Content-centric network for autonomous driving

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 75-79).We introduce CarSpeak, a communication system for autonomous driving. CarSpeak enables a car to query and access sensory information captured by other cars in a manner similar to how it accesses information from its local sensors. CarSpeak adopts a content-centric approach where information objects - i.e., regions along the road - are first class citizens. It names and accesses road regions using a multi-resolution system, which allows it to scale the amount of transmitted data with the available bandwidth. CarSpeak also changes the MAC protocol so that, instead of having nodes contend for the medium, contention is between road regions, and the medium share assigned to any region depends on the number of cars interested in that region. CarSpeak is implemented in a state-of-the-art autonomous driving system and tested on indoor and outdoor hardware testbeds including an autonomous golf car and 10 iRobot Create robots. In comparison with a baseline that directly uses 802.11, CarSpeak reduces the time for navigating around obstacles by 2.4x, and reduces the probability of a collision due to limited visibility by 14 x.by Swarun Suresh Kumar.S.M

A cloud-assisted design for autonomous driving

This paper presents Carcel, a cloud-assisted system for autonomous driving. Carcel enables the cloud to have access to sensor data from autonomous vehicles as well as the roadside infrastructure. The cloud assists autonomous vehicles that use this system to avoid obstacles such as pedestrians and other vehicles that may not be directly detected by sensors on the vehicle. Further, Carcel enables vehicles to plan efficient paths that account for unexpected events such as road-work or accidents. We evaluate a preliminary prototype of Carcel on a state-of-the-art autonomous driving system in an outdoor testbed including an autonomous golf car and six iRobot Create robots. Results show that Carcel reduces the average time vehicles need to detect obstacles such as pedestrians by 4.6x compared to today's systems that do not have access to the cloud.Smart.fmNational Science Foundation (U.S.

Guaranteeing Spoof-Resilient Multi-Robot Networks

Multi-robot networks use wireless communication to provide wide-ranging services such as aerial surveillance and unmanned delivery. However, effective coordination between multiple robots requires trust, making them particularly vulnerable to cyber-attacks. Specifically, such networks can be gravely disrupted by the Sybil attack, where even a single malicious robot can spoof a large number of fake clients. This paper proposes a new solution to defend against the Sybil attack, without requiring expensive cryptographic key-distribution. Our core contribution is a novel algorithm implemented on commercial Wi-Fi radios that can "sense" spoofers using the physics of wireless signals. We derive theoretical guarantees on how this algorithm bounds the impact of the Sybil Attack on a broad class of robotic coverage problems. We experimentally validate our claims using a team of AscTec quadrotor servers and iRobot Create ground clients, and demonstrate spoofer detection rates over 96%

WSR: A WiFi Sensor for Collaborative Robotics

In this paper we derive a new capability for robots to measure relative direction, or Angle-of-Arrival (AOA), to other robots operating in non-line-of-sight and unmapped environments with occlusions, without requiring external infrastructure. We do so by capturing all of the paths that a WiFi signal traverses as it travels from a transmitting to a receiving robot, which we term an AOA profile. The key intuition is to "emulate antenna arrays in the air" as the robots move in 3D space, a method akin to Synthetic Aperture Radar (SAR). The main contributions include development of i) a framework to accommodate arbitrary 3D trajectories, as well as continuous mobility all robots, while computing AOA profiles and ii) an accompanying analysis that provides a lower bound on variance of AOA estimation as a function of robot trajectory geometry based on the Cramer Rao Bound. This is a critical distinction with previous work on SAR that restricts robot mobility to prescribed motion patterns, does not generalize to 3D space, and/or requires transmitting robots to be static during data acquisition periods. Our method results in more accurate AOA profiles and thus better AOA estimation, and formally characterizes this observation as the informativeness of the trajectory; a computable quantity for which we derive a closed form. All theoretical developments are substantiated by extensive simulation and hardware experiments. We also show that our formulation can be used with an off-the-shelf trajectory estimation sensor. Finally, we demonstrate the performance of our system on a multi-robot dynamic rendezvous task.Comment: 28 pages, 25 figures, *co-primary author

Peekaboo: A Hub-Based Approach to Enable Transparency in Data Processing within Smart Homes (Extended Technical Report)

We present Peekaboo, a new privacy-sensitive architecture for smart homes that leverages an in-home hub to pre-process and minimize outgoing data in a structured and enforceable manner before sending it to external cloud servers. Peekaboo's key innovations are (1) abstracting common data pre-processing functionality into a small and fixed set of chainable operators, and (2) requiring that developers explicitly declare desired data collection behaviors (e.g., data granularity, destinations, conditions) in an application manifest, which also specifies how the operators are chained together. Given a manifest, Peekaboo assembles and executes a pre-processing pipeline using operators pre-loaded on the hub. In doing so, developers can collect smart home data on a need-to-know basis; third-party auditors can verify data collection behaviors; and the hub itself can offer a number of centralized privacy features to users across apps and devices, without additional effort from app developers. We present the design and implementation of Peekaboo, along with an evaluation of its coverage of smart home scenarios, system performance, data minimization, and example built-in privacy features.Comment: 18 page

Interference alignment by motion

Recent years have witnessed increasing interest in interference alignment which has been demonstrated to deliver gains for wireless networks both analytically and empirically. Typically, interference alignment is achieved by having a MIMO sender precode its transmission to align it at the receiver. In this paper, we show, for the first time, that interference alignment can be achieved via motion, and works even for single-antenna transmitters. Specifically, this alignment can be achieved purely by sliding the receiver's antenna. Interestingly, the amount of antenna displacement is of the order of one inch which makes it practical to incorporate into recent sliding antennas available on the market. We implemented our design on USRPs and demonstrated that it can deliver 1.98× throughput gains over 802.11n in networks with both single-antenna and multi- antenna nodes.National Science Foundation (U.S.

Sanitizable signatures with strong transparency in the standard model

Sanitizable signatures provide several security features which are useful in many scenarios including military and medical applications. Sanitizable signatures allow a semi-trusted party to update some part of the digitally signed document without interacting with the original signer. Such schemes, where the verifer cannot identify whether the message has been sanitized, are said to possess strong transparency. In this paper, we have described the first efficient and provably secure sanitizable signature scheme having strong transparency under the standard model

Bringing cross-layer MIMO to today's wireless LANs

Recent years have seen major innovations in cross-layer wireless designs. Despite demonstrating significant throughput gains, hardly any of these technologies have made it into real networks. Deploying cross-layer innovations requires adoption from Wi-Fi chip manufacturers. Yet, manufacturers hesitate to undertake major investments without a better understanding of how these designs interact with real networks and applications. This paper presents the first step towards breaking this stalemate, by enabling the adoption of cross-layer designs in today's networks with commodity Wi-Fi cards and actual applications. We present OpenRF, a cross-layer architecture for managing MIMO signal processing. OpenRF enables access points on the same channel to cancel their interference at each other's clients, while beamforming their signal to their own clients. OpenRF is self-configuring, so that network administrators need not understand MIMO or physical layer techniques. We patch the iwlwifi driver to support OpenRF on off-the-shelf Intel cards. We deploy OpenRF on a 20-node network, showing how it manages the complex interaction of cross-layer design with a real network stack, TCP, bursty traffic, and real applications. Our results demonstrate an average gain of 1.6x for TCP traffic and a significant reduction in response time for real-time applications, like remote desktop.National Science Foundation (U.S.