40,759 research outputs found
HiTrust: building cross-organizational trust relationship based on a hybrid negotiation tree
Small-world phenomena have been observed in existing peer-to-peer (P2P) networks which has proved useful in the design of P2P file-sharing systems. Most studies of constructing small world behaviours on P2P are based on the concept of clustering peer nodes into groups, communities, or clusters. However, managing additional multilayer topology increases maintenance overhead, especially in highly dynamic environments. In this paper, we present Social-like P2P systems (Social-P2Ps) for object discovery by self-managing P2P topology with human tactics in social networks. In Social-P2Ps, queries are routed intelligently even with limited cached knowledge and node connections. Unlike community-based P2P file-sharing systems, we do not intend to create and maintain peer groups or communities consciously. In contrast, each node connects to other peer nodes with the same interests spontaneously by the result of daily searches
Myths and Legends of the Baldwin Effect
This position paper argues that the Baldwin effect is widely
misunderstood by the evolutionary computation community. The
misunderstandings appear to fall into two general categories.
Firstly, it is commonly believed that the Baldwin effect is
concerned with the synergy that results when there is an evolving
population of learning individuals. This is only half of the story.
The full story is more complicated and more interesting. The Baldwin
effect is concerned with the costs and benefits of lifetime
learning by individuals in an evolving population. Several
researchers have focussed exclusively on the benefits, but there
is much to be gained from attention to the costs. This paper explains
the two sides of the story and enumerates ten of the costs and
benefits of lifetime learning by individuals in an evolving population.
Secondly, there is a cluster of misunderstandings about the relationship
between the Baldwin effect and Lamarckian inheritance of acquired
characteristics. The Baldwin effect is not Lamarckian. A Lamarckian
algorithm is not better for most evolutionary computing problems than
a Baldwinian algorithm. Finally, Lamarckian inheritance is not a
better model of memetic (cultural) evolution than the Baldwin effect
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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