Life cycle assessment (LCA) is a methodology for holistically measuring the environmental impact of a product from initial manufacturing to end-of-life disposal.However, the extent to which LCA informs the design of computing devices remains unclear.To understand how this information is collected and applied, we interviewed 17 industry professionals with experience in LCA or electronics design, systematically coded the interviews, and investigated common themes.These themes highlight the challenge of LCA data collection and reveal distributed decision-making processes where responsibility for sustainable design choices-and their associated costs-is often ambiguous.Our analysis identifes opportunities for HCI technologies to support LCA computation and its integration into the design process to facilitate sustainability-oriented decision-making.While this work provides a nuanced discussion about sustainable design in the information and communication technologies (ICT) hardware industry, we hope our insights will also be valuable to other sectors.

We present Phaser, a flexible system that directs narrow-beam laser light to moving robots for concurrent wireless power delivery and communication. We design a semiautomatic calibration procedure to enable fusion of stereo-vision-based 3D robot tracking with high-power beam steering, and a low-power optical communication scheme that reuses the laser light as a data channel. We fabricate a Phaser prototype using off-the-shelf hardware and evaluate its performance with battery-free autonomous robots. Phaser delivers optical power densities of over 110 mW/cm2and error-free data to mobile robots at multi-meter ranges, with on-board decoding drawing 0.3 mA (97% less current than Bluetooth Low Energy). We demonstrate Phaser fully powering gram-scale battery-free robots to nearly 2x higher speeds than prior work while simultaneously controlling them to navigate around obstacles and along paths. Code, an open-source design guide, and a demonstration video of Phaser is available at: mobilex.cs.columbia.edu/phaser

Spiking neural networks(SNNs) have emerged as a promising solution for deployment on resource-constrained edge devices and neuromorphic hardware due to their low power consumption.Spiking transformers, which integrate attention mechanisms similar to those found in artificial neural networks (ANNs), have recently exhibited impressive performance.However, these models are large in size and involve high-volume computation in both time and space, posing significant challenges for efficient hardware acceleration.We present Bishop, the first dedicated hardware accelerator architecture and HW/SW co-design framework for spiking transformers that optimally represents, manages, and processes spike-based workloads while exploring spatiotemporal sparsity and data reuse.Specifically, we introduce the concept of Token-Time Bundle (TTB), a container that bundles spiking data of a set of tokens over multiple time points.Our heterogeneous accelerator architecture Bishop concurrently processes workload packed in TTBs and explores intra-and inter-bundle multiple-bit weight reuse to significantly reduce memory access.Bishop utilizes a stratifier, a dense core array, and a sparse core array to process MLP blocks and projection layers.The stratifier routes high-density spiking activation workload to the dense core and low-density counterpart to the sparse core, ensuring optimized processing tailored to the given spatiotemporal sparsity level.To further reduce data access and computation, we introduce a novel Bundle Sparsity-Aware (BSA) training pipeline that enhances not only the overall but also structured TTB-level firing sparsity.Moreover, the processing efficiency of self-attention layers is boosted by the proposed Error-Constrained TTB Pruning (ECP), which trims activities in spiking queries, keys, and values both before and after the computation of spiking attention maps with a well-defined error bound.Finally, we design a reconfigurable TTB spiking attention core to efficiently compute spiking attention maps by executing highly simplified "AND" and "Accumulate" operations.On average, Bishop achieves a 5.91× speedup and 6.11×

Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge. Traditional quality control measures, such as prescreening workers and refining instructions, often focus solely on optimizing economic output. This paper explores just-in-time AI interventions to enhance both labeling quality and domain-specific knowledge among crowdworkers. We introduce LabelAId, an advanced inference model combining Programmatic Weak Supervision (PWS) with FT-Transformers to infer label correctness based on user behavior and domain knowledge. Our technical evaluation shows that our LabelAId pipeline consistently outperforms state-of-the-art ML baselines, improving mistake inference accuracy by 36.7% with 50 downstream samples. We then implemented LabelAId into Project Sidewalk, an open-source crowdsourcing platform for urban accessibility. A between-subjects study with 34 participants demonstrates that LabelAId significantly enhances label precision without compromising efficiency while also increasing labeler confidence. We discuss LabelAId’s success factors, limitations, and its generalizability to other crowdsourced science domains.

We introduce a low-power communication method that lets smartwatches leverage existing electrocardiogram (ECG) hardware as a data communication interface. Our unique approach enables the connection of external, inexpensive, and low-power "add-on" sensors to the smartwatch, expanding its functionalities. These sensors cater to specialized user needs beyond those offered by pre-built sensor suites, at a fraction of the cost and power of traditional communication protocols, including Bluetooth Low Energy. To demonstrate the feasibility of our approach, we conduct a series of exploratory and evaluative tests to characterize the ECG interface as a communication channel on commercial smartwatches. We design a simple transmission scheme using commodity components, demonstrating cost and power benefits. Further, we build and test a suite of add-on sensors, including UV light, body temperature, buttons, and breath alcohol, all of which achieved testing objectives at low material cost and power usage. This research paves the way for personalized and user-centric wearables by offering a cost-effective solution to expand their functionalities.

The growing demands for computational power in cloud computing have led to a significant increase in the deployment of high-performance servers. The growing power consumption of servers and the heat they produce is on track to outpace the capacity of conventional air cooling systems, necessitating more efficient cooling solutions such as liquid immersion cooling. The superior heat exchange capabilities of immersion cooling both eliminates the need for bulky heat sinks, fans, and air flow channels while also unlocking the potential go beyond conventional 2D blade servers to three-dimensional designs. In this work, we present a computational framework to explore designs of servers in three-dimensional space, specifically targeting the maximization of server density within immersion cooling tanks. Our tool is designed to handle a variety of physical and electrical server design constraints. We demonstrate our optimized designs can reduce server volume by 25--52% compared to traditional flat server designs. This increased density reduces land usage as well as the amount of liquid used for immersion, with significant reduction in the carbon emissions embodied in datacenter buildings. We further create physical prototypes to simulate dense server designs and perform real-world experiments in an immersion cooling tank demonstrating they operate at safe temperatures. This approach marks a critical step forward in sustainable and efficient datacenter management.

Sub-gram flying robots have transformative potential in applications from search and rescue to precision agriculture to environmental monitoring. However, a key gap in achieving autonomous flight for these applications is the low lift to weight ratio of flapping wing and quadrotor designs around 1 g or less. To close this gap, we propose a helictoper-style design that minimizes size and weight by leveraging the high lift, reliability, and low-voltage of sub-gram motors. We take an important step to enable this goal by designing a light-weight, micfrofabricated flybar mechanism to passively stabilize such a robot. Our 48 mg flybar is folded from a flat carbon fiber laminate into a 3D mechanism that couples tilting of the flybar to a change in the angle of attack of the rotors. Our design uses flexure joints instead of ball-in-socket joints common in larger flybars. To expedite the design exploration and optimization of a microfabricated flat-folded flybar, we develop a novel user-in-the-loop bi-level optimization workflow that combines Bayesian optimization design tools and expert feedback. We develop four template designs and use this method to achieve a peak damping ratio of 0.528, an 18.9x improvement from our initial design. Compared to a flybar-less rotor with a near 0 damping ratio, our flybar-rotor mechanism maintains a stable roll and pitch with relative deviations < 1°. Our results show that, if combined with a counter-torque mechanism such as a tail rotor, our miniaturized flybar could mechanically provide attitude stability for a sub-gram helicopter.

We present MilliMobile: a first of its kind battery-free autonomous robot capable of operating on harvested solar and RF power. We challenge the conventional assumption that motion and actuation are beyond the capabilities of battery-free devices and demonstrate completely untethered autonomous operation in realistic indoor and outdoor lighting as well as RF power delivery scenarios. We show first that through miniaturizing a robot to gram scale, we can significantly reduce the energy required to move it. Second, we develop methods to produce intermittent motion by discharging a small capacitor (47--150 μF) to move a motor in discrete steps, enabling motion from as little as 50 μW of power or less. We further develop software defined techniques for maximizing power harvesting. MilliMobile operates in the optimal part of the charging curve by varying the charging time to achieve maximum speeds of up to 5.5 mm/s.

Flying insect robots weighing less than a gram (FIRs) have advantages over their larger counterparts due to their low materials cost, small size, and low weight, allowing for deployment in large numbers. Control autonomy in such aircraft introduces challenges arising from their small size such as high-speed dynamics, limited power and payload capacity. Previous work has produced and characterized sensors with compatible mass and power specifications, many of which are biologically-inspired. And controlled flight has been demon-strated using feedback from external motion capture cameras. But to date, no avionics system has been reported that is light enough and capable of providing the feedback necessary to perform controlled hovering flight using only components carried on-board. Here we present such a system. It consists a sensor package consisting of an inertial measurement unit, a laser rangefinder and an optical flow sensor, and an associated estimator based on the nonlinear Extended Kalman Filter (EKF). The sensor suite weighs 187 mg and consumes 21 mW. We implemented a low-latency wireless link to transmit this data at 1 kHz without cumbersome wires. The EKF estimates attitude, altitude and lateral velocities. We estimate that computation power usage is <400 µW using floating-point operations on a standard microcontroller. Our system's RMSE attitude and position error are less than 4° and 1 cm relative to motion capture estimates.

This paper presents Eclipse, a platform for low-cost urban environmental sensing using solar-powered and cellular-connected devices. Dense sensor networks promise to monitor pollution at fine spatial and temporal resolutions, yet few cities have actually implemented such networks due to high costs and limited accuracy. We address these barriers by developing an end-to-end framework for urban air quality sensing with minimal infrastructure requirements. We designed an unobtrusive device that collects data on fine particulate matter (PM2.5), temperature, relative humidity, and barometric pres-sure. A modular design further includes four low-cost gas sensors - Ozone (03), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Carbon Monoxide (CO) - selected based on local priorities. We deployed 115 devices across Chicago, reliably collecting data for over 90% of expected sensor-hours from July 2 - September 30, 2021. We further developed a calibration strategy that reduced errors by 41.2 – 98.8%, improving accuracy to levels recommended for hotspot detection (PM2.5and 03) or education (NO2and SO2). Through this work, we offer insights on the real-world deployment of a replicable, large-scale, end-to-end platform for hyperlocal urban environmental sensing.

We have witnessed explosive growth in computing devices at all scales, in particular with small wireless devices that can permeate most of our physical world. The IoT industry is helping to fuel this insatiable desire for more and more data. We have to balance this growth with an understanding of its environmental impact. Indeed, the ENSsys community must take leadership in putting sustainability up front as a primary design principle for the future of IoT and related areas, expanding the research mandate beyond the intricacies of the computing systems in isolation to encompass and integrate the materials, new applications, and circular lifecycle of electronics in the IoT. Our call to action is seeded with a circularity-focused computing agenda that demands a cross-stack research program for energy-harvesting computational things.

This work highlights the need for a low-cost and low-overhead solution to monitor pallet-level environment in the food supply chain to create traceability, accountability and reduce wastage. We identify post-harvest sensing through the supply chain as a key need to reduce food waste. Toward this end, we develop initial prototypes of two different wireless environmental sensing architectures. The first leverages an ultra-low power timer with a current consumption of 35 nA to power gate and periodically wake up the system. The second mode explores a sparse event driven sensing model leveraging the threshold detection features of low power sensors to log events of interest. We demonstrate a millimeter scale prototypes that can read and backscatter temperature and humidity data with as little as 3.2 μW of power.

Constrained battery life on current Unmanned Aerial Vehicles (drones) limits the time they can operate and distance they can travel. We address this challenge by harvesting solar power to enable duty-cycled operation on a palm-sized drone. We present a scaling analysis that suggests that more solar power can be collected per unit mass of the drone as scale reduces, favoring small drones. By charging from the sun, the drone can operate for more than a single charging cycle, enabling extended mission time, and long-distance travel. To realize this, we design a high efficiency charging circuit and introduce two innovations. The first is a photovoltaic array that passively folds down while in flight to reduce air drag and automatically opens during landing due to the ground effect. The second is a sensor system and controller that autonomously finds suitable charging sites that are flat and well-lit. The drone can be fully charged in 3 hrs using the solar array and charging circuit with an average efficiency of 90.84%. Each charge enables a 4.7 min flight, allowing the drone to travel up to 1.2 km in a day. We also discuss how this platform could be used to take periodic measurements for smart agriculture or wildlife tracking, rapidly deploy wireless networks, or deploy microrobots in the future.

We present the first system that can airdrop wireless sensors from small drones and live insects. In addition to the challenges of achieving low-power consumption and long-range communication, airdropping wireless sensors is difficult because it requires the sensor to survive the impact when dropped in mid-air. Our design takes inspiration from nature: small insects like ants can fall from tall buildings and survive because of their tiny mass and size. Inspired by this, we design insect-scale wireless sensors that come fully integrated with an onboard power supply and a lightweight mechanical actuator to detach from the aerial platform. Our system introduces a first-of-its-kind 37 mg mechanical release mechanism to drop the sensor during flight, using only 450 μJ of energy as well as a wireless communication link that can transmit sensor data at 33 kbps up to 1 km. Once deployed, our 98 mg wireless sensor can run for 1.3-2.5 years when transmitting 10-50 packets per hour on a 68 mg battery. We demonstrate attachment to a small 28 mm wide drone and a moth (Manduca sexta) and show that our insect-scale sensors flutter as they fall, suffering no damage on impact onto a tile floor from heights of 22 m.

Wireless protocol design for IoT networks is an active area of research. We demonstrate tinySDR which is a low-power software-defined radio platform tailored to the needs of IoT endpoints. TinySDR is a standalone, fully programmable software-defined radio platform which has the requirements of IoT protocols. We present the physical layer implementations of BLE beacon and LoRa protocols to demonstrate the capabilities of tinySDR.

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.

To date, insect scale aerial robots have required wire tethers for providing power due to the challenges of integrating the required high-voltage power electronics within their severely constrained weight budgets. In this paper we present a significant milestone in the achievement of flight autonomy: the first wireless liftoff of a 190 mg aerial vehicle. Our robot is remotely powered using a 976 nm laser and integrates a complete power electronics package weighing a total of 104 mg, using commercially available components and fabricated using a fast-turnaround laser based circuit fabrication technique. The onboard electronics include a lightweight boost converter capable of producing high voltage bias and drive signals of over 200 V at up to 170 Hz and regulated by a microcontroller performing feedback control. We present our system design and analysis, detailed description of our fabrication method, and results from flight experiments.

As connected devices continue to decrease in size, we explore the idea of leveraging everyday surfaces such as tabletops and walls to augment the wireless capabilities of devices. Specifically, we introduce Surface MIMO, a technique that enables MIMO communication between small devices via surfaces coated with conductive paint or covered with conductive cloth. These surfaces act as an additional spatial path that enables MIMO capabilities without increasing the physical size of the devices themselves. We provide an extensive characterization of these surfaces that reveal their effect on the propagation of EM waves. Our evaluation shows that we can enable additional spatial streams using the conductive surface and achieve average throughput gains of 2.6-3x for small devices. Finally, we also leverage the wideband characteristics of these conductive surfaces to demonstrate the first Gbps surface communication system that can directly transfer bits through the surface at up to 1.3Gbps.

The vision of tracking small IoT devices runs into the reality of localization technologies --- today it is difficult to continuously track objects through walls in homes and warehouses on a coin cell battery. While Wi-Fi and ultra-wideband radios can provide tracking through walls, they do not last more than a month on small coin and button cell batteries since they consume tens of milliwatts of power. We present the first localization system that consumes microwatts of power at a mobile device and can be localized across multiple rooms in settings like homes and hospitals. To this end, we introduce a multi-band backscatter prototype that operates across 900 MHz, 2.4 and 5 GHz and can extract the backscatter phase information from signals that are below the noise floor. We build sub-centimeter sized prototypes which consume 93 μW and could last five to ten years on button cell batteries. We achieved ranges of up to 60 m away from the AP and accuracies of 2, 12, 50 and 145 cm at 1, 5, 30 and 60 m respectively. To demonstrate the potential of our design, we deploy it in two real-world scenarios: five homes in a metropolitan area and the surgery wing of a hospital in patient pre-op and post-op rooms as well as storage facilities.

We present the first wireless physical analytics system for 3D printed objects using commonly available conductive plastic filaments. Our design can enable various data capture and wireless physical analytics capabilities for 3D printed objects, without the need for electronics. To achieve this goal, we make three key contributions: (1) demonstrate room scale backscatter communication and sensing using conductive plastic filaments, (2) introduce the first backscatter designs that detect a variety of bi-directional motions and support linear and rotational movements, and (3) enable data capture and storage for later retrieval when outside the range of the wireless coverage, using a ratchet and gear system. We validate our approach by wirelessly detecting the opening and closing of a pill bottle, capturing the joint angles of a 3D printed e-NABLE prosthetic hand, and an insulin pen that can store information to track its use outside the range of a wireless receiver.

Our goal is to 3D print wireless sensors, input widgets and objects that can communicate with smartphones and other Wi-Fi devices, without the need for batteries or electronics. To this end, we present a novel toolkit for wireless connectivity that can be integrated with 3D digital models and fabricated using commodity desktop 3D printers and commercially available plastic filament materials. Specifically, we introduce the first computational designs that 1) send data to commercial RF receivers including Wi-Fi, enabling 3D printed wireless sensors and input widgets, and 2) embed data within objects using magnetic fields and decode the data using magnetometers on commodity smartphones. To demonstrate the potential of our techniques, we design the first fully 3D printed wireless sensors including a weight scale, flow sensor and anemometer that can transmit sensor data. Furthermore, we 3D print eyeglass frames, armbands as well as artistic models with embedded magnetic data. Finally, we present various 3D printed application prototypes including buttons, smart sliders and physical knobs that wirelessly control music volume and lights as well as smart bottles that can sense liquid flow and send data to nearby RF devices, without batteries or electronics.

We present fingerIO, a novel fine-grained finger tracking solution for around-device interaction. FingerIO does not require instrumenting the finger with sensors and works even in the presence of occlusions between the finger and the device. We achieve this by transforming the device into an active sonar system that transmits inaudible sound signals and tracks the echoes of the finger at its microphones. To achieve sub-centimeter level tracking accuracies, we present an innovative approach that use a modulation technique commonly used in wireless communication called Orthogonal Frequency Division Multiplexing (OFDM). Our evaluation shows that fingerIO can achieve 2-D finger tracking with an average accuracy of 8 mm using the in-built microphones and speaker of a Samsung Galaxy S4. It also tracks subtle finger motion around the device, even when the phone is in the pocket. Finally, we prototype a smart watch form-factor fingerIO device and show that it can extend the interaction space to a 0.5×0.25 m2 region on either side of the device and work even when it is fully occluded from the finger.

We show for the first time that commodity devices can be used to generate wireless data transmissions that are confined to the human body. Specifically, we show that commodity input devices such as fingerprint sensors and touchpads can be used to transmit information to only wireless receivers that are in contact with the body. We characterize the propagation of the resulting transmissions across the whole body and run experiments with ten subjects to demonstrate that our approach generalizes across different body types and postures. We also evaluate our communication system in the presence of interference from other wearable devices such as smartwatches and nearby metallic surfaces. Finally, by modulating the operations of these input devices, we demonstrate bit rates of up to 50 bits per second over the human body.

In this poster, we show for the first time that commodity devices can be used to generate wireless data transmissions that are confined to the human body. Specifically, we show that commodity input devices such as fingerprint sensors and touchpads can be used to transmit information to only wireless receivers that are in contact with the body.

We introduce inter-technology backscatter, a novel approach that transforms wireless transmissions from one technology to another, on the air. Specifically, we show for the first time that Bluetooth transmissions can be used to create Wi-Fi and ZigBee-compatible signals using backscatter communication. Since Bluetooth, Wi-Fi and ZigBee radios are widely available, this approach enables a backscatter design that works using only commodity devices.