Abstract
Sensors are being used in thousands of applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control. As these applications collect zettabytes of data everyday sensors play an integral role into big data. However, most of these data are redundant, and useless. Thus, efficient data aggregation and processing are significantly important in reducing redundant and useless data in sensor-based big data frameworks. Current studies on big data analytics do not focus on aggregating and filtering data at multiple layers of big data frameworks especially at the lower level at data collecting nodes (sensors) that reduce the processing overhead at the upper layer, i.e., big data server. Thus, this paper introduces a multi-tier data aggregation technique for sensor-based big data frameworks. While this work focuses more on data aggregation at sensor networks. To achieve energy efficiency it also demonstrates that efficient data processing at lower layers (sensor) significantly reduces overall energy consumption of the network and data transmission latency.
Highlights
As sensors-based big data aggregation is an important area of research to reduce computational cost as well as energy consumption this paper introduces a sensor data aggregation approach for a multi-tier big data framework
We introduced a sensor-based big data aggregation approach in this paper
Experimental results demonstrate that the proposed hybrid and dynamic data aggregation scheme is better than traditional cluster and tree-based schemes in terms of network energy consumption, network lifetime and data transmission latency
Summary
As the proposed communication framework only consists three layers of communication and processing devices (i.e., sensors, gateway node that connects to Internet, and big data server) this data aggregation approach has three layers. Clustering is used in most sensor network applications especially, they are greatly required for emergency or real-time applications such as rescue operations, health, and traffic monitoring to reduce data transmission latency (results in reduced data processing delay and overhead at big data server). The proposed approach works by selecting a few nodes that work as active nodes [19] to collect and aggregate data for a certain period of time unless the residual energy of these nodes become critical.
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