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The DEV should keep a list of the DEVs with which it will communicate Data encryption can be done on beacon payloads, command payloads, and data payloads This is typically done by using a key shared by a certain group of DEVs Frame integrity works with the data encryption to ensure that frames being received are actually coming from the group of DEVs with the cryptographic key Sequential freshness is a service that will reject frames that have been replayed When frames are read, a freshness value is given to the frame This value is compared to identical frames in order to tell which one is the most upto-date frame 13362 Security Modes Based upon the security mode, the MAC layer provides security services There are three modes: unsecured, asynchronous connectionless (ACL), and secured In the unsecured mode, there are no security services.

Clearly, our estimates of the distance traveled would be degraded, on the whole, since less information usually results in less accurate estimates Because we lack access to the predictor information, our best estimate for y is simply y , the sample mean of the number of hours traveled We would be forced to use y = 16 to estimate the number of kilometers traveled for every competitor, regardless of the number of hours that person had traveled Consider Figure 22 The estimates for distance traveled when ignoring the time information is shown by the horizontal line y = 16 Disregarding the time information entails predicting y = 16 kilometers for the distance traveled, for orienteering competitors who have been hiking only 2 or 3 hours, as well as for those who have been out all day (8 or 9 hours) This is clearly not optimal.

In the ACL mode the upper layer provides the services to the DEV This mode essentially makes sure that the sending and receiving DEVs belong to the same cryptographic key group If a DEV is in the secure mode, the MAC layer can provide any of the services from the previous section These services are access control, data encryption, frame integrity, and sequential freshness..

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The data points in Figure 22 seem to cluster tighter around the estimated regression line than around the line y = 16, which suggests that overall, the prediction errors are smaller when we use the x-information than otherwise For example, consider competitor 10, who hiked y = 25 kilometers in x = 9 hours If we ignore the x-information, the estimation error would be y y = 25 16 = 9 kilometers This prediction error is indicated as the vertical line between the data point for this competitor and the horizontal line, that is, the vertical distance between the y observed and the y = 16 predicted..

increasing numbers of adults. For ma 0:04 we have high equilibrium; for ma 0:27 we have two cycles; for ma 0:73 we have low equilibrium; and for ma 0:96 we have an aperiodic behavior [13]. We use ma 0:96 to identify this model. This unknown nonlinear system has the standard form (8.1) without using input u k 1 . We use the recurrent neural network given in (8.2) and (8.4) or (8.2), (8.5), and (8.6) without using ^i;k 1 to identify it, where x k x1 k ; x2 k ; x3 k T , g ^ x k 2 R3 , S 2 R3 3 . We select S diag 0:1 , which is a stable diagonal matrix. The parameters of the ENFRN are Pi;1 diag 1 105 2 R3 1 3 1 , r 0:9, and du 4. We compare the ENFRN without a pruning algorithm with the ENFRN with the pruning algorithm given in this chapter based on the population density. The identi cation results for x1 k are shown in Figure 8.8. The comparison results for the identi cation error are shown in Figure 8.9 and the growth of the hidden neurons are shown in Figure 8.10, denoted by m1. In Figures 8.8 through 8.10, it is seen that the proposed pruning algorithm given in this chapter provides an improvement to the result with a guaranteed stability. The result is more compact and the error is smaller.