Energy Scaling Laws for Distributed Inference in Random Fusion Networks

Details Energy Scaling Laws for Distributed Inference in Random Fusion Networks Energy Scaling Laws for Distributed Inference in Random Fusion Networks

2008-09-03

arxiv.org/abs/0809.0686v3

vol. 27, no. 7, pp.1203-1217, Sept. 2009

Computer Science

Information Theory

IEEE JSAC on Stochastic Geometry and Random Graphs for Wireless Networks

Scientific paper

The energy scaling laws of multihop data fusion networks for distributed inference are considered. The fusion network consists of randomly located sensors distributed i.i.d. according to a general spatial distribution in an expanding region. Among the class of data fusion schemes that enable optimal inference at the fusion center for Markov random field (MRF) hypotheses, the scheme with minimum average energy consumption is bounded below by average energy of fusion along the minimum spanning tree, and above by a suboptimal scheme, referred to as Data Fusion for Markov Random Fields (DFMRF). Scaling laws are derived for the optimal and suboptimal fusion policies. It is shown that the average asymptotic energy of the DFMRF scheme is finite for a class of MRF models.

Affiliated with

Also associated with

No associations

Rating

Energy Scaling Laws for Distributed Inference in Random Fusion Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Energy Scaling Laws for Distributed Inference in Random Fusion Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Energy Scaling Laws for Distributed Inference in Random Fusion Networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-105123