562,130 research outputs found
Anonymous Networking amidst Eavesdroppers
The problem of security against timing based traffic analysis in wireless
networks is considered in this work. An analytical measure of anonymity in
eavesdropped networks is proposed using the information theoretic concept of
equivocation. For a physical layer with orthogonal transmitter directed
signaling, scheduling and relaying techniques are designed to maximize
achievable network performance for any given level of anonymity. The network
performance is measured by the achievable relay rates from the sources to
destinations under latency and medium access constraints. In particular,
analytical results are presented for two scenarios:
For a two-hop network with maximum anonymity, achievable rate regions for a
general m x 1 relay are characterized when nodes generate independent Poisson
transmission schedules. The rate regions are presented for both strict and
average delay constraints on traffic flow through the relay.
For a multihop network with an arbitrary anonymity requirement, the problem
of maximizing the sum-rate of flows (network throughput) is considered. A
selective independent scheduling strategy is designed for this purpose, and
using the analytical results for the two-hop network, the achievable throughput
is characterized as a function of the anonymity level. The throughput-anonymity
relation for the proposed strategy is shown to be equivalent to an information
theoretic rate-distortion function
Performance-based control system design automation via evolutionary computing
This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD)
automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient
evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional
designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’
controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of
a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on
a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing,
with good closed-loop performance and robustness in the presence of practical constraints and perturbations
Content Delivery Latency of Caching Strategies for Information-Centric IoT
In-network caching is a central aspect of Information-Centric Networking
(ICN). It enables the rapid distribution of content across the network,
alleviating strain on content producers and reducing content delivery
latencies. ICN has emerged as a promising candidate for use in the Internet of
Things (IoT). However, IoT devices operate under severe constraints, most
notably limited memory. This means that nodes cannot indiscriminately cache all
content; instead, there is a need for a caching strategy that decides what
content to cache. Furthermore, many applications in the IoT space are
timesensitive; therefore, finding a caching strategy that minimises the latency
between content request and delivery is desirable. In this paper, we evaluate a
number of ICN caching strategies in regards to latency and hop count reduction
using IoT devices in a physical testbed. We find that the topology of the
network, and thus the routing algorithm used to generate forwarding
information, has a significant impact on the performance of a given caching
strategy. To the best of our knowledge, this is the first study that focuses on
latency effects in ICN-IoT caching while using real IoT hardware, and the first
to explicitly discuss the link between routing algorithm, network topology, and
caching effects.Comment: 10 pages, 9 figures, journal pape
Scenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and
analysis of perception systems, especially those based on machine learning.
Specifically, we consider the problems of training a perception system to
handle rare events, testing its performance under different conditions, and
debugging failures. We show how a probabilistic programming language can help
address these problems by specifying distributions encoding interesting types
of inputs and sampling these to generate specialized training and test sets.
More generally, such languages can be used for cyber-physical systems and
robotics to write environment models, an essential prerequisite to any formal
analysis. In this paper, we focus on systems like autonomous cars and robots,
whose environment is a "scene", a configuration of physical objects and agents.
We design a domain-specific language, Scenic, for describing "scenarios" that
are distributions over scenes. As a probabilistic programming language, Scenic
allows assigning distributions to features of the scene, as well as
declaratively imposing hard and soft constraints over the scene. We develop
specialized techniques for sampling from the resulting distribution, taking
advantage of the structure provided by Scenic's domain-specific syntax.
Finally, we apply Scenic in a case study on a convolutional neural network
designed to detect cars in road images, improving its performance beyond that
achieved by state-of-the-art synthetic data generation methods.Comment: 41 pages, 36 figures. Full version of a PLDI 2019 paper (extending UC
Berkeley EECS Department Tech Report No. UCB/EECS-2018-8
Deep Drone Racing: From Simulation to Reality with Domain Randomization
Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics
Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854
Secure Transmission Design for Cognitive Radio Networks With Poisson Distributed Eavesdroppers
In this paper, we study physical layer security
in an underlay cognitive radio (CR) network. We consider
the problem of secure communication between a secondary
transmitter-receiver pair in the presence of randomly distributed
eavesdroppers under an interference constraint set by the primary
user. For different channel knowledge assumptions at the
transmitter, we design four transmission protocols to achieve the
secure transmission in the CR network. We give a comprehensive
performance analysis for each protocol in terms of transmission
delay, security, reliability, and the overall secrecy throughput.
Furthermore, we determine the optimal design parameter for
each transmission protocol by solving the optimization problem
of maximizing the secrecy throughput subject to both security
and reliability constraints. Numerical results illustrate the performance
comparison between different transmission protocols.ARC Discovery Projects Grant DP15010390
- …