  Objective-The main purpose of this paper is to construct a data accuracy
model for the maximal set of sensor nodes that sense a point event and forms a
cluster with fully connected network between them. We determine the minimal set
of sensor nodes that are sufficient to give approximately the same data
accuracy achieve by the maximal set of sensor nodes. Design
approach/Procedure-L set of sensor nodes are randomly deployed over a region Z.
Since a point event S has occurred in the region Z, M maximal set of sensor
nodes wake up and start sensing the point event. The set of M sensor nodes
forms a cluster with fully connected network and remaining set of sensor nodes
continue to be in sleep mode. One sensor node is elected randomly as a cluster
head (CH) node which can estimate the data accuracy for the cluster before data
aggregation and finally send the data to the sink node. Findings - Since we
simulate the data accuracy for the cluster (M set of sensor nodes) at CH node,
there exist P minimal set of sensor nodes which give approximately the same
data accuracy level achieve by M set of sensor nodes .Moreover we find that as
the distance from the point event to the number of sensor nodes increases, the
data accuracy also get decreases. Design Limitation -This model is only
applicable to estimate data accuracy for the point event where the sensed data
are assumed to be spatially correlated with approximately same variations.
Practical implementation-Detect point event e.g. fire in forest.
Inventive/Novel idea - This is the first time that a data accuracy model is
performed for the cluster before data aggregation at the CH node which can
reduce data redundancy and communication overhead.
