MobiCom 2018 Papers

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.

Enabling touch gesture sensing on all surfaces of the mobile device, not limited to the touchscreen area, leads to new user interaction experiences. In this paper, we propose VSkin, a system that supports fine-grained gesture-sensing on the back of mobile devices based on acoustic signals. VSkin utilizes both the structure-borne sounds, i.e., sounds propagating through the structure of the device, and the air-borne sounds, i.e., sounds propagating through the air, to sense finger tapping and movements. By measuring both the amplitude and the phase of each path of sound signals, VSkin detects tapping events with an accuracy of 99.65% and captures finger movements with an accuracy of 3.59mm.

Economies of scale associated with hyper-scale public cloud platforms offer flexibility and cost-effectiveness, resulting in various services and businesses moving to the cloud. One area with little progress in this direction is cellular core networks. A cellular core network manages the state of cellular clients; it is essentially a large distributed state machine with very different virtualization challenges compared to typical cloud services. In this paper we present a novel cellular core network architecture, called ECHO, particularly suited to public cloud deployments, where the availability guarantees might be an order of magnitude worse compared to existing (redundant) hardware platforms. We present the design and implementation of our approach and evaluate its functionality on a public cloud platform. Analysis shows ECHO promises higher availability than existing telco solutions.

We provide the first solution to an important question, "how a physical-layer authentication method can defend against signal replay attacks''. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications.

Cross-technology Communication (CTC) is a key technique to explore the full capacity of heterogeneous wireless. The latest CTC designs explore the PHY-layer to reach the standards' maximum rate, but leaving a critical gap to practicality -- existing PHY-layer CTCs are commonly transmitter-side techniques requiring a high-end transmitter (with a high degree of freedom in signal manipulation) to emulate the receiver signal closely. This inherently limits the reverse direction (low-end to high-end) communication. We present XBee, a unique receiver-side CTC that fills in the gap and makes a critical step towards achieving CTC bidirectionality. XBee is demonstrated as a ZigBee to BLE communication, where the key innovation lies in the unique mechanism of cross-technology decoding, or cross-decoding in short, which interprets a ZigBee frame only by carefully observing the bit patterns obtained at the BLE receiver. Technically, XBee counterintuitively explores the sampling offset to overcome the intrinsic challenge due to BLE's narrower bandwidth (1MHz) than ZigBee (2MHz). Extensive implementation and evaluation on USRP and commodity devices reach 250 kbps under 85% reliability, a 15,000x improvement over state-of-the-art ZigBee to BLE communication, and comparable with the latest PHY-layer CTCs to achieve CTC bidirectionality.

Multi-stream 60 GHz communication has the potential to achieve data rates up to $100$ Gbps via multiplexing multiple data streams. Unfortunately, establishing multi-stream directional links can be a high overhead procedure as the search space increases with the number of spatial streams and the product of AP-client beam resolution. In this paper, we present MUlti-stream beam-Training for mm-wavE networks (MUTE) a novel system that leverages channel sparsity, GHz-scale sampling rate, and the knowledge of mm-Wave RF codebook beam patterns to construct a set of candidate beams for multi-stream beam steering. In 60 GHz WLANs, the AP establishes and maintains a directional link with every client through periodic beam training. MUTE repurposes these beam acquisition sweeps to estimate the Power Delay Profile (PDP) of each beam with zero additional overhead. Coupling PDP estimates with beam pattern knowledge, MUTE selects a set of candidate beams that capture diverse or ideally orthogonal paths to obtain maximum stream separability. Our experiments demonstrate that MUTE achieves 90% of the maximum achievable aggregate rate while incurring only 0.04% of exhaustive search's training overhead.

Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer human activities without the need for the monitored subject to carry a dedicated device. Existing device free human activity recognition approaches and systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The wireless signals arriving at the receiving devices usually carry substantial information that is specific to the environment where the activities are recorded and the human subject who conducts the activities. Due to this reason, an activity recognition model that is trained on a specific subject in a specific environment typically does not work well when being applied to predict another subject's activities that are recorded in a different environment. To address this challenge, in this paper, we propose EI, a deep-learning based device free activity recognition framework that can remove the environment and subject specific information contained in the activity data and extract environment/subject-independent features shared by the data collected on different subjects under different environments. We conduct extensive experiments on four different device free activity recognition testbeds: WiFi, ultrasound, 60 GHz mmWave, and visible light. The experimental results demonstrate the superior effectiveness and generalizability of the proposed EI framework.

Multi-carrier cellular access dynamically selects a preferred wireless carrier by leveraging the availability and diversity of multiple carrier networks at a location. It offers an alternative to the dominant single-carrier paradigm, and shows early signs of success through the operational Project Fi by Google. In this paper, we study the important, yet largely unexplored, problem of inter-carrier switching for multi-carrier access. We show that policy conflicts can arise between inter- and intra-carrier switching, resulting in oscillations among carriers in the worst case akin to BGP looping. We derive the conditions under which such oscillations occur for three categories of popular policy, and validate them with Project Fi whenever possible. We provide practical guidelines to ensure loop-freedom and assess them via trace-driven emulations.

Hidden screen-camera communication techniques emerge as a new paradigm that embeds data imperceptibly into regular videos while remaining unobtrusive to human viewers. Three key goals on imperceptible, high rate, and reliable communication are desirable but conflicting, and existing solutions usually made a trade-off among them. In this paper, we present the design and implementation of ChromaCode, a screen-camera communication system that achieves all three goals simultaneously. In our design, we consider for the first time color space for perceptually uniform lightness modifications. On this basis, we design an outcome-based adaptive embedding scheme, which adapts to both pixel lightness and regional texture. Last, we propose a concatenated code scheme for robust coding and devise multiple techniques to overcome various screen-camera channel errors. Our prototype and experiments demonstrate that ChromaCode achieves remarkable raw throughputs of >700 kbps, data goodputs of 120 kbps with BER of 0.05, and with fully imperceptible flicker for viewing proved by user study, which significantly outperforms previous works.