Formulation of 3D Euclidean distance for network clustering in wireless sensor network

Abstract

In wireless sensor networks, nodes operating under dynamic topology are often correlated with their behavior. Correlated behavior may pose devastating impact towards network connectivity. A node may change its behaviour from cooperative node to misbehave node which directly affects the network’s connectivity. Misbehaviour nodes tend to have correlated effect which creates partitioning within the network. To improve network connectivity in providing an efficient communication in the events of the correlated behaviors, a new formulation of correlated degree to perform network clustering is required. This paper proposes a formulation on correlated degree using 3D Euclidean distance to achieve higher network connectivity under correlated node behavior. The key idea behind the 3D Euclidean distance in network clustering is to identify a set of sensors whose sensed values present some data correlation referring to correlated degree. The correlated degree is formulated based on three-point distance within a correlation region to identify the level of node correlation within neighboring nodes. In addition, the correlated degree also be able to detect the same group of node behavior which is grouped in correlated regions. 3D Euclidean distance is shown in mathematical analysis and how the new formulation calculates correlated degree is also discussed. It is also expected that the new 3D Euclidean distance formulation may help correlation region to change it cluster formation dynamically to achieve the required network connectivity.