11,342 research outputs found
Adaptive Dynamics of Realistic Small-World Networks
Continuing in the steps of Jon Kleinberg's and others celebrated work on
decentralized search in small-world networks, we conduct an experimental
analysis of a dynamic algorithm that produces small-world networks. We find
that the algorithm adapts robustly to a wide variety of situations in realistic
geographic networks with synthetic test data and with real world data, even
when vertices are uneven and non-homogeneously distributed.
We investigate the same algorithm in the case where some vertices are more
popular destinations for searches than others, for example obeying power-laws.
We find that the algorithm adapts and adjusts the networks according to the
distributions, leading to improved performance. The ability of the dynamic
process to adapt and create small worlds in such diverse settings suggests a
possible mechanism by which such networks appear in nature
Void Traversal for Guaranteed Delivery in Geometric Routing
Geometric routing algorithms like GFG (GPSR) are lightweight, scalable
algorithms that can be used to route in resource-constrained ad hoc wireless
networks. However, such algorithms run on planar graphs only. To efficiently
construct a planar graph, they require a unit-disk graph. To make the topology
unit-disk, the maximum link length in the network has to be selected
conservatively. In practical setting this leads to the designs where the node
density is rather high. Moreover, the network diameter of a planar subgraph is
greater than the original graph, which leads to longer routes. To remedy this
problem, we propose a void traversal algorithm that works on arbitrary
geometric graphs. We describe how to use this algorithm for geometric routing
with guaranteed delivery and compare its performance with GFG
Analysis of Performance of Dynamic Multicast Routing Algorithms
In this paper, three new dynamic multicast routing algorithms based on the
greedy tree technique are proposed; Source Optimised Tree, Topology Based Tree
and Minimum Diameter Tree. A simulation analysis is presented showing various
performance aspects of the algorithms, in which a comparison is made with the
greedy and core based tree techniques. The effects of the tree source location
on dynamic membership change are also examined. The simulations demonstrate
that the Source Optimised Tree algorithm achieves a significant improvement in
terms of delay and link usage when compared to the Core Based Tree, and greedy
algorithm
An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks
Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs
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