  Objective-The main purpose of this paper is to construct a distributed
clustering algorithm such that each distributed cluster can perform the data
accuracy at their respective cluster head node before data aggregation and
transmit the data to the sink node. Design approach/Procedure - We investigate
that the data are spatially correlated among the sensor nodes which form the
clusters in the spatial domain. Due to high correlation of data, these clusters
of sensor nodes are overlapped in the spatial domain. To overcome this problem,
we construct a distributed clustering algorithm with non-overlapping irregular
clusters in the spatial domain. Then each distributed cluster can perform data
accuracy at the cluster head node and finally send the data to the sink node.
Findings- Simulation result shows the associate sensor nodes of each
distributed cluster and clarifies their data accuracy profile in the spatial
domain. We demonstrate the simulation results for a single cluster to verify
that their exist an optimal cluster which give approximately the same data
accuracy level achieve by the single cluster. Moreover we find that as the
distance from the tracing point to the number of sensor node increases the data
accuracy decreases. Design Limitations - This model is only applicable to
estimate data accuracy for distributed clusters where the sensed data are
assumed to be spatially correlated with approximately same variations.
Practical implementation - Measure the moisture content in the distributed
agricultural field. Inventive/Novel idea- This is the first time that a data
accuracy model is performed for the distributed clusters before data
aggregation at the cluster head node which can reduce data redundancy and
communication overhead.
