MobiCom 2022 Papers

The emerging volumetric videos offer a fully immersive, six degrees of freedom (6DoF) viewing experience, at the cost of extremely high bandwidth demand. In this paper, we design, implement, and evaluate Vues, an edge-assisted transcoding system that delivers high-quality volumetric videos with low bandwidth requirement, low decoding overhead, and high quality of experience (QoE) on mobile devices. Through an IRB-approved user study, we build a first-of-its-kind QoE model to quantify the impact of various factors introduced by transcoding volumetric content into 2D videos. Motivated by the key observations from this user study, Vues employs a novel multiview approach with the overarching goal of boosting QoE. The Vues edge server adaptively transcodes a volumetric video frame into multiple 2D views with the help of a few lightweight machine learning models and strategically balances the extra bandwidth consumption of additional views and the improved QoE, indicated by our QoE model. The client selects the view that optimizes the QoE among the delivered candidates for display. Comprehensive evaluations using a prototype implementation indicate that Vues dramatically outperforms existing approaches. On average, it improves the QoE by 35% (up to 85%), compared to single-view transcoding schemes, and reduces the bandwidth consumption by 95%, compared to the state-of-the-art that directly streams volumetric videos.

This paper presents a uGPS (Underground GPS), which is the first SDR (Software Defined Radio)-based GNSS service to commodity GNSS receivers, including the latest iPhone, Android, and Car navigation. The uGPS is a system consisting of SDRs for GNSS signal generation, a GNSS D.O. for nano-second level timing synchronization's economic selection, fiber optic, and leaky feeder for a minimal environmental effect in tunnel environments. The proposed uGPS provides consistent 8 GPS satellites and 4 GLONASS satellites signal through a 1.5-kilometer-long tunnel. The uGPS equipped tunnel is highly compatible with commodity GNSS/GPS receivers. Therefore, it does not require any APP or special hardware. Our benchmark tested package was on top of the FPGA, in which modifications and upgrades were demonstrated via evaluation on three popular GNSS/GPS devices including Android phones, iPhones, and Car navigation systems. Further extensive evaluations demonstrated that the uGPS consistently achieved average location accuracy of 10 meters in 30~70km/h speed driving tests (without a map-matching algorithm), and a seamless handover between live GNSS and the uGPS system.

Next generation wireless networks embrace mmWave technology for its high capacity. Yet, mmWave radios bear a fundamental coverage limitation due to the high directionality and propagation artifacts. In this paper, we explore an economical paradigm based on 3D printing technology for mmWave coverage expansion. We propose MilliMirror, a fully passive metasurface, which can reshape and resteer mmWave beams to anomalous directions to illuminate the coverage blind spots. We develop a closed-form model to efficiently synthesize the MilliMirror design with thousands of unit elements and across a wide frequency band. We further develop an economical process based on 3D printing and metal deposition to fabricate MilliMirror. Our field test results show that MilliMirror can effectively fill the coverage holes and operate transparently to the standard mmWave beam management protocols.

In cellular networks, attacks on the communication link between a mobile device and the core network significantly impact privacy and availability. Up until now, fake base stations have been required to execute such attacks. Since they require a continuously high output power to attract victims, they are limited in range and can be easily detected both by operators and dedicated apps on users' smartphones.

Infrastructure-assisted autonomous driving is an emerging paradigm that expects to significantly improve the driving safety of autonomous vehicles. The key enabling technology for this vision is to fuse LiDAR results from the roadside infrastructure and the vehicle to improve the vehicle's perception in real time. In this work, we propose VIPS, a novel lightweight system that can achieve decimeter-level and real-time (up to 100 ms) perception fusion between driving vehicles and roadside infrastructure. The key idea of VIPS is to exploit highly efficient matching of graph structures that encode objects' lean representations as well as their relationships, such as locations, semantics, sizes, and spatial distribution. Moreover, by leveraging the tracked motion trajectories, VIPS can maintain the spatial and temporal consistency of the scene, which effectively mitigates the impact of asynchronous data frames and unpredictable communication/compute delays. We implement VIPS end-to-end based on a campus smart lamppost testbed. To evaluate the performance of VIPS under diverse situations, we also collect two new multi-view point cloud datasets using the smart lamppost testbed and an autonomous driving simulator, respectively. Experiment results show that VIPS can extend the vehicle's perception range by 140% within 58 ms on average, and delivers a 4X improvement in perception fusion accuracy and 47X data transmission saving over existing approaches. A video demo of VIPS based on the lamppost dataset is available at https://youtu.be/zW4oi_EWOu0.

Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, the idea of piracy tracing is not widely used at the moment, since existing fingerprinting-based streaming systems fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, a polarized and randomized SVD watermarking scheme suitable for short segments, and a collusion-resistant fingerprinting scheme optimized for large-scale streaming services. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing SVD watermarking schemes, the proposed watermarking scheme improves the watermark extraction accuracy by 2.25x at most and 1.5x on average. Compared with existing collusion-resistant fingerprinting schemes, the proposed scheme catches more colluders and improves the recall rate by 26%.

The spectrum above 100 GHz has the potential to enable accurate 3D wireless localization due to the large swath of available spectrum. Yet, existing wide-band localization systems utilize the time of arrival measurements requiring strict time synchronization. In this paper, we present 123-LOC, a novel non-coherent system for one-shot dual-polarized 3D localization above 100 GHz. Our key idea is to create unique asymmetric THz fingerprints in 3D so that a wireless node can jointly infer its angular position and distance by taking hints from the measured power-spectrum profile. We introduce a dual-polarized dual-slit waveguide structure that emits out signals into free-space with a key feature that the beam pattern depends on the frequency of the signal and the geometry of the slit. To distinguish the emissions from the two slits, we use polarization diversity and manipulate the aperture geometry of the two slits so that they transmit slightly different angular-spectral signatures. Our over-the-air experiments demonstrate that 123-LOC achieves an average angle estimation error of 1° together with millimeter-scale ranging resolution, solely through non-coherent power measurements.

Mobile GPU, as a ubiquitous and powerful accelerator, plays an important role in accelerating on-device DNN (Deep Neural Network) inference. The frequent-upgrade and diversity of mobile GPUs require automatic kernel generation to empower fast DNN deployment. However, current generated kernels have poor performance.

With the wide adoption of AI applications, there is a pressing need of enabling real-time neural network (NN) inference on small embedded devices, but deploying NNs and achieving high performance of NN inference on these small devices is challenging due to their extremely weak capabilities. Although NN partitioning and offloading can contribute to such deployment, they are incapable of minimizing the local costs at embedded devices. Instead, we suggest to address this challenge via agile NN offloading, which migrates the required computations in NN offloading from online inference to offline learning. In this paper, we present AgileNN, a new NN offloading technique that achieves real-time NN inference on weak embedded devices by leveraging eXplainable AI techniques, so as to explicitly enforce feature sparsity during the training phase and minimize the online computation and communication costs. Experiment results show that AgileNN's inference latency is >6X lower than the existing schemes, ensuring that sensory data on embedded devices can be timely consumed. It also reduces the local device's resource consumption by >8X, without impairing the inference accuracy.

In this paper, we are interested in high-quality imaging of still objects with only received power measurements of off-the-shelf WiFi transceivers. We show that the scattered WiFi signals off of objects carry much richer information about the edges of the objects than the surface points. Based on this observation, we then propose a completely different way of thinking about this imaging problem. More specifically, we propose Wiffract, a new foundation for imaging objects via edge tracing. Our approach uses the Geometrical Theory of Diffraction (GTD) and the corresponding Keller cones to image edges of the object. We extensively validate our approach with 37 experiments in three different areas, including through-wall scenarios. We take developing a WiFi Reader as one example application to showcase the capabilities of our proposed pipeline. More specifically, we show how our approach can successfully image several alphabet-shaped objects. We further show that our approach enables WiFi to read, i.e., correctly classify the letters, with an accuracy of 86.7%. Finally, we show how our approach enables WiFi to image and read through walls, by imaging the details and further reading the letters of the word "BELIEVE" through walls. Overall, our proposed approach can open up new directions for RF imaging.

With its ability to communicate long distances, LoRa promises city-scale IoT deployments for smart city applications. This long-range, however, also increases contention as many thousands of devices are connected. Recently, CSMA has been proposed as a viable MAC for resolving contention in LoRa networks. In this paper, supported by measurements, we demonstrate that CSMA is ineffective in urban deployments. While gateways stationed at rooftops enjoy a long communication range, 70% of the devices placed at street level fail to sense each others' transmissions and remain hidden, owing to obstructions by tall structures. We present Busy Signal Multiple Access (BSMA), where the LoRa gateway transmits a downlink busy signal while receiving an uplink transmission. The IoT devices defer uplink transmissions while a busy signal is present. Practically viable BSMA requires a full-duplex LoRa gateway - i.e., a gateway that can simultaneously receive and transmit in the same channel. We develop the first full Duplex LoRa gateway in the 915 MHz ISM band, overcoming challenges that arise from a 9× greater delay spread and the need for 1000× greater self-interference cancellation. Our implementation works with COTS LoRa devices and improves network capacity by 100% compared to CSMA in bursty loads while being fair to all IoT devices near and far.

Troubleshooting cellular service issues at the per-UE (User Equipment) level is an essential task for cellular providers. However, diagnosing service issues at per-UE level is costly because it requires advanced expertise and in-depth inspection of massive network log data. This paper presents NeTExp, a generic and comprehensive data-driven approach to automatically troubleshoot cellular service issues reported by customers. NeTExp determines whether the root cause of a user-reported service issue is from the network side or the device side through deep neural networks, which extract complex spatial-temporal feature profiles from massive network log data. The system is trained and validated using an extensive period of network and customer care data from a major cellular service provider in United States. We also present a case study on an external event that caused cellular service issues in 2020 to demonstrate the effectiveness of NeTExp on detecting network issues and identifying network-issue-related root causes at per-UE level.

This paper presents our experience in adopting recent research results of mobile phone based indoor/outdoor detection (IODetector) to support the real world business of on-demand food delivery. The real world deployment of the adopted IODetector involves three phases spanning 20 months, during which the deployment scales from a feasibility study across a few areas of interest to a city-wide trial in Shanghai, and eventually to nationwide deployment over 367 cities in China. Iterative development has been performed throughout different deployment phases to excel the IODetector. Large scale evaluation and comparative A/B testing suggest key value of adopting indoor/outdoor detection in the real world business. We also present the lessons learned from the deployment experience including real world know-hows, practical limits and constraints, as well as discussions on design alternatives. We believe this paper provides insights to guide future efforts in translating research results to industry adoptions.

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with enhanced privacy, aiming to achieve a "good" ML model for as many as participants while consuming as little as wall clock time. By executing across thousands or even millions of clients, FL demonstrates heterogeneous statistical characteristics and system divergence widely across participants, making its training suffer when adopting the traditional ML paradigm. The root cause of the training efficiency degradation is the random client selection criteria. Although existing FL paradigms propose several optimization schemes for client selection, they are still coarse-grained due to their under-exploitation on the clients' data and system heterogeneity, yielding sub-optimal performance for a variety of FL applications. In this paper, we propose PyramidFL1 to speed up the FL training while achieving a higher final model performance (i.e., time-to-accuracy). The core of PyramidFL is a fine-grained client selection, in which PyramidFL does not only focus on the divergence of those selected participants and non-selected ones for client selection but also fully exploits the data and system heterogeneity within selected clients to profile their utility more efficiently. Specifically, PyramidFL first determines the utility-based client selection from the global (i.e., server) view and then optimizes its utility profiling locally (i.e., client) for further client selection. In this way, we can prioritize the use of those clients with higher statistical and system utility consistently. In comparison with the state-of-the-art (i.e., Oort), our evaluation on the open-source FL benchmark shows that PyramidFL improves the final model accuracy by 3.68% -- 7.33%, with a speedup of 2.71 x -- 13.66X on the wall clock time consumption.

This paper presents the design, implementation, and experimental evaluation of a wireless biomedical implant platform exploiting the magnetoelectric effect for wireless power and bi-directional communication. As an emerging wireless power transfer method, magnetoelectric is promising for mm-scaled bio-implants because of its superior misalignment sensitivity, high efficiency, and low tissue absorption compared to other modalities [46, 59, 60]. Utilizing the same physical mechanism for power and communication is critical for implant miniaturization, but low-power magnetoelectric uplink communication has not been achieved yet. For the first time, we design and demonstrate near-zero power magnetoelectric backscatter from the mm-sized implants by exploiting the converse magnetostriction effects.