3,304 research outputs found
Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks
A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned
A multiarchitecture parallel-processing development environment
A description is given of the hardware and software of a multiprocessor test bed - the second generation Hypercluster system. The Hypercluster architecture consists of a standard hypercube distributed-memory topology, with multiprocessor shared-memory nodes. By using standard, off-the-shelf hardware, the system can be upgraded to use rapidly improving computer technology. The Hypercluster's multiarchitecture nature makes it suitable for researching parallel algorithms in computational field simulation applications (e.g., computational fluid dynamics). The dedicated test-bed environment of the Hypercluster and its custom-built software allows experiments with various parallel-processing concepts such as message passing algorithms, debugging tools, and computational 'steering'. Such research would be difficult, if not impossible, to achieve on shared, commercial systems
Dynamic and Transparent Analysis of Commodity Production Systems
We propose a framework that provides a programming interface to perform
complex dynamic system-level analyses of deployed production systems. By
leveraging hardware support for virtualization available nowadays on all
commodity machines, our framework is completely transparent to the system under
analysis and it guarantees isolation of the analysis tools running on its top.
Thus, the internals of the kernel of the running system needs not to be
modified and the whole platform runs unaware of the framework. Moreover, errors
in the analysis tools do not affect the running system and the framework. This
is accomplished by installing a minimalistic virtual machine monitor and
migrating the system, as it runs, into a virtual machine. In order to
demonstrate the potentials of our framework we developed an interactive kernel
debugger, nicknamed HyperDbg. HyperDbg can be used to debug any critical kernel
component, and even to single step the execution of exception and interrupt
handlers.Comment: 10 pages, To appear in the 25th IEEE/ACM International Conference on
Automated Software Engineering, Antwerp, Belgium, 20-24 September 201
ExplainIt! -- A declarative root-cause analysis engine for time series data (extended version)
We present ExplainIt!, a declarative, unsupervised root-cause analysis engine
that uses time series monitoring data from large complex systems such as data
centres. ExplainIt! empowers operators to succinctly specify a large number of
causal hypotheses to search for causes of interesting events. ExplainIt! then
ranks these hypotheses, reducing the number of causal dependencies from
hundreds of thousands to a handful for human understanding. We show how a
declarative language, such as SQL, can be effective in declaratively
enumerating hypotheses that probe the structure of an unknown probabilistic
graphical causal model of the underlying system. Our thesis is that databases
are in a unique position to enable users to rapidly explore the possible causal
mechanisms in data collected from diverse sources. We empirically demonstrate
how ExplainIt! had helped us resolve over 30 performance issues in a commercial
product since late 2014, of which we discuss a few cases in detail.Comment: SIGMOD Industry Track 201
HyBIS: Windows Guest Protection through Advanced Memory Introspection
Effectively protecting the Windows OS is a challenging task, since most
implementation details are not publicly known. Windows has always been the main
target of malwares that have exploited numerous bugs and vulnerabilities.
Recent trusted boot and additional integrity checks have rendered the Windows
OS less vulnerable to kernel-level rootkits. Nevertheless, guest Windows
Virtual Machines are becoming an increasingly interesting attack target. In
this work we introduce and analyze a novel Hypervisor-Based Introspection
System (HyBIS) we developed for protecting Windows OSes from malware and
rootkits. The HyBIS architecture is motivated and detailed, while targeted
experimental results show its effectiveness. Comparison with related work
highlights main HyBIS advantages such as: effective semantic introspection,
support for 64-bit architectures and for latest Windows (8.x and 10), advanced
malware disabling capabilities. We believe the research effort reported here
will pave the way to further advances in the security of Windows OSes
Data processing model for the CDF experiment
The data processing model for the CDF experiment is described. Data
processing reconstructs events from parallel data streams taken with different
combinations of physics event triggers and further splits the events into
datasets of specialized physics datasets. The design of the processing control
system faces strict requirements on bookkeeping records, which trace the status
of data files and event contents during processing and storage. The computing
architecture was updated to meet the mass data flow of the Run II data
collection, recently upgraded to a maximum rate of 40 MByte/sec. The data
processing facility consists of a large cluster of Linux computers with data
movement managed by the CDF data handling system to a multi-petaByte Enstore
tape library. The latest processing cycle has achieved a stable speed of 35
MByte/sec (3 TByte/day). It can be readily scaled by increasing CPU and
data-handling capacity as required.Comment: 12 pages, 10 figures, submitted to IEEE-TN
SGXIO: Generic Trusted I/O Path for Intel SGX
Application security traditionally strongly relies upon security of the
underlying operating system. However, operating systems often fall victim to
software attacks, compromising security of applications as well. To overcome
this dependency, Intel introduced SGX, which allows to protect application code
against a subverted or malicious OS by running it in a hardware-protected
enclave. However, SGX lacks support for generic trusted I/O paths to protect
user input and output between enclaves and I/O devices.
This work presents SGXIO, a generic trusted path architecture for SGX,
allowing user applications to run securely on top of an untrusted OS, while at
the same time supporting trusted paths to generic I/O devices. To achieve this,
SGXIO combines the benefits of SGX's easy programming model with traditional
hypervisor-based trusted path architectures. Moreover, SGXIO can tweak insecure
debug enclaves to behave like secure production enclaves. SGXIO surpasses
traditional use cases in cloud computing and makes SGX technology usable for
protecting user-centric, local applications against kernel-level keyloggers and
likewise. It is compatible to unmodified operating systems and works on a
modern commodity notebook out of the box. Hence, SGXIO is particularly
promising for the broad x86 community to which SGX is readily available.Comment: To appear in CODASPY'1
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
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