In the summer of 2025, Dan Halperin and his collaborators were awarded the SIGMOBILE Test of Time Award for their SIGCOMM 2010 paper entitled ''Predictable 802.11 Packet Delivery from Wireless Channel Measurements.''
The past few years have witnessed growing interest in millimeter wave (mmWave) based reconstruction in the mobile community [3, 5, 7, 14]. Unlike classical vision-based imaging systems, which are limited to line-of-sight, these mmWave-based systems can operate in through-occlusion scenarios, enabling them to sense objects in closed boxes and beneath clutter. This is because mmWave signals can traverse through many everyday occlusions (e.g., cardboard, fabric, etc.) [1, 11], and reflect off objects behind these occlusions, allowing them to produce images of the occluded objects. This capability, combined with the recent emergence of low-cost commercial mmWave radars, has the potential to enable many promising applications. For example, pick-and-place robots can leverage through-occlusion reconstructions to find and manipulate hidden objects, such as those beneath clutter or within a closed box. Similarly, Augmented Reality (AR) devices could leverage them to perceive occluded objects and display them to the user, truly augmenting our human perception. Smart home devices can use them for through-occlusion gesture recognition, to enable non-verbal commands even when users are hidden from view.
Passive Internet of Things (Passive IoT) has attracted widespread attention from both academia and industry due to its potential for ubiquitous deployment and round-theclock operation without the need for dedicated power sources [1]. As a key enabling technology for Passive IoT, backscatter communication has been an active research area for over a decade and has achieved remarkable progress [2, 3, 4]. We have witnessed the feasibility of implementing backscatter communication using various ambient energy sources [5, 6, 7, 8], with the communication range continuously extending from a few meters to reliably transmitting data over distances of more than one kilometer [9].
Mobile Augmented Reality (AR) applications demand high-quality, real-time visual prediction, including pixel-level depth and semantics, to enable immersive and context-aware user experiences. Recently, Vision Foundation Models (VFMs) have offered strong generalization capabilities on diverse and unseen data, supporting scalable mobile AR experiences. However, deploying VFMs on mobile devices is challenging due to computational limitations, particularly in maintaining both prediction accuracy and real-time performance. In this article, we present ARIA [3], the first system that enables on-device inference acceleration of a VFM. ARIA employs the heterogeneity of mobile processors through a parallel and selective inference scheme: full-frame prediction is periodically offloaded to a processor with high parallelism capability like GPU, while lowlatency updates on dynamic regions are conducted via a specialized accelerator like NPU. Implemented and evaluated using mobile devices, ARIA achieved significant improvements in accuracy and deadline success rate on real-world mobile AR scenarios.
When 5G first arrived, it came with big promises: ultra-high bandwidth, ultra-low latency, and a new generation of mobile experiences - from AR/VR to connected cars to early visions of the metaverse. Many of these applications were designed for the road, where consistency matters just as much as speed in maintaining high QoE. Yet the very design choices that give 5G its power - especially its use of higher-frequency spectrum - also make it fragile. Signals attenuate faster in higher frequency bands and coverage shrinks. And once you start moving, performance can vary wildly from one minute to the next.
Sound has connected people across distance for centuries, carrying voices, music, and emotion through the air. In the digital age, it connects people with their smartphones, bridging human communication and computational sensing. Today, smartphones use sound not only to transmit information but also to sense their surroundings. For example, voice assistants respond to spoken commands [10, 19, 21], while acoustic sensing techniques enable smartphones to detect hand gestures [1, 27], finger movements [13, 14], and even subtle physiological and behavioral signals such as respiration [3, 15], eye blinks [4, 16], and heartbeats [20, 26]. Together, these capabilities have transformed sound into a versatile sensing modality that allows smartphones to perceive and interpret the physical world.
The core principle of wireless sensing is that human activities and the environment physically alter the radio signals that travel through them. When a person moves or even breathes, they disturb the wireless signals, like Wi-Fi and mmWave, causing measurable changes in their properties [1]. By analyzing these subtle changes, our devices can learn to perceive the world without cameras, enabling applications from smart home control to healthcare monitoring. This transforms everyday devices into powerful privacy-preserving sensors.
Length is one of the seven fundamental physical quantities. For several centuries we have measured distances using calibrated physical objects, and more recently using light, sound, and radio waves. These measurements and the tools we use have enabled advances in several different domains, from the construction industry to space travel, from GPS localization to tracking of airplanes. With advances in electronics, clocks, miniaturization, and development of new algorithms, wireless distance measurement has now become possible. Measuring distances using wireless sensors offers the option of locating objects across rooms, through walls, and without visual line-of-sight. The measurement accuracy improves with larger bandwidth, which has resulted in the ultra-wideband (UWB) radio technology gaining significant traction. Seeing an opportunity in this capability, smartphone manufacturers such as Google and Apple have incorporated UWB radios in their offerings, setting the stage for future innovation using this versatile technology. [10] In this article we look beyond the UWB object-finding use-case and explore how a modest radio can transform mobile computing for decades to come.
Large-scale IoT deployment calls for inexpensive, low-power sensor nodes that still perform long-range, large-scale networking at the system level. In this paper, we propose a novel processor-sharing IoT architecture that converts the vast majority of sensor nodes from embedded computers to low-end RF peripherals. The conventional full-fledged sensor nodes are smashed into the air, and the scattered chips are scaled well with negligible overheads through a virtual I2C bus called RFBus.
Deep Neural Networks (DNNs) are crucial for applications like autonomous driving, augmented reality, and mixed reality. Growing concerns about latency and privacy increasingly require deploying task-specific networks on mobile and edge devices. Extensive research focuses on accelerating DNNs while preserving output quality, particularly through model adjustments tailored for resource-limited devices [1-5].
Wi-Fi 8, defined by IEEE 802.11bn and expected to be certified in 2028, introduces Ultra High Reliability (UHR) to support mobile computing and the Internet of Things (IoT). Unlike previous standards, Wi-Fi 8 ensures sustained throughput and low-latency connectivity in congested, highinterference environments through innovations such as multi-access point coordination, distributed resource units, latency reduction, and dynamic power management. This study analyzes the technological advancements of Wi-Fi 8 and its applications in augmented reality, industrial IoT, and smart cities, providing insights such as scalable interference mitigation and global spectrum coordination for the 32 billion IoT devices expected to reach by 2030. This paper provides practical guidance for engineers and researchers to meet the challenges in high-interference environments.
The wearable healthcare market is experiencing significant growth in recent years, reaching over 121 billion globally in 2021 and is predicted to surpass 390 billion by 2030 [1]. With more than 500 million units shipped annually, ear-worn wearables, such as headphones, earphones, etc., hold an enormous potential to be the platforms of innovations. Furthermore, they are already socially accepted as part of our daily life. As a result, introducing more sensing modalities into these wearables is less likely to obstruct the user's everyday activities, resulting in easy adoption for newly developed research and technology in this field [2].
High-resolution underwater imaging is central to deep-sea exploration, marine science, and subsea infrastructure inspection. As interest in deep-sea missions grows - from ecological monitoring and resource management to defense and offshore energy - autonomous underwater vehicles (AUVs) have become key enablers. These mobile robots must traverse large, complex terrains where visibility is minimal, and GPS is unavailable. Acoustic imaging, particularly synthetic aperture sonar (SAS), is a leading technique for underwater scene reconstruction, offering high spatial resolution with compact form factors. However, its widespread deployment remains limited by one critical constraint: the speed at which imaging can be performed.
In the fall of 2024, my PhD advisor, Brian Noble, and his collaborators were awarded the SIGMOBILE Test of Time Award for their seminal SOSP 1997 paper on Odyssey. It is a paper that I come back to many times over the years and, more often than not, I come away new insights or greater clarity on a research problem I am trying to understand. The paper stands out in at least three ways.
Promoting behavior change by delivering timely prompts based on real-time contextual recognition is the aim of AI-based just-in-time (JIT) mobile health interventions. Despite their growing adoption in mobile and wearable technologies, user disengagement remains a key challenge. To better understand this issue, we built a mobile JIT app that prompts physical activities and conducted an eight-week field study with 54 college students. Our findings highlight the impact of personal traits such as boredom proneness and self-control, and identify key disengagement factors. We offer design insights to support sustained engagement in mobile JIT systems.
Personal Informatics (PI) systems, such as apps and wearables that help users track physical activity, sleep, heart rate, or stress, have become critical tools for self-monitoring and health research. As these systems increasingly drive personal and clinical decisionmaking, it's vital to understand how equitable and representative they really are. Real-world harms have already surfaced in adjacent domains: health sensors like pulse oximeters underperform on darker skin tones [1], and female speakers and non-US nationalities experience significant performance degradation in automated speaker recognition [2]. These failures aren't just technical - they're structural, human-centric, and societal. Yet, despite their growing influence, PI systems remain critically under-researched from a fairness and equity perspective [3]. Our research, detailed in [4], investigates this question by examining when, how, and for whom bias arises in the lifecycle of PI systems.
When people start to feel the symptoms of a cold or the flu, one of the first things they do is check their temperature using either the back of their hand or a thermometer to determine if they have a fever, as an elevated body temperature can be an early indicator of illness. While consumer-grade thermometers are fairly ubiquitous, they are not always around when we need them. In a project we call FeverPhone, our goal was to develop an accessible alternative for cases when traditional thermometers are not readily available. This includes scenarios such as telehealth consultations and resource-constrained environments.
The pursuit of seamless human activity recognition and monitoring has driven extensive research into wearable sensing technologies. While inertial measurement units (IMUs) have become a dominant modality, challenges remain in achieving comfortable, unobtrusive integration - particularly for applications demanding longterm wearability and design flexibility. Conventional IMU-based systems often necessitate rigid attachment to specific body locations, hindering their adoption in fashion-forward or everyday garments. MoCaPose [1] is a novel approach that decouples sensor position from pose estimation by leveraging multi-channel capacitive sensing integrated within loose-fitting smart textiles. We aim to demonstrate the potential of this paradigm shift for creating truly wearable motion capture systems which can be used for human activity recognition (HAR) that prioritize both functionality and aesthetic integration.
The age of self-driving cars is no longer a distant sci-fi dream; it's an emergent reality. Highly automated vehicles, those capable of handling all driving tasks under specific or all conditions (SAE Level 4 or 5), promise a future where we can reclaim our commute time for work, relaxation, or entertainment. However, as with any technological shift, widespread adoption hinges on public trust and acceptance. For some individuals, the idea of ceding control to a machine, especially a first-time experience, can evoke anxiety [1], particularly when the vehicle behaves in unexpected ways. This is where the concept of ''explanations'' becomes relevant. We are discovering that providing passengers with information about what an automated vehicle is ''judging'' or ''perceiving'' can enhance their experience. Explanations can give passengers an increased sense of control and help them appropriately calibrate their trust based on the vehicle's actual capabilities [2]. Ultimately, this can reduce anxiety, encouraging automation use and making the journey more comfortable and productive.
Human faces have been widely adopted in many applications and systems requiring a high-security standard. Although face authentication is deemed to be mature nowadays, many existing works have demonstrated not only the privacy leakage of facial information but also the success of spoofing attacks on face biometrics. The critical reason behind this is the failure of liveness detection in biometrics. This work advances most biometric-based user authentication schemes by exploring dynamic biometrics (human facial activities) rather than traditional static biometrics (human faces). Inspired by observations from psychology, we propose the mmFaceID to leverage humans' dynamic facial activities when performing word reading for achieving robust, highly accurate, and effective user authentication via mmWave sensing. By addressing a series of technical challenges of capturing micro-level facial muscle movements using a mmWave sensor, we build a neural network to reconstruct facial activities via estimated expression parameters. Then, unique features can be extracted to enable robust user authentication regardless of relative distances and orientations. We conduct comprehensive experiments on 23 participants to evaluate mmFaceID in terms of distances/orientations, length of word lists, occlusion, and language backgrounds, demonstrating an authentication accuracy of 94.7%. We also extend our evaluation in a real IoT scenario. By speaking real IoT commends, the average authentication accuracy can reach up to 92.28%.
