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.
