MobiCom 2016 论文列表

This paper reports on our methodology and experience from a multi-year effort to cross-validate a vehicular network experiment with four hundred Dedicated Short Range Communications IEEE 802.11p transmitters through ns-3 simulations. With most of these transmitters in communication range, this represents an extremely dense wireless configuration that challenges radio and interference models. Field test and simulations were conducted in tandem and iteratively to facilitate model selection and configuration as well as to allow a detailed evaluation of simulation accuracy. We have learned that 1) results were most sensitive to parameter choices in the propagation and receiver models, with simulator default parameters not providing a good match; 2) results could, however, be significantly improved by adapting, implementing, and calibrating the propagation models and receiver models from the literature, yielding 88% accuracy (in terms of packet error rate compared to the field test) in such a complex large-scale setting; 3) the process was helpful in identifying errors both in the simulation models and in the experimental code and points to opportunities for further research.

Today's mobile devices are usually equipped with multiple wireless network interfaces that provide new opportunities for improving application performance. In this paper, we conduct an in-depth study of multipath for mobile settings, focusing on MPTCP, with the goal of developing key insights for evolving the mobile multipath design. First, we conduct to our knowledge the most in-depth and the longest user trial of mobile multipath that focuses not only on MPTCP performance, but also on cross-layer interactions. Second, we identify a new research problem of multipath-aware CDN server selection. We demonstrate its real-world importance and provide recommendations. Third, our measurement findings lead us to design and implement a flexible software architecture for mobile multipath called MPFlex, which strategically employs multiplexing to improve multipath performance (by up to 63% for short-lived flows). MPFlex decouples the high-level scheduling algorithm and the low-level OS protocol implementation, and enables developers to flexibly plug-in new multipath features. MPFlex also provides an ideal vantage point for flexibly realizing user-specified multipath policies and is friendly to middleboxes.

Mobile apps that use sensors to monitor user behavior often employ resource heavy inference algorithms that make computational offloading a common practice. However, existing schedulers/offloaders typically emphasize one primary offloading aspect without fully exploring complementary goals (e.g., heterogeneous resource management with only partial visibility into underlying algorithms, or concurrent sensor app execution on a single resource) and as a result, may overlook performance benefits pertinent to sensor processing.

We design and implement MobileInsight, a software tool that collects, analyzes and exploits runtime network information from operational cellular networks. MobileInsight runs on commercial off-the-shelf phones without extra hardware or additional support from operators. It exposes protocol messages on both control plane and (below IP) data plane from the 3G/4G chipset. It provides in-device protocol analysis and operation logic inference. It further offers a simple API, through which developers and researchers obtain access to low-level network information for their mobile applications. We have built three showcases to illustrate how MobileInsight is applied to cellular network research.

Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a fail-safe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters -- a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.

Cellular operators want to be able to deploy small cells with the same ease as WiFi access points, to quickly address traffic hotspots in dense urban areas. However, deploying small cells has not been easy. The reason is that due to scarcity of licensed spectrum, small cells need to use the same spectrum as the existing macro cells, and they need to explicitly coordinate their spectrum usage with each other to manage interference. The challenge is that this coordination needs to happen with latencies less than a millisecond, otherwise adding small cells does not help scale the overall network capacity. Implementing such tight coordination in dense urban deployments has not been easy in practice.

The transition to IPv6 cellular networks creates uncertainty for content providers (CPs) and content delivery networks (CDNs) of whether and how to follow suit. Do CPs that update their CDN contracts to allow IPv6 hosting achieve better, or worse performance in mobile networks? Should CDNs continue to host mobile content over IPv4 networks, or persuade to their CP customers the performance benefits of IPv6 content delivery?

Global Positioning System (GPS) is used ubiquitously in a wide variety of applications ranging from navigation and tracking to modern smart grids and communication networks. However, it has been demonstrated that modern GPS receivers are vulnerable to signal spoofing attacks. For example, today it is possible to change the course of a ship or force a drone to land in a hostile area by simply spoofing GPS signals. Several countermeasures have been proposed in the past to detect GPS spoofing attacks. These counter-measures offer protection only against naive attackers. They are incapable of detecting strong attackers such as those capable of seamlessly taking over a GPS receiver, which is currently receiving legitimate satellite signals, and spoofing them to an arbitrary location. Also, there is no hardware platform that can be used to compare and evaluate the effectiveness of existing countermeasures in real-world scenarios.

Multilateration techniques have been proposed to verify the integrity of unprotected location claims in wireless localization systems. A common assumption is that the adversary is equipped with only a single device from which it transmits location spoofing signals. In this paper, we consider a more advanced model where the attacker is equipped with multiple devices and performs a geographically distributed coordinated attack on the multilateration system. The feasibility of a distributed multi-device attack is demonstrated experimentally with a self-developed attack implementation based on multiple COTS software-defined radio (SDR) devices. We launch an attack against the OpenSky Network, an air traffic surveillance system that implements a time-difference-of-arrival (TDoA) multi-lateration method for aircraft localization based on ADS-B signals. Our experiments show that the timing errors for distributed spoofed signals are indistinguishable from the multilateration errors of legitimate aircraft signals, indicating that the threat of multi-device spoofing attacks is real in this and other similar systems. In the second part of this work, we investigate physical-layer features that could be used to detect multi-device attacks. We show that the frequency offset and transient phase noise of the attacker's radio devices can be exploited to discriminate between a received signal that has been transmitted by a single (legitimate) transponder or by multiple (malicious) spoofing sources. Based on that, we devise a multi-device spoofing detection system that achieves zero false positives and a false negative rate below 1%.

This paper presents DopEnc, an acoustic-based encounter profiling system on smartphones. DopEnc can automatically identify the persons that users interact with in the context of encountering. DopEnc performs encounter profiling in two major steps: (1) Doppler profiling to detect that two persons approach and stop in front of each other via an effective trajectory, and (2) voice profiling to confirm that they are thereafter engaged in an interactive conversation. DopEnc is further extended to support parallel acoustic exploration of many users by incorporating a unique multiple access scheme within the limited inaudible acoustic frequency band. All implementation of DopEnc is based on commodity sensors like speakers, microphones and accelerometers integrated on commercial-off-the-shelf smartphones. We evaluate DopEnc with detailed experiments and a real use-case study of 11 participants. Overall DopEnc achieves an accuracy of 6.9% false positive and 9.7% false negative in real usage.