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evaluation shows the advantage that can be achieved by means of such a way of selecting aggregation points with regard to more empirical approaches The paper also points out how delay of data fusion should be rewarded by a signi cant amount of aggregated data, so lower or higher delay spent at aggregation points should depend on the amount of data that can be fused at that aggregation point In [117], the authors assume a set of aggregation points and address the problem of setting the timeouts at the different aggregation points encountered along the sourcedestination paths The assumptions made are the periodic transmission of data from the source to the sink and the adoption of a tree-based converge-casting.

Solvated Electrons In strongly basic solvents like HMPA, amines and liquid ammonia, solvated electrons are relatively stable. In these solvents, if the supporting electrolyte is the salt of Li+ or Na+, blue solvated electrons, e , are generated from the surface of the SH platinum electrode polarized at a very negative potential:

Three different approaches are compared: 1) periodic simple, in which all the packets received within time T are aggregated, 2) periodic per hop, in which an aggregation point waits to receive data from all its children in the dissemination tree before it sends the aggregated packet out (recall the traf c is periodic), and 3) the proposed approach in periodic per hop adjusted In this latter approach, the authors envisage the use of cascading timeouts Not only do timeouts depend on the aggregation point position in the dissemination tree but a node timeout will happen just before its parent In this way, a cascading effect (ie.

2 Blitz++ is a C++ class library for scientifi computing which provides performance on par with Fortran 77/90. See http://www.oonumerics.org/blitz for details.

data originating at the leaves will be clocked out rst, reaching nodes in the next tree level in time to be aggregated at that level together with the data originated at that level and so on) reduces the overall delay to reach the sink Comparative simulations among the different schemes show that, as expected, a cascading timeout-based scheme reduces delay without impacting the resulting received data accuracy Similar ideas have been reported in [133] for the case in which the WSN traf c is event-based.

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The authors discuss the potential inef ciencies associated with data fusion in the case where timers are not synchronized: though the packets sent by sources are generated at similar times as they detect the occurrence of an event, unsynchronized settings of the aggregation point timers may result in an aggregation point at level i not being able to gather information from the nodes at level i + 1 before performing aggregation To avoid this problem, each aggregation point starting its timer informs its neighbors, also triggering their timer start, and the timer s duration depends on the aggregation point level in the tree The solution proposed exploits synchronization and different timer settings at the different levels to allow a node at level i to gather information from the previous levels before its timer clocks out.

It is also possible to produce optics giving negative phase contrast, where the S wave is retarded relative to the D wave, causing high-refractive-index objects to appear bright against a gray background. In this case, the phase plate contains an elevated ring that retards the phase of the 0th-order S wave relative to the phase of the D wave. The effect of this action in generating negative phase contrast is shown on the right-hand side of Figure 7-8.

In [19], it is proposed that clustering to select cluster heads as aggregation points is exploited In addition, the paper proposes assigning an interval of possible values to each metric of interest and to select critical values for each measured metric A pattern code is then computed based on the measured values and sent to the cluster head The cluster head uses this aggregated implicit information of the data gathered by the sensor node to deduce whether some of the data to transmit are redundant and to avoid redundant transmissions before they occur Redundancy is thus eliminated by a pattern code exchange between the sensor nodes and their cluster head and by the cluster head deciding based on such codes, which sensor should send the data This.