Distributed Aggregate Function Estimation by Biphasically Configured Metropolis-Hasting Weight Model

• DOI: 10.13164/re.2017.0479
• Publication Date: 2017-06-13T08:46:42Z
• Authors: M. Kenyeres; J. Kenyeres; V. Skorpil; R. Burget
• Keywords: average consensus algorithm; 0202 electrical engineering, electronic engineering, information engineering; Electrical engineering. Electronics. Nuclear engineering; 02 engineering and technology; metropolis-hasting weight model; wireless sensor networks; aggregate function; Distributed computing; TK1-9971

An energy-efficient estimation of an aggregate function can significantly optimize a global event detection or monitoring in wireless sensor networks. This is probably the main reason why optimization complementary consensus algorithms one key challenges lifetime extension networks on which attention many scientists paid. In this paper, we introduce optimized weight model for average algorithm. It called Biphasically configured Metropolis-Hasting and based modification by rephrasing initial configuration into two parts. The first default model, while, other recalculation weights allocated to adjacent nodes' incoming values at cost decreasing value inner states.The whole executed fully-distributed manner.In experimental section, it proven that our optimizes several aspects achieves better results compared with concurrent models.