6,550 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
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Decision making localization and decentralization in Japanese MNCs: Are there costs of leaving local managers out of the loop?
This paper reports results on decision making decentralization and localization in a study of 119 Japanese affiliates located in Europe and the U.S. The data indicate that decisions are generally decentralized. However, they also show that Japanese managers are involved in 80% of all decisions, and many decisions are made without any involvement by local managers. Our data also indicate, however, that there are few significant relationships between decision making decentralization or localization and affiliate performance. Implications of the results are discussed
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