10,925 research outputs found
Self-organization of Nodes using Bio-Inspired Techniques for Achieving Small World Properties
In an autonomous wireless sensor network, self-organization of the nodes is
essential to achieve network wide characteristics. We believe that connectivity
in wireless autonomous networks can be increased and overall average path
length can be reduced by using beamforming and bio-inspired algorithms. Recent
works on the use of beamforming in wireless networks mostly assume the
knowledge of the network in aggregation to either heterogeneous or hybrid
deployment. We propose that without the global knowledge or the introduction of
any special feature, the average path length can be reduced with the help of
inspirations from the nature and simple interactions between neighboring nodes.
Our algorithm also reduces the number of disconnected components within the
network. Our results show that reduction in the average path length and the
number of disconnected components can be achieved using very simple local rules
and without the full network knowledge.Comment: Accepted to Joint workshop on complex networks and pervasive group
communication (CCNet/PerGroup), in conjunction with IEEE Globecom 201
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
A Self-Organization Framework for Wireless Ad Hoc Networks as Small Worlds
Motivated by the benefits of small world networks, we propose a
self-organization framework for wireless ad hoc networks. We investigate the
use of directional beamforming for creating long-range short cuts between
nodes. Using simulation results for randomized beamforming as a guideline, we
identify crucial design issues for algorithm design. Our results show that,
while significant path length reduction is achievable, this is accompanied by
the problem of asymmetric paths between nodes. Subsequently, we propose a
distributed algorithm for small world creation that achieves path length
reduction while maintaining connectivity. We define a new centrality measure
that estimates the structural importance of nodes based on traffic flow in the
network, which is used to identify the optimum nodes for beamforming. We show,
using simulations, that this leads to significant reduction in path length
while maintaining connectivity.Comment: Submitted to IEEE Transactions on Vehicular Technolog
Biology of Applied Digital Ecosystems
A primary motivation for our research in Digital Ecosystems is the desire to
exploit the self-organising properties of biological ecosystems. Ecosystems are
thought to be robust, scalable architectures that can automatically solve
complex, dynamic problems. However, the biological processes that contribute to
these properties have not been made explicit in Digital Ecosystems research.
Here, we discuss how biological properties contribute to the self-organising
features of biological ecosystems, including population dynamics, evolution, a
complex dynamic environment, and spatial distributions for generating local
interactions. The potential for exploiting these properties in artificial
systems is then considered. We suggest that several key features of biological
ecosystems have not been fully explored in existing digital ecosystems, and
discuss how mimicking these features may assist in developing robust, scalable
self-organising architectures. An example architecture, the Digital Ecosystem,
is considered in detail. The Digital Ecosystem is then measured experimentally
through simulations, with measures originating from theoretical ecology, to
confirm its likeness to a biological ecosystem. Including the responsiveness to
requests for applications from the user base, as a measure of the 'ecological
succession' (development).Comment: 9 pages, 4 figure, conferenc
Bio Inspired Approach as a Problem Solving Technique
This paper describes the biologically inspired methodology as a computing and problem solving technique. Bio-inspired methods have recently gained importance in computing due to the need for flexible, adaptable ways of solving engineering problems. Bio-inspired algorithms are based on the structure and functioning of complex natural systems and tend to solve problems in an adaptable and distributed fashion. An example of a bio-inspired approach to solving the problem of location search has been taken up and discussed in this paper. The bio-inspired methodology has several merits and demerits, which are also discussed in the paper. Keywords: Bio-inspired approach, Merits and Demerits, Haptotaxis, Competitive and Cooperative Interaction
Autopoiesis of the artificial: from systems to cognition
In the seminal work on autopoiesis by Varela, Maturana, and Uribe, they start by addressing the confusion between processes that are history dependent and processes that are history independent in the biological world. The former is particularly linked to evolution and ontogenesis, while the latter pertains to the organizational features of biological individuals. Varela, Maturana, and Uribe reject this framework and propose their original theory of autopoietic organization, which emphasizes the strong complementarity of temporal and non-temporal phenomena. They argue that the dichotomy between structure and organization lies at the core of the unity of living systems. By opposing history-dependent and history-independent processes, methodological challenges arise in explaining phenomena related to living systems and cognition. Consequently, Maturana and Varela reject this approach in defining autopoietic organization. I argue, however, that this relationship presents an issue that can be found in recent developments of the science of artificial intelligence (AI) in different ways, giving rise to related concerns. While highly capable AI systems exist that can perform cognitive tasks, their internal workings and the specific contributions of their components to the overall system behavior, understood as a unified whole, remain largely uninterpretable. This article explores the connection between biological systems, cognition, and recent developments in AI systems that could potentially be linked to autopoiesis and related concepts such as autonomy and organization. The aim is to assess the advantages and disadvantages of employing autopoiesis in the synthetic (artificial) explanation for biological cognitive systems and to determine if and how the notion of autopoiesis can still be fruitful in this perspective
Bio-inspired design of a kinetic node for adaptable structures
Thesis (Master)--Ä°zmir Institute Of Technology, Architecture, Ä°zmir, 2011Includes bibliographical references (leaves: 112-119)Text in English; Abstract: Turkish and Englishxiii, 119 leavesThe architectural design should no longer consider just in terms of today's demands, but also the life cycle and the further requirements of the built environment. The design process should consider the adaptation to the changing conditions which can be in terms of the building usage, environmental factors or even in the changes ofsociological demands. Rapid change in activities of modern society and building technologies, has led to the need for adaptable spaces. Those spaces can be obtained by the adaptable structures which have potential for using our resources in efficient way and also for responding to the era's needs. This can be achieved with kinetic structural systems and learning adaptable structures from nature.Nature has always inspired humanity by solving the basic needs with minimum material and sustainable solutions. Observation of nature enables architects and engineers familiar with highly developed structures and lead to the creation of new forms. The designs that are produced by learning from nature lead to practical engineering solutions in terms of sustainability. The aim of this research is to propose a joint; kinetic node with multidisciplinary approach. This kinetic node is designed by inspiring from the minimum energy shape configurations and the structural orders in natural structures especially the cell membrane and analyzing the joining details of space truss structural systems and the geometric principles of Bricard linkage mechanism. This new kinetic node gives capability to construct variable static and dynamic structural systems while constructing in different structural orders
Self-Organization of Wireless Ad Hoc Networks as Small Worlds Using Long Range Directional Beams
We study how long range directional beams can be used for self-organization
of a wireless network to exhibit small world properties. Using simulation
results for randomized beamforming as a guideline, we identify crucial design
issues for algorithm design. Subsequently, we propose an algorithm for
deterministic creation of small worlds. We define a new centrality measure that
estimates the structural importance of nodes based on traffic flow in the
network, which is used to identify the optimum nodes for beamforming. This
results in significant reduction in path length while maintaining connectivity.Comment: Accepted to Joint workshop on complex networks and pervasive group
communication (CCNet/PerGroup), in conjunction with IEEE Globecom 201
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