In Network Processing and Data Aggregation in

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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
consumes more energy than the initial sensor (as an accelerometer triggering a GPSreceiver), whereas in the case of network traffic a triggered imager for example generatesmuch less traffic than a constant video stream. In other words, in-network processingmight choose the best application of a sparse resource (for example, a motion sensortriggering a steerable camera).COUGAR [5], [6]In order to enable declarative querying of sensor networks, in [5],[6] is proposed aquery layer consisting of a query proxy on every sensor node. Concerning thearchitecture of the sensor node, the query proxy lies between the network layer and theapplication layer and it provides higher-level services using queries that can be injectedinto the network from a specified gateway node.Cougar, enables the user to parameterize the queries. A complex query may not onlyconsist of a large number of parameters and operators but also various user requirementson the query answers, such as specification of a maximum permissible latency andaccuracy of the query result.Also, the query proxy is responsible for the data aggregation. Part of the computationcan be moved from a location outside the network and pushed into the sensor network,aggregating records, or eliminating irrelevant records. Compared to traditionalcentralized data extraction and analysis, in-network processing can reduce energyconsumption and improve sensor network lifetime significantly. This is the reason whyone of the main roles of the query proxy when processing user queries is to perform innetworkprocessing.Nevertheless, COUGAR has some drawbacks as well. First, the addition of querylayer on each sensor node may add an extra overhead in terms of energy consumptionand memory storage. Second, to obtain successful in-network data computation,synchronization among nodes is required (not all data are received at the same time fromincoming sources) before sending the data to the leader node. Third, the leader nodesshould be dynamically maintained to prevent them from being hot-spots (failure prone).As an example, suppose that we have a long-running query Q to monitor the averagetemperature of an office every t seconds. The query Q notifies the administrator of thenetwork if the average temperature in the office is greater than a user-defined threshold,
Page 1: Athens University of Economics and
Page 4 and 5: 1 IntroductionRecently, the technol
Page 7 and 8: Thus, in-network processing is one
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Page 13 and 14: 2.1.3 Proposed schemesSeveral syste
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Page 19 and 20: o CSMA/CA - Is also an extension of
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Page 24 and 25: amount of noise to the channel for
Page 27 and 28: wild animals are monitored inside a
Page 29 and 30: than the whole network (GAF, GEAR).
Page 31 and 32: In the data-centric approach we ass
Page 33 and 34: 3 Data Aggregation in WSNs3.1 Gener
Page 35 and 36: 2. Less packet collisions.Packet co
Page 37: Exemplary aggregates, on the other
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Page 45 and 46: of communication failures because e
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Page 49 and 50: Benefits1. It is a completely distr
Page 51 and 52: application can tolerate big estima
Page 53 and 54: REFERENCES[1] W. Ye, J. Heidemann,

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