13,420 research outputs found
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
CUP: Comprehensive User-Space Protection for C/C++
Memory corruption vulnerabilities in C/C++ applications enable attackers to
execute code, change data, and leak information. Current memory sanitizers do
no provide comprehensive coverage of a program's data. In particular, existing
tools focus primarily on heap allocations with limited support for stack
allocations and globals. Additionally, existing tools focus on the main
executable with limited support for system libraries. Further, they suffer from
both false positives and false negatives.
We present Comprehensive User-Space Protection for C/C++, CUP, an LLVM
sanitizer that provides complete spatial and probabilistic temporal memory
safety for C/C++ program on 64-bit architectures (with a prototype
implementation for x86_64). CUP uses a hybrid metadata scheme that supports all
program data including globals, heap, or stack and maintains the ABI. Compared
to existing approaches with the NIST Juliet test suite, CUP reduces false
negatives by 10x (0.1%) compared to the state of the art LLVM sanitizers, and
produces no false positives. CUP instruments all user-space code, including
libc and other system libraries, removing them from the trusted code base
Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams
Wildfires are frequent, devastating events in Australia that regularly cause
significant loss of life and widespread property damage. Fire weather indices
are a widely-adopted method for measuring fire danger and they play a
significant role in issuing bushfire warnings and in anticipating demand for
bushfire management resources. Existing systems that calculate fire weather
indices are limited due to low spatial and temporal resolution. Localized
wireless sensor networks, on the other hand, gather continuous sensor data
measuring variables such as air temperature, relative humidity, rainfall and
wind speed at high resolutions. However, using wireless sensor networks to
estimate fire weather indices is a challenge due to data quality issues, lack
of standard data formats and lack of agreement on thresholds and methods for
calculating fire weather indices. Within the scope of this paper, we propose a
standardized approach to calculating Fire Weather Indices (a.k.a. fire danger
ratings) and overcome a number of the challenges by applying Semantic Web
Technologies to the processing of data streams from a wireless sensor network
deployed in the Springbrook region of South East Queensland. This paper
describes the underlying ontologies, the semantic reasoning and the Semantic
Fire Weather Index (SFWI) system that we have developed to enable domain
experts to specify and adapt rules for calculating Fire Weather Indices. We
also describe the Web-based mapping interface that we have developed, that
enables users to improve their understanding of how fire weather indices vary
over time within a particular region.Finally, we discuss our evaluation results
that indicate that the proposed system outperforms state-of-the-art techniques
in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure
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