In Network Processing and Data Aggregation in

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A different method is used in TAG [10] to represent a user query is an SQL-likelanguage (Figure 11). The query is always performed over a single table called “sensors”.SELECT AVG(volume),room FROM sensorsWHERE floor = 6GROUP BY roomHAVING AVG(volume) > thresholdEPOCH DURATION 30sFigure 11Regarding the duration of the execution a query it is usually defined within its body(In Figure 10 the duration attribute and in Figure 11 the EPOCH clause).A query may be executed many times periodically (periodic, long-running) or it isexecuted only one time and returns just a snapshot of the current state of the network(snapshot queries).3.3 Aggregation operatorsAuthors of TAG [10] propose a nice taxonomy of the aggregation operators by usingseveral criteria:1. Duplicate sensitivity.This property specifies whether an aggregate function will returnthe same result when the dataset contains duplicate values. Examplesof duplicate sensitive aggregates are MEDIAN, AVERAGE, and COUNT.Examples of duplicate insensitive aggregates include MIN, MAX, andCOUNT DISTINCT.2. Exemplary/Summary.Exemplary aggregates always return a representative value presentin the dataset while summary aggregates perform some calculationover the entire dataset and return the calculated value. Summaryvalues (such as AVERAGE and COUNT) are more easily estimated evenin a network with losses, where all data packets are not received.
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
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Page 53 and 54: REFERENCES[1] W. Ye, J. Heidemann,

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