13,824 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
A Pseudo Random Numbers Generator Based on Chaotic Iterations. Application to Watermarking
In this paper, a new chaotic pseudo-random number generator (PRNG) is
proposed. It combines the well-known ISAAC and XORshift generators with chaotic
iterations. This PRNG possesses important properties of topological chaos and
can successfully pass NIST and TestU01 batteries of tests. This makes our
generator suitable for information security applications like cryptography. As
an illustrative example, an application in the field of watermarking is
presented.Comment: 11 pages, 7 figures, In WISM 2010, Int. Conf. on Web Information
Systems and Mining, volume 6318 of LNCS, Sanya, China, pages 202--211,
October 201
3D Textured Model Encryption via 3D Lu Chaotic Mapping
In the coming Virtual/Augmented Reality (VR/AR) era, 3D contents will be
popularized just as images and videos today. The security and privacy of these
3D contents should be taken into consideration. 3D contents contain surface
models and solid models. The surface models include point clouds, meshes and
textured models. Previous work mainly focus on encryption of solid models,
point clouds and meshes. This work focuses on the most complicated 3D textured
model. We propose a 3D Lu chaotic mapping based encryption method of 3D
textured model. We encrypt the vertexes, the polygons and the textures of 3D
models separately using the 3D Lu chaotic mapping. Then the encrypted vertices,
edges and texture maps are composited together to form the final encrypted 3D
textured model. The experimental results reveal that our method can encrypt and
decrypt 3D textured models correctly. In addition, our method can resistant
several attacks such as brute-force attack and statistic attack.Comment: 13 pages, 7 figures, under review of SCI
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