Two Level Data Fusion Model for Data Minimization and Event Detection in Periodic Wireless Sensor Network
Neetu Verma, Dinesh Kumar Singh · International Journal on AdHoc Networking Systems · 2020
Periodic Wireless Sensor Networks used for many applications such continuous Weather Monitoring, Cattle Monitoring, Water Quality Monitoring and Event Detection Applications.Due to continuous Monitoring, Large Amount of redundant data is transferred over the Network, which causes depletion of energy resources.Data Fusion is used with the Aim of deleting redundant, inconsistent data and provides more accurate data to the Sink.However, redundant data improves the data quality.Therefore, Two Level Data Fusion Model is proposed in this paper to transfer minimized data with the ability of accurately determines the event with minimum Delay.Data Fusion Model employs Cluster based Data Fusion at Two Levels; First Level at Sensor Node ( SNs) and another level at Cluster Head ( CH )Node.At the first level of Fusion, SNs Send single most common measurement to the CH by using a similarity function.Second Level of Fusion is applied on CH to remove similar multi attribute measurement.Also at CH Node, Multiple Correlation is used to accurately distinguish the abnormal event from the normal event or outliers within minimum delay.Experimental results are performed on real data to validate proposed Data Fusion Model is Better in terms of data transfer over Network, Redundancy, Energy Consumption over the Prefix Filtering Technique (PFF) .This Model is also used to accurately detect early events in case of Emergency.