Data aggregation in sensor networks

Abstract

Severe energy constraints and limited computing abilities of the nodes in a network present a major challenge in the design and deployment of a wireless sensor network. This thesis aims to present energy efficient algorithms for data fusion and information aggregation in a sensor network. The various methodologies of data fusion presented in this thesis intend to reduce the data traffic within a network by mapping the sensor network application task graph onto a sensor network topology. Partitioning of an application into sub-tasks that can be mapped onto the nodes of a sensor network offers opportunities to reduce the overall energy consumption of a sensor network. The first approach proposes a grid based coordinated incremental data fusion and routing with heterogeneous nodes of varied computational abilities. In this approach high performance nodes arranged in a mesh like structure spanning the network topology, are present amongst the resource constrained nodes. The sensor network protocol performance, measured in terms of hop-count is analysed for various grid sizes of the high performance nodes. To reduce network traffic and increase the energy efficiency in a randomly deployed sensor network, distributed clustering strategies which consider network density and structure similarity are applied on the network topology. The clustering methods aim to improve the energy efficiency of the sensor network by dividing the network into logical clusters and mapping the fusion points onto the clusters. Routing of network information is performed by inter-cluster and intra-cluster routing.