In Network Processing and Data Aggregation in

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processing is needed for this step, the node enters again the sleeping mode [11]. Whilethis approach is satisfactory suitable for ideal network conditions, Madden et al. [10]proposed some methods with the goal of improving the performance of their system. Onesolution is to cache previous values and reuse them if newer ones are unavailable but ofcourse, the previous values may reflect losses at lower levels of the tree.Pipelined Aggregation [7]The authors of [7] proposed a fully-pipelined approach for aggregation. Also, as thisapproach is an ancestor of TAG, it has many similarities with it. Both of them assumethat a sensor network is a distributed database. Furthermore, it implements a generalpurpose,SQL-style interface that can execute queries over any type of sensor data whileproviding the abilities for significant optimization.Each sensor possesses a unique id that is used just for local interactions and not inorder to route the sensed data back to the sink as expected. It does not use a data-centricrouting mechanism but it forms a routing tree upon query distribution, just as the casewas in TAG. Again, one sensor plays the role of the root point, upon which, aggregateddata will converge and also it is the interface of the querying user for the rest of thenetwork.The computation of an aggregate consists of two phases: the propagation phase, inwhich aggregate queries are pushed down into sensor networks, and the aggregationphase, in which the aggregate values are propagated up from children to parents. Whiledata flow back to the top of the tree (to sink/root), all the sensors that have any childrenmust wait for their responses before computing the local aggregate and then they canforward it to its own parents. There is a timeout t for waiting to hear a propagate messagefrom a child to bound in-network delay. Choosing a small value for t may result inmissed reports from children. Also, it has to be noted that the proper value of t varies,depending on the depth of the routing tree.This simple approach would work fine if the nature of a WSN was not unreliable. Theunreliability affects the accuracy of the returned aggregated results/values. Here comesthe pipelined aggregation to give the solution. The propagation of aggregates is done asdescribed above except that time is divided in intervals of duration i. During each iinterval:
a) Sensors that heard aggregate request start to transmit to their parent a fused valueof:• the partial aggregate they have already received• the aggregates that received by their children• their local readingb) After the elapse of the first i-duration, the root will have received messages thatcontain aggregates from sensors that are only 1-hop away. After the elapse of the 2ndduration the root will have received messages with aggregates from sensors that are 2-hops away and so on.In the pipelined approach that was described above, a new aggregate arrives every iseconds. In this manner, the user that injected the query can have a first draft estimationof the value of the aggregate (faster first response) whereas every i seconds the user willget a more accurate result. Consequently, the technique of the pipelined aggregationprovides users with a stream of aggregate values that changes, as sensor readings and theunderlying network change.The most significant disadvantage of this approach is that a number of additionalmessages are transmitted, in order to extract the temporary aggregate values every i-timeinterval over all the sensors of the network. However, despite these negative effectssimulation shows that this technique generally improves the robustness as well as thethroughput of the network.In [7] several optimizations are proposed:1) Snooping: Takes advantage of the shared radio channel through which everymessage is broadcasted to the other sensors of the network within the range. When asensor misses an initial request to aggregate, it may start to initiate the aggregationactivity whenever it listens to another sensor, reporting an aggregate value to its ownparents. Going one step forward, a sensor does not need to explicitly tell its children tobegin aggregation. It can simply report its value to its parents, as this value will also beheard by its children. The children will assume they missed the start request and as aresult they will initiate aggregation locally. It is important to mention that Snoopingreduces the total messages required to compute the first full aggregate of the network fora total savings of 23%.
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
Page 11 and 12: The following figure (Figure 4) vis
Page 13 and 14: 2.1.3 Proposed schemesSeveral syste
Page 15 and 16: Medium access functionality is so i
Page 17 and 18: encodes the data before transmissio
Page 19 and 20: o CSMA/CA - Is also an extension of
Page 21: made a discussion over the issues t
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 and 38: Exemplary aggregates, on the other
Page 39 and 40: consumes more energy than the initi
Page 41: Tree. To be more specific, as the q
Page 45 and 46: of communication failures because e
Page 47 and 48: function SE() to translate its loca
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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