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Robust aggregation in sensor network: an efficient frequent itemset and number of occurrence counting.
| Content Provider | CiteSeerX |
|---|---|
| Author | Kayalvizhi, S. Vanitha, K. |
| Abstract | Sensor networks are collection of sensor nodes which co-operatively send sensed data to base station. As sensor nodes are battery driven, an efficient utilization of power is essential in order to use networks for long duration hence it is needed to reduce data traffic inside sensor networks, reduce amount of data that need to send to base station. The aim of the project is to develop scalable aggregation methods to extract useful information from the data the sensors collect. Partitioning large set of data, for the result of horizontal aggregation, in to homogeneous dataset is important task in this system. Association rule apriority algorithm using SQL is best suited for implementing this operation. In this project we consider the PIVOT operator which is a built-in operator in a commercial DBMS. Since this operator can perform transposition it can help evaluating horizontal aggregations, our proposal though the list of distinct to values must be provided by the user, whereas ours does it automatically, output columns are automatically created. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Sensor Network Efficient Frequent Itemset Occurrence Counting Robust Aggregation Sensor Node Horizontal Aggregation Homogeneous Dataset Important Task Long Duration Hence Pivot Operator Efficient Utilization Built-in Operator Useful Information Commercial Dbms Large Set Scalable Aggregation Method Association Rule Apriority Data Traffic Output Column |
| Content Type | Text |
