2,286 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
Rate-distortion Balanced Data Compression for Wireless Sensor Networks
This paper presents a data compression algorithm with error bound guarantee
for wireless sensor networks (WSNs) using compressing neural networks. The
proposed algorithm minimizes data congestion and reduces energy consumption by
exploring spatio-temporal correlations among data samples. The adaptive
rate-distortion feature balances the compressed data size (data rate) with the
required error bound guarantee (distortion level). This compression relieves
the strain on energy and bandwidth resources while collecting WSN data within
tolerable error margins, thereby increasing the scale of WSNs. The algorithm is
evaluated using real-world datasets and compared with conventional methods for
temporal and spatial data compression. The experimental validation reveals that
the proposed algorithm outperforms several existing WSN data compression
methods in terms of compression efficiency and signal reconstruction. Moreover,
an energy analysis shows that compressing the data can reduce the energy
expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294
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