3-D spatial correlation model for reducing the transmitting nodes in densely deployed WSN

Abstract

In Wireless Sensor Networks, a large number of sensor nodes are distributed in the monitoring area to increase fault tolerance, coverage and communication range. In highly dense network, many nodes belong to common sensing region and record almost similar data of the event. Base station, however, can also identify the event features from data of a few representative nodes of the sensing region. The battery power of some sensor nodes may be saved by not sending multiple copies of the sensed information. In order to reduce transmitting nodes from the sensing region, an analytical model is presented to segregate the whole network into group of correlated regions. The minimum number of transmitting nodes are selected from probability based deployment of sensor nodes in 3D scenario and rest of the nodes are operated in sleep mode for saving the battery power. Effectiveness of proposed models is demonstrated with established technique of CHEF i.e. Cluster Head Election using Fuzzy Logic. Results show that number of nodes transmitting data from sense region can be reduced considerably with respect to threshold correlation value $\left(\xi \right)$, which results in the energy saving of additional nodes and enhancement of network life. With implementation of proposed models, at $\xi \le 0.5$, maximum transmitting nodes are 87% which saves battery power of at least 13% nodes.