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.

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.

Pushing the limits of wireless networks

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 259-277).Wireless networks are everywhere around us and form a big part of our day-to-day lives. In this dissertation, we address the key challenges and opportunities of modern wireless networks. First, perhaps our biggest expectation from modern wireless networks is faster communication speeds. However, state-of-the-art Wi-Fi networks continue to struggle in crowded environments - airports and hotel lobbies. The core reason is interference - Wi-Fi access points today avoid transmitting at the same time on the same frequency, since they would otherwise interfere with each other. This thesis describes OpenRF, a novel system that enables today's Wi-Fi access points to directly combat this interference and demonstrate significantly faster data-rates for real applications. In addition, it presents MoMIMO, which demonstrates how the natural mobility of mobile users can be used to further mitigate interference. Second, can we use the ubiquitous Wi-Fi infrastructure around us to deliver new services, beyond communication? In particular, this dissertation focuses on indoor positioning, a service that has grabbed the attention of the academia and industry. While GPS has revolutionized outdoor navigation, it does not work indoors. Past work that has explored this problem is either limited in accuracy with errors of several meters, or advocates complete overhaul of the infrastructure with massive antenna-array access points that do not exist on consumer devices. Inspired by radar systems, we present Ubicarse, the first purely-software indoor positioning system for existing Wi-Fi devices that achieves tens of cm in positioning accuracy. Further, we build on this design to develop LTEye, which reveals new insights on how location impacts the performance of commercial AT&T and Verizon LTE cellular networks in the indoor space. Finally, we demonstrate how the tools we develop for indoor positioning open up new connections between wireless networking and robotics, to improve communication and security in multi-robot networks.by Swarun Kumar.Ph. D

Adaptive communication in multi-robot systems using directionality of signal strength

We consider the problem of satisfying communication demands in a multi-agent system where several robots cooperate on a task and a fixed subset of the agents act as mobile routers. Our goal is to position the team of robotic routers to provide communication coverage to the remaining client robots. We allow for dynamic environments and variable client demands, thus necessitating an adaptive solution. We present an innovative method that calculates a mapping between a robot’s current position and the signal strength that it receives along each spatial direction, for its wireless links to every other robot. We show that this information can be used to design a simple positional controller that retains a quadratic structure, while adapting to wireless signals in real-world environments. Notably, our approach does not necessitate stochastic sampling along directions that are counter-productive to the overall coordination goal, nor does it require exact client positions, or a known map of the environment.Lincoln LaboratoryMicro Autonomous Consortium Systems and Technology (ARL Grant W911NF-08-2-0004