Data redundancy reduction scheme for data aggregation in wireless sensor network - Adawy, Mohammad Ibrahim (2020)
Wireless Sensor Network (WSN) is a set of sensor nodes that are densely and randomly deployed where the sensor nodes are not situated faraway from each other. Thus, an overlapping area is generated due to overlap their sensing ranges. If an event occurs within the overlapping area, all sharing nodes sense same event and produce redundant data. Data redundancy exhausts network resources and increases communication overhead. Data aggregation methods and techniques have been employed in WSN such as clustered-based data aggregation to eliminate redundant data. However, many issues are explored in the clustered-based data aggregation and reduced data aggregation efficiency. Therefore, several studies have employed some schemes to reduce data redundancy in the clustered network before data aggregation to mitigate the problems that affects the data aggregation efficiency. This research proposes Data Redundancy Reduction Scheme (DRRS) which includes three algorithms namely, Metadata Classification (MC), Selection Active Nodes (SAN) and Anomaly Detection (AD) algorithms that works before data aggregation, when multiple composite events simultaneously occur in the different locations within the cluster. The aim to design MC and SAN algorithms is to increase data aggregation efficiency in terms of energy consumption, End-to-end delay, whereas the aim to design AD algorithm is to conserve aggregated data accuracy. Network simulator OMNeT++ is used to simulate DRRS and it is evaluated with Low-Energy Adaptive Clustering Hierarchy (LEACH), Redundancy elimination Energy-Efficient Routing Protocol (REERP) and Fault-Tolerant Data Aggregation (FTDA). The results show that DRRS outperforms LEACH and REERP in terms of Cluster Head (CH) energy consumption and End-to-end delay in which the CH node depletes 61.01% and 31.62% of it’s battery energy in the REERP and DRRS schemes, respectively. Also, the results show that the proposed Anomaly Detection (AD) outperforms FTDA in terms of aggregated data accuracy in which the AD conserved approximately, 59.5% of aggregated data accuracy for event compared with FTDA algorithm which conserved 54.25% of aggregated data accuracy for event. DRRS helps monitoring applications in WSN by extending the CHs lifetime, assist the CHs to detect multitargets in quick manner and to make accurate event decision about event occurrence.