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the rings topology. Aggregation proceeds level-by-level, with level i + 1 nodestransmitting, while level i nodes are listening. In contrast to trees, the rings topologyexploits the wireless broadcast medium by forcing all level i nodes that hear a level i + 1partial result, to incorporate that result into their own. In that way, there is a significantincrease in robustness, because each reading is accounted for in many paths towards thebase station, and all would have to fail for the reading so as not to be accounted for in thequery result. As with trees, nodes can monitor link quality and level changes aswarranted. A key advantage of using a rings topology is that the communication error istypically very low, in stark contrast with trees. Moreover, the rings approach is as energyefficientas the tree approach (within 1%). Nevertheless, because each partial result isaccounted for in multiple other partial results, special techniques are required to avoiddouble-counting.Aggregation PhaseThe aggregate computation is defined by three functions on thesynopses:• Synopsis Generation:A synopsis generation function SG(arg) takes a sensor reading(including its metadata) and generates a synopsis representing thatdata.• Synopsis Fusion:A synopsis fusion function SF(arg1, arg2) takes two synopses andgenerates a new synopsis.• Synopsis Evaluation:A synopsis evaluation function SE(arg) translates a synopsis into thefinal answer.The exact details of the functions SG(), SF(), and SE() depend on theparticular aggregate query to be answered.During the aggregation phase, each node periodically uses the function SG() in orderto convert sensor data to a local synopsis and the function SF() so as to merge twosynopses to create a new local synopsis. For example, whenever a node receives asynopsis from a neighbour, it may update its local synopsis by applying SF() to itscurrent local synopsis and the received synopsis. Finally, the querying node uses the
function SE() to translate its local synopsis to the final answer. The continuous querydefines the desired period between successive answers, as well as the overall duration ofthe query.Double-Counting ProblemAs we have already mentioned, multipath techniques usually double count the samevalues because of the multiple parents of some nodes. Nevertheless, Synopsis diffusionavoids double-counting through the use of order and duplicate-insensitive (ODI)synopses that compactly summarize intermediate results during in-network aggregation.Main drawbackThe total number of massages that are exchanged between the sensor nodes during theaggregation phase is a lot higher comparing to that of a simple tree-based approach.Therefore, the “synopsis diffusion” approach is more robust-oriented that energyefficient.3.4.3 Distributed Localized AlgorithmsUsing Distributed Estimation [19]Boulis et. al in [19] proposed a distributed localized algorithm that explores theenergy/accuracy subspace for the periodic aggregation domain. They firstly separated thein-network processing algorithms in two different types.Processing Type-I: Includes all the “snapshot aggregation” solutions that calculate anaggregate by simply combining the values at multiple intermediate nodes until the finalvalue reaches the sink (this type includes all the approaches that have been reviewed sofar).Processing Type-2: Includes the approaches that take into account that the sensedvalues are just approximations of the real ones and moreover, they do not try to calculatean aggregate but they rather attempt to achieve a “good” estimation of it.
Page 1: Athens University of Economics and
Page 4 and 5: 1 IntroductionRecently, the technol
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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 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
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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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