Distributed source coding for sensor data model and estimation of cluster head errors using Bayesian and k-near neighborhood classifiers in deployment of dense wireless sensor networks

Vasanth Iyer, S. S. Iyengar, N. Balakrishnan, Vir Phoha, G. Rama Murthy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The lifetime calculation of large dense sensor networks with fixed energy resources and the remaining residual energy have shown that for a constant energy resource in a sensor network the fault rate at the cluster head is network size invariant when using the network layer with no MAC losses. Even after increasing the battery capacities in the nodes the total lifetime does not increase after a max limit of 8 times. As this is a serious limitation lots of research has been done at the MAC layer which allows to adapt to the specific connectivity, traffic and channel polling needs for sensor networks. There have been lots of MAC protocols which allow to control the channel polling of new radios which are available to sensor nodes to communicate. This further reduces the communication overhead by idling and sleep scheduling thus extending the lifetime of the monitoring application. We address the two issues which effects the distributed characteristics and performance of connected MAC nodes. (1) To determine the theoretical minimum rate based on joint coding for a correlated data source at the single-hop, (2a) to estimate cluster head errors using Bayesian rule for routing using persistence clustering when node densities are the same and stored using prior probability at the network layer, (2b) to estimate the upper bound of routing errors when using passive clustering were the node densities at the multi-hop MACS are unknown and not stored at the multi-hop nodes a priori. In this paper we evaluate many MAC based sensor network protocols and study the effects on sensor network lifetime. A renewable energy MAC routing protocol is designed when the probabilities of active nodes are not known a priori. From theoretical derivations we show that for a Bayesian rule with known class densities of ω1, ω2 with expected error P* is bounded by max error rate of P = 2P* for single-hop. We study the effects of energy losses using cross-layer simulation of large sensor network MACS setup, the error rate which effect finding sufficient node densities to have reliable multi-hop communications due to unknown node densities. The simulation results show that even though the lifetime is comparable the expected Bayesian posterior probability error bound is close or higher than P ≥ 2P*.

Original languageEnglish (US)
Title of host publicationProceedings - 2009 3rd International Conference on Sensor Technologies and Applications, SENSORCOMM 2009
Pages19-24
Number of pages6
DOIs
StatePublished - 2009
Event2009 3rd International Conference on Sensor Technologies and Applications, SENSORCOMM 2009 - Athens, Glyfada, Greece
Duration: Jun 18 2009Jun 23 2009

Publication series

NameProceedings - 2009 3rd International Conference on Sensor Technologies and Applications, SENSORCOMM 2009

Other

Other2009 3rd International Conference on Sensor Technologies and Applications, SENSORCOMM 2009
CountryGreece
CityAthens, Glyfada
Period6/18/096/23/09

Keywords

  • Baysian error classifiers
  • Cosets
  • Sensor data reliability
  • Slepian & Wolf coding

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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