23,863 research outputs found
Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks
In this paper, a family of ant colony algorithms called DAACA for data
aggregation has been presented which contains three phases: the initialization,
packet transmission and operations on pheromones. After initialization, each
node estimates the remaining energy and the amount of pheromones to compute the
probabilities used for dynamically selecting the next hop. After certain rounds
of transmissions, the pheromones adjustment is performed periodically, which
combines the advantages of both global and local pheromones adjustment for
evaporating or depositing pheromones. Four different pheromones adjustment
strategies are designed to achieve the global optimal network lifetime, namely
Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data
aggregation algorithms, DAACA shows higher superiority on average degree of
nodes, energy efficiency, prolonging the network lifetime, computation
complexity and success ratio of one hop transmission. At last we analyze the
characteristic of DAACA in the aspects of robustness, fault tolerance and
scalability.Comment: To appear in Journal of Computer and System Science
Dependability in Aggregation by Averaging
Aggregation is an important building block of modern distributed
applications, allowing the determination of meaningful properties (e.g. network
size, total storage capacity, average load, majorities, etc.) that are used to
direct the execution of the system. However, the majority of the existing
aggregation algorithms exhibit relevant dependability issues, when prospecting
their use in real application environments. In this paper, we reveal some
dependability issues of aggregation algorithms based on iterative averaging
techniques, giving some directions to solve them. This class of algorithms is
considered robust (when compared to common tree-based approaches), being
independent from the used routing topology and providing an aggregation result
at all nodes. However, their robustness is strongly challenged and their
correctness often compromised, when changing the assumptions of their working
environment to more realistic ones. The correctness of this class of algorithms
relies on the maintenance of a fundamental invariant, commonly designated as
"mass conservation". We will argue that this main invariant is often broken in
practical settings, and that additional mechanisms and modifications are
required to maintain it, incurring in some degradation of the algorithms
performance. In particular, we discuss the behavior of three representative
algorithms Push-Sum Protocol, Push-Pull Gossip protocol and Distributed Random
Grouping under asynchronous and faulty (with message loss and node crashes)
environments. More specifically, we propose and evaluate two new versions of
the Push-Pull Gossip protocol, which solve its message interleaving problem
(evidenced even in a synchronous operation mode).Comment: 14 pages. Presented in Inforum 200
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
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