Abstract
In this paper we derive a clustering method based
on the Hidden Conditional Random Field (HCRF) model in order
to maximizes the performance of a wireless sensor. Our novel
approach to clustering in this paper is in the application of an
index invariant graph that we defined in a previous work and that
precisely links a hyper-tree structure to the data set assumptions.
We show that a set of conditional index invariant hyper graph
forms a tree and then, we show that any tree factorization
optimizes the conditional probability of an HCRF model. We
evaluate our method based on a custom data set that we obtain
by running simulations of a time dynamic sensor network. The
performance of the proposed method outperforms the existing
clustering methods, such as the Girvan-Newmans algorithm, the
Kargers algorithm and the Spectral Clustering method, in terms
of packet acceptance probability and delay.
on the Hidden Conditional Random Field (HCRF) model in order
to maximizes the performance of a wireless sensor. Our novel
approach to clustering in this paper is in the application of an
index invariant graph that we defined in a previous work and that
precisely links a hyper-tree structure to the data set assumptions.
We show that a set of conditional index invariant hyper graph
forms a tree and then, we show that any tree factorization
optimizes the conditional probability of an HCRF model. We
evaluate our method based on a custom data set that we obtain
by running simulations of a time dynamic sensor network. The
performance of the proposed method outperforms the existing
clustering methods, such as the Girvan-Newmans algorithm, the
Kargers algorithm and the Spectral Clustering method, in terms
of packet acceptance probability and delay.
| Original language | English |
|---|---|
| Title of host publication | 2016 50th Annual Asilomar Conference on Signals, Systems, and Computers : (ASILOMAR 2016) |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | Nov 2016 |
| Pages | 1400-1405 |
| ISBN (Print) | 978-1-5386-3955-9 |
| ISBN (Electronic) | 978-1-5386-3954-2 |
| DOIs | |
| Publication status | Published - Nov 2016 |
| Event | IEEE Annual Asilomar Conference on Signals, Systems, and Computers: 2016 50th Annual Asilomar Conference on Signals, Systems, and Computers - Pacific Grove, California, Pacific Grove, United States Duration: 6 Nov 2016 → 9 Nov 2016 |
Conference
| Conference | IEEE Annual Asilomar Conference on Signals, Systems, and Computers |
|---|---|
| Location | Pacific Grove, California |
| Country/Territory | United States |
| City | Pacific Grove |
| Period | 06/11/2016 → 09/11/2016 |