In Network Processing and Data Aggregation in

Exemplary aggregates, on the other hand, may be highly inaccurate ifeven a few messages are lost. Such aggregates include MIN, MAX, andMEDIAN.3. Monotonic aggregates.Aggregates that allow early testing of predicates in the network aremonotonic. For example, assume the user requests the MAXtemperature reading in the network. As source nodes report theirvalues toward the host node, other nodes may listen and only reporttheir own values if they are greater than the current MAX. Thisprovides savings in the overall number of messages sent through thenetwork without affecting the result.4. Partial state requirements.The amount of partial state information required differs amongaggregate functions. Aggregates such as SUM and COUNT requirepartial state records that are the same size as the final aggregate. TheAVERAGE function requires a partial state record containing two values(both the SUM and COUNT). Other aggregates such as MEDIAN andHISTOGRAM require that the entire dataset be returned to the hostnode unless some type of compression or estimation is used (see [10]).3.4 A Taxonomy of Current Aggregation Approaches3.4.1 Tree-Based TechniquesLow level Naming – Filter Based [4]In order to illustrate the way in which in-network processing can reduce data traffic toconserve energy, an example of filter-driven data aggregation using Directed Diffusion ispresented below. The scope of an anticipated sensor application is to query a number ofsensors so as to be able to take some action when one or more of the sensors is activated.Take as an example a surveillance system which could notify a biologist if an animalenters a specific region. In order to ensure robust coverage, the coverage of deployedsensors will overlap and as a result, one event is likely to trigger multiple sensors of thenetwork. Every sensor will report detection to the user whereas communication and