DACA: Data-Aware Clustering and Aggregation in Query-Driven Wireless Sensor Networks

Data aggregation is an effective technique which is introduced to conserve energy by reducing packet transmissions in wireless sensor networks (WSNs). In addition, it is possible to consume less energy by using the spatial correlation and redundancy of data in dense networks to form clusters of nodes sensing similar values and, in turn, transmit one data packet per cluster. In this paper, we propose a Data-Aware Clustering and Aggregation scheme (DACA) to manage the energy constraint in a query-driven WSN. The DACA selects cluster head nodes by forming a new factor as a function of three parameters including the residual energy, the data value, and the number of neighbors at each node. Moreover, it exploits a cluster merging method to overcome the problem of the previous studies in which all sensor nodes become cluster heads over time, so it prolongs the network lifetime. Extensive simulations in NS-2 verify the superiority of our approach.