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In a recent work [18], a correlation-based MAC protocol has been proposed which exploits the spatial correlation between closely located sensor nodes that regulate medium access and prevent redundant transmissions from closely located sensors. Based on the spatial correlation among sensor nodes, the MAC protocol collaboratively regulates medium access so that redundant transmissions from correlation neighbors are suppressed. In addition, necessary mechanisms for the ef cient transmission of the information from the sensor nodes to the sink have also been incorporated into the proposed correlation-based MAC solution. The experimental results in reference 18 reveal that signi cant performance gains and energy savings are obtained by exploiting spatial correlation in sensor networks. 5.4.2 Spatiotemporal Correlation and Reliable Event Communication In order to realize the potential gains of the WSN, it is imperative that desired event features are reliably communicated to the sink. Unlike traditional communication networks, the sensor network paradigm necessitates that the event features are estimated within a certain distortion bound (i.e., required reliability level) at the sink as discussed in Section 5.2. Reliable event detection at the sink is based on collective information provided by source nodes and not on any individual report. Hence, conventional endto-end reliability de nitions and solutions are inapplicable in the WSN regime and would only lead to overutilization of scarce sensor resources. On the other hand, the absence of reliable transport altogether can seriously impair event detection, which is the main objective of WSN deployment. Hence, the WSN paradigm necessitates a collective event-to-sink reliability notion rather than the traditional end-to-end notion [19]. Such event-to-sink reliable transport notion based on collective identi cation of spatially and temporally correlated data ows from the event to the sink is illustrated in Figure 5.4 and depends on the following de nitions:



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Imports and exports are the mechanisms that enable the dynamic linking process of executables described earlier. Consider an executable that references functions in other executables while it is being compiled and linked. The compiler and linker have no idea of the actual addresses of the imported functions. It is only in runtime that these addresses will be known. To solve this problem, the linker creates a special import table that lists all the functions imported by the current module by their names. The import table contains a list of modules that the module uses and the list of functions called within each of those modules. When the module is loaded, the loader loads every module listed in the import table, and goes to find the address of each of the functions listed in each module. The addresses are found by going over the exporting module s export table, which contains the names and RVAs of every exported function. When the importing module needs to call into an imported function, the calling code typically looks like this:





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The MCE training algorithm is a linear classification algorithm, of which the decision boundaries generated are straight lines The advantage of linear classification algorithms is their simplicity and computational efficiency However, linear decision boundaries have little computational flexibility and are unable to handle data sets with concave distributions SVM is a recently developed pattern classification algorithm with nonlinear formulation It is based on the idea that the classification that affords dot-products can be computed efficiently in higher dimensional feature spaces [15 17] The classes which are not linearly separable in the original parametric space can be linearly separated in.

Figure 5.4. Typical sensor network topology with event and sink. The sink is only interested in collective information of sensor nodes within the event radius and not in their individual data.

call [SomeAddress]

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De nition 1 The observed event distortion Di is the distortion achieved [ie, as in (523)] when the sink performs estimation of the signal S being tracked in decision interval i De nition 2 The desired event distortion D is the maximum distortion allowed to assure reliable event detection in the estimation performed by the sink This upper bound for the distortion level is determined by the application and based on the physical characteristics of the signal S being tracked Based on the packets generated by the sensor nodes in the event area, the sink estimates the event features to determine the necessary action and observes Di at each decision interval i Note that a distortion level D for the estimation of event features from the sensor observations corresponds to the reliability level of the eventto-sink communication in the WSN.

Where SomeAddress is a pointer into the executable import address table (IAT). When the modue is linked the IAT is nothing but an list of empty values, but when the module is loaded, the linker resolves each entry in the IAT to point to the actual function in the exporting module. This way when the calling code is executed, SomeAddress will point to the actual address of the imported function. Figure 3.4 illustrates this process on three executables: ImportingModule.EXE, SomeModule.DLL, and AnotherModule.DLL.

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