In Network Processing and Data Aggregation in

energy costs could be reduced by aggregating this data as it returns to the user. Thisaggregation could be performed to a binary value (a detection exists), an area (a detectionexists in quadrant 2), or it can be application specific (seismic and infrared sensorsindicate 80% chance of detection).In spite of the fact that details of aggregation can be application-specific, a commonproblem is the design of mechanisms for the establishment of data dissemination paths tothe sensors within the region, as well as for the aggregation of responses. Let’s nowconsider the way in which this kind of data fusion may be implemented in a traditionalnetwork where low-level node names are topologically assigned. Firstly, a bindingservice must exist, so as to list the node identifiers of sensors within a given region, inorder to determine which sensors are present in that region. Once these sensors aretasked, an election algorithm must choose dynamically one or more nodes, to aggregatethe data and return the result to the querier.On the other hand, Directed Diffusion [3] faces this problem by using opportunisticdata aggregation. The selection as well as the tasking of sensors is accomplished bynaming nodes using geographic attributes. As data is sent from the sensors to the querier,the intermediate sensors of the return path are able and do identify and cache relevantdata with the aid of application-specific filters. Also, they can then suppress duplicatedata, by simply not propagating it, or they may slightly delay and aggregate data frommultiple sources.Opportunistic aggregation strategies benefit from filters in various ways as theyprovide a natural approach for the insertion of application-specific code into the network.The naming and matching of attributes, allow these filters to stay inactive until they aretriggered by relevant data. The significance of a common attribute set is that filters incurno network costs to interact with directory or mapping services.In [4] one can find more complex examples concerning in-network aggregation usingfilters. Another important issue is that nested queries (where one sensor cues another) canbe used for triggering as well as to reduce overall energy consumption significantly. Forexample, the entrance of a person in a room is often correlated with changes in light ormotion. When implementing multi-modal sensor networks there is the ability to use thesecorrelations by triggering a secondary sensor based on the status of another, or in otherwords by nesting one query inside another. The overall energy consumption as well asthe network traffic can be reduced, by reducing the duty cycle of some sensors. Forexample, in the case of the energy consumption this happens if the secondary sensor