Products contribute to carbon emissions in each phase of their life cycle, from manufacturing to disposal. Estimating the embodied carbon in products is a key step towards understanding their impact, and undertaking mitigation actions. Precise carbon attribution is challenging at scale, requiring both domain expertise and granular supply chain data. As a first-order approximation, standard reports use Economic Input-Output based Life Cycle Assessment (EIO-LCA) which estimates carbon emissions per dollar at an industry sector level using transactions between different parts of the economy. EIO-LCA models map products to an industry sector, and uses the corresponding carbon per dollar estimates to calculate the embodied carbon footprint of a product. An LCA expert needs to map each product to one of upwards of 1000 potential industry sectors. To reduce the annotation burden, the standard practice is to group products by categories, and map categories to their corresponding industry sector. We present CaML, an algorithm to automate EIO-LCA using semantic text similarity matching by leveraging the text descriptions of the product and the industry sector. CaML uses a pre-trained sentence transformer model to rank the top-5 matches, and asks a human to check if any of them are a good match. We annotated 40K products with non-experts. Our results reveal that pre-defined product categories are heterogeneous with respect to EIO-LCA industry sectors, and lead to a large mean absolute percentage error (MAPE) of 51% in kgCO2e/$. CaML outperforms the previous manually intensive method, yielding a MAPE of 22% with no domain labels (zero-shot). We compared annotations of a small sample of 210 products with LCA experts, and find that CaML accuracy is comparable to that of annotations by non-experts.

Adaptive instruction for online education can increase learning gains and decrease the work required of learners, instructors, and course designers. Reinforcement Learning (RL) is a promising tool for developing instructional policies, as RL models can learn complex relationships between course activities, learner actions, and educational outcomes. This paper demonstrates the first RL model to schedule educational activities in real time for a large online course through active learning. Our model learns to assign a sequence of course activities while maximizing learning gains and minimizing the number of items assigned. Using a controlled experiment with over 1,000 learners, we investigate how this scheduling policy affects learning gains, dropout rates, and qualitative learner feedback. We show that our model produces better learning gains using fewer educational activities than a linear assignment condition, and produces similar learning gains to a self-directed condition using fewer educational activities and with lower dropout rates.

DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems. Using the platform, we demonstrate how a 1/18th scale car can learn to drive autonomously using RL with a monocular camera. It is trained in simulation with no additional tuning in the physical world and demonstrates: 1) formulation and solution of a robust reinforcement learning algorithm, 2) narrowing the reality gap through joint perception and dynamics, 3) distributed on-demand compute architecture for training optimal policies, and 4) a robust evaluation method to identify when to stop training. It is the first successful large-scale deployment of deep reinforcement learning on a robotic control agent that uses only raw camera images as observations and a model-free learning method to perform robust path planning. We open source our code and video demo on GitHub2.

Batteryless sensors avoid battery replacement at the cost of slowing down or stopping their operations when there is not sufficient energy to harvest in the environment. While this strategy can work for some applications, event-based applications still remain a challenge as events arrive sporadically and energy availability is uncertain. One solution is to only turn On a sensor right before an event is happening to both detect the event and save as much energy as possible. Therefore, the system has to correctly predict events while managing limited resource availability. In this demo, we present Ember, an energy management system based on deep reinforcement learning to duty cycle event-driven sensors in low-energy conditions. We show how our system learns environmental patterns over time and makes decisions to maximize the event detection rate for batteryless energy-harvesting sensor nodes subject to low energy availability. Furthermore, we show a novel self-supervised data collection algorithm that helps Ember in discovering new environmental patterns over time. For more details, we refer readers to the full paper of Ember [2].

Energy management can extend the lifetime of batteryless, energy-harvesting systems by judiciously utilizing the energy available. Duty cycling of such systems is especially challenging for event detection, as events arrive sporadically and energy availability is uncertain. If the node sleeps too much, it may miss important events; if it depletes energy too quickly, it will stop operating in low energy conditions and miss events. Thus, accurate event prediction is important in making this tradeoff. We propose Ember, an energy management system based on deep reinforcement learning to duty cycle event-driven sensors in low energy conditions. We train a policy using historical real-world data traces of motion, temperature, humidity, pressure, and light events. The resulting policy can learn to capture up to 95% of the events without depleting the node. Without historical data for training when deploying a node at a new location, we propose a self-supervised mechanism to collect ground-truth data while learning from the data at the same time. Ember learns to capture the majority of events within a week without any historical data and matches the performance of the policies trained with historical data in a few weeks. We deployed 40 nodes running Ember for indoor sensing and demonstrate that the learned policies generalize to real-world settings as well as outperform state-of-the-art techniques.

Many modern smart building applications are supported by wireless sensors to sense physical parameters, given the flexibility they offer and the reduced cost of deployment. However, most wireless sensors are powered by batteries today, and large deployments are inhibited by the requirement of periodic battery replacement. Energy harvesting sensors provide an attractive alternative, but they need to provide adequate quality of service to applications given the uncertainty of energy availability. We propose ACES, which uses reinforcement learning to maximize sensing quality of energy harvesting sensors for periodic and event-driven indoor sensing with available energy. Our custom-built sensor platform uses a supercapacitor to store energy and Bluetooth Low Energy to relay sensors data. Using simulations and real deployments, we use the data collected to continually adapt the sensing of each node to changing environmental patterns and transfer learning to reduce the training time in real deployments. In our 60-node deployment lasting 2 weeks, nodes stop operations only 0.1% of the time, and collection of data is comparable with current battery-powered nodes. We show that ACES reduces the node duty-cycle period by an average of 33% compared to three prior reinforcement learning techniques while continuously learning environmental changes over time.

Modern Building Management Systems (BMSs) have been designed to automate the behavior of complex buildings, but unfortunately they do not allow occupants to customize it according to their preferences, and only the facility manager is in charge of setting the building policies. To overcome this limitation, we present BuildingRules, a trigger-action programming-based system that aims to provide occupants of commercial buildings with the possibility of specifying the characteristics of their office environment through an intuitive interface. Trigger-action programming is intuitive to use and has been shown to be effective in meeting user requirements in home environments. To extend this intuitive interface to commercial buildings, an essential step is to manage the system scalability as large number of users will express their policies. BuildingRules has been designed to scale well for large commercial buildings as it automatically detects conflicts that occur among user specified policies and it supports intelligent grouping of rules to simplify the policies across large numbers of rooms. We ensure the conflict resolution is fast for a fluid user experience by using the Z3 SMT solver. BuildingRules backend is based on RESTful web services so it can connect to various BMSs and scale well with large number of buildings. We have tested our system with 23 users across 17 days in a virtual office building, and the results we have collected prove the effectiveness and the scalability of BuildingRules.

This demo abstract presents PrOLoc, a localization system that combines partially homomorphic encryption with a new way of structuring the localization problem to enable efficient and accurate computation of a target's location while preserving the privacy of the observers.

Today large amount of data is generated by cities. Many of the datasets are openly available and are contributed by different sectors, government bodies and institutions. The new data can affect our understanding of the issues faced by cities and can support evidence based policies. However usage of data is limited due to difficulty in assimilating data from different sources. Open datasets often lack uniform structure which limits its analysis using traditional database systems. In this paper we present Citadel, a data hub for cities. Citadel's goal is to support end to end knowledge discovery cyber-infrastructure for effective analysis and policy support. Citadel is designed to ingest large amount of heterogeneous data and supports multiple use cases by encouraging data sharing in cities. Our poster presents the proposed features, architecture, implementation details and initial results.

Tagging animals with sensors is a powerful approach to acquire critical information about the behavioural ecology of free-living animals, which ultimately can provide data to inform best practice in conservation efforts. Sensor tags for such deployments need long lifetimes and incorporate multiple sensors, especially location because space use can contextualize behavior. The tag size needs to be minimal so as not to affect the activities of the animal. Aquatic animals in particular present challenges due to lack of wireless communication and water-proofing. Taking these points into consideration we have designed Aquamote: an ultra-low power, tiny sensor tag (20 x 29 mm2) which integrates accelerometer, gyroscope, magnetometer, depth sensor, GPS and BLE. Our poster will showcase the performance of AquaMote and highlight our design decisions to reduce its size and power consumption.

Thermostats are the primary interface for occupants of office buildings to express their thermal comfort preferences. However, traditional thermostats are often ineffective due to physical inaccessibility, lack of information or limited responsiveness, which lead to occupant discomfort. Modern thermostat designs do overcome some of these limitations, but retrofitting them to existing buildings is prohibitively expensive. Software thermostats based on web or smartphone apps provide an alternate interaction mechanism with minimal deployment cost. However, their usage and effectiveness have not been studied extensively in real settings. We present Genie, a novel software thermostat that we designed and deployed in our university for over 21 months. We compare the use of Genie to traditional thermostats. Our data and user study show that due to the clarity of information and wider thermal control provided by Genie, users feel more comfortable in their offices. Furthermore, the improved comfort did not affect the overall energy consumption or lead to misuse of HVAC controls.

Bridging disparate realms of physical and cyber system components requires models and methods that enable rapid evaluation of design alternatives in cyber-physical systems (CPS). The diverse intellectual traditions of physical and mathematical sciences makes this task exceptionally hard. This paper seeks to explore potential solutions by examining specific examples of CPS applications in automobiles and smart buildings. Both smart buildings and automobiles are complex systems with embedded knowledge across several domains. We present our experiences with development of CPS applications to illustrate the challenges that arise when expertise across domains is integrated into the system, and show that creation of models, abstractions, and architectures that address these challenges are key to next generation CPS applications.

Commercial buildings contribute to 19% of the primary energy consumption in the US, with HVAC systems accounting for 39.6% of this usage. To reduce HVAC energy use, prior studies have proposed using wireless occupancy sensors or even cameras for occupancy based actuation showing energy savings of up to 42%. However, most of these solutions require these sensors and the associated network to be designed, deployed, tested and maintained within existing buildings which is significantly costly.

We propose a novel power saving strategy called Sleep during Neighbor-Addressed Frame (SNAF) for improving energy efficiency of IEEE 802.11 based wireless networks. IEEE 802.11 (Wi-Fi) radios that employ SNAF mode can turn OFF their wireless transceivers (i.e., put radios in sleep mode) within specific periods of neighbor-addressed frames while they are being received. The sleep duration of transceivers is easy to determine with no loss of packet's critical control information. The proposed SNAF mode operation does not have any negative effect on network throughput and even complements Power Saving Mode (PSM) available in 802.11 standard. We further propose GreenFrame format for next generation wireless networks. In experiments conducted in wireless LAN scenarios, we observed savings as much as 57.8% when we implement SNAF mode in 802.11 standard and up to 49.5% when we implement SNAF mode in 802.11 PSM.