137,725 research outputs found
Achieving Small World Properties using Bio-Inspired Techniques in Wireless Networks
It is highly desirable and challenging for a wireless ad hoc network to have
self-organization properties in order to achieve network wide characteristics.
Studies have shown that Small World properties, primarily low average path
length and high clustering coefficient, are desired properties for networks in
general. However, due to the spatial nature of the wireless networks, achieving
small world properties remains highly challenging. Studies also show that,
wireless ad hoc networks with small world properties show a degree distribution
that lies between geometric and power law. In this paper, we show that in a
wireless ad hoc network with non-uniform node density with only local
information, we can significantly reduce the average path length and retain the
clustering coefficient. To achieve our goal, our algorithm first identifies
logical regions using Lateral Inhibition technique, then identifies the nodes
that beamform and finally the beam properties using Flocking. We use Lateral
Inhibition and Flocking because they enable us to use local state information
as opposed to other techniques. We support our work with simulation results and
analysis, which show that a reduction of up to 40% can be achieved for a
high-density network. We also show the effect of hopcount used to create
regions on average path length, clustering coefficient and connectivity.Comment: Accepted for publication: Special Issue on Security and Performance
of Networks and Clouds (The Computer Journal
Seven properties of self-organization in the human brain
The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of âstrongâ artificial intelligence in robotics are brought forward
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