Probabilistic Data Aggregation in Information-based Clustered Sensor Network

Efficient data aggregation helps in achieving maximum performance for complex interactive and sensing applications. In our proposed work, we have considered a heterogeneous sensor network which is partitioned into clusters. Each cluster is further divided into smaller information-based groups (also called similar groups) by the local processing center (LPC). The LPC acts as a cluster head and computes the probabilistic similarity of sensors by exploiting resemblance in the pattern of their sensed data. It further schedules the active and sleep duration of sensor nodes, such that only single node from every group remains active to participate in the aggregation cycle. The LPC gathers multi-characteristic dataset from the active clustered nodes and processes them by using probabilistic aggregation model. The proposed model normalizes the multi-characteristic data for deriving relative weights of each of the characteristic attribute. Finally, the aggregated information is transmitted to the global processing center. Our protocol is evaluated with wide range of performance metrics which includes aggregation gain, information accuracy, aggregation miss ratio, network lifetime and energy consumption.