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Figure 4-4 shows the complete program The vectorized cannonball study produced essentially the same results as the repeated-run study, as expected One thousand repeated runs and the statistics computation took 5 s on a 24-GHz Athlon64, and the equivalent vectorized study took 34 s This speed advantage is typical for small models The repeated-run overhead saved by vectorization becomes less significant as the model size increases

At first glance E-Pizza has exactly what ihe experiments of Pizza2Go are lacking so far, a good looking front end and a few concepts that die Wonder Troops have not even considered Admittedly, they have only worked on experiments that were designed for use of the internal operators of Pizza2Go The fundamental difference is that this is a true web site that can take orders over the Internet, and the more channels the better The team decides to take a little tour of the web site and learn from it They start from the home page, which can be seen in the following figure

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of techniques as compared with the problem of having the node securely report its location to some other entity inside the network or to the problem of guaranteeing the privacy of the location information of a node In the rest of this section we will consider secure location determination The secure localization techniques are based either on the principle of range dependent localization or on the principle of range independent localization or on a hybrid of the two We start off by looking at a technique that addresses some (though not all) of the security vulnerabilities present in the time-of- ight technique Following these techniques we will also study a scheme proposed for location veri cation We will not look at location privacy in this chapter

They first realize thai in order to be able to do business with E-Pizza, the visitor is required to register by clicking the REGISTER link to move along The following figure shows how they complete the registration operation:

FIGURE 4-3 This repeated-run Monte Carlo study determines the impact-range dispersion of a 1776 cannon shot due to random elevation-setting errors A track-hold difference equation (Section 2-16b) holds the impact coordinate

After successfully registering, the team moves on to explore the MENU link, displaying the standard range of pizzas This is shown in the next screenshot:

6

(b) Interactive Monte Carlo Simulation: Computing Time Histories of Statistics with Compiled DOT Operations

The distance bounding protocol was rst introduced in [155] This protocol addresses a vulnerability in the two-way mode of the time-of- ight technique We have seen in Section 72112 that the two-way mode of the time-of- ight technique is vulnerable to distance reduction attack when an adversary re ects back the sequence of bits transmitted by the rst party This is possible due to the lack of source authentication To prevent the above problem, the time-of- ight technique will have to be enhanced to provide for authentication resulting in a protocol exchange such as given in Figure 712 In order to explain this protocol we consider two parties, Alice and Bob, who need to determine their mutual distance Alice is the claimant and Bob is the veri er since Bob wishes to verify or determine the distance (not location) of Alice In the rst step of this protocol, Alice commits to a random value by hashing it17 Note that other modes of commitment are also possible The hashed value is then transmitted by Alice to the veri er, Bob Bob replies with a challenge nonce transmitted in reverse bit order As soon as the last bit of the

The Wonder Troops are surprised that as well as offering the standard pizzas, E-Pizza also allows the customer to create their own pizza by selecting from a variety of toppings, a real departure in comparison to the way Pizza2Go has done business so far This procedure is illustrated in the following figure:

And if that was not enough, you can also choose a beverage to accompany the order, as is shown below:

Vectorized Monte Carlo simulation has another interesting and important feature Since a single simulation run samples all n replicated models at each point of time, one can compute and display time histories of statistics, and observe the results of parameter changes as the simulation run proceeds Such interactive Monte Carlo simulation was formerly possible only with very fast (and very inaccurate) analog computers [7] Runtime statistics computations are needed only at output-sampling times, not at every derivative call We will thus save time by programming DYNAMIC-segment statistics computations following an OUT or SAMPLE m statement (Section 1-6) As noted in Section 4-6a, most statistics are functions of sample averages A vectorized DYNAMIC program segment computes the sample averages

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