A Cluster-Based Data Fusion Technique to Analyze Big Data inWireless Multi-Sensor System
With the development of the latest technologies and changes in market demand, the wireless multi-sensor system is widely used. Thesemulti-sensors are integrated in away that produces an overwhelm-ing amount of data, termed as big data. The multi-sensor system creates several challenges, which include getting actual information frombig datawith high accuracy, increasing processing efciency, reducing power consumption, providing a reliable route toward destination using minimum bandwidth, and so on. Such shortcomings can be overcome by exploiting some novel techniques, such as clustering, data fusion, and coding schemes. Moreover, data fusion and clustering techniques are proven architectures that are used for efcient data processing; resultant data have less uncertainty, providing energy-aware routing protocols. Because of the limited resources of the multi-sensor system, it is a challenging task to reduce the energy consumption to survive a network for a longer period. Keeping challenges above in view, this paper presents a novel technique by using a hybrid algorithm for clustering and cluster member selection in the wireless multi-sensor system. After the selection of cluster heads and member nodes, the proposed data fusion technique is used for partitioning and processing the data. The proposed scheme efciently reduces the blind broadcast messages but also decreases the signal overhead as the result of cluster formation. Afterward, the routing technique is provided based on the layered architecture. The proposed layered architecture efciently minimizes the routing paths toward the base station. Comprehensive analysis is performed on the proposed scheme with state-of-the-art centralized clustering and distributed clustering techniques. From the results, it is shown that the proposed scheme outperforms competitive algorithms in terms of energy consumption, packet loss, and cluster formation.
Data fusion, big data, clustering, multi-sensors, layered architecture