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COs exhibit a strong ad hoc character Therefore, neither an infrastructure nor a base station can be assumed A decentralized key management system is, therefore, more practical Several approaches are based upon random pairwise key pre-distribution [35, 23] That is, before deployment for instance, by the sensor manufacturer every device is supplied with a random set of keys from a key pool After deployment, the devices try to establish connections by nding a commonly shared key or by creating a new key through a secure path including other devices Due to random pre-distribution, real authenticated communication between arbitrary devices is not always possible It can only be assured with some probability that two arbitrary devices are able to communicate in a secure way Moreover, an attacker could also reconstruct the complete key pool by compromising enough nodes.

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Determine the focal lengths of the lenses using the method described in the text and label them with pieces of lab tape applied to their edges. Use three 50 mm lenses for the objective, ocular, and illuminator s collector lens to construct the optical bench microscope. Mount in order of sequence on the optical bench: a tungsten lamp illuminator, 50 mm collector lens, specimen holder, objective lens, and ocular. Handwrite the letter a with a fine marker pen on a sheet of lens tissue or on a microscope slide, and tape it to the specimen holder, centering the letter on the optic axis. Position the collector lens about 75 mm away from the lamp filament. Position the specimen about 20 cm away from the collector lens. The light from the illuminator should be focused into a 1 2 cm diameter spot centered on the object (the letter a). Using the lens equation 1/f 1/a 1/b, calculate the object-lens distance that gives an image-lens distance of 30 cm, and mount the objective lens at the calculated position.

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EXHIBIT A9 Histogram of Distribution of Returns for MSFT and the Equivalent Normal Distribution for Each CDF Tail Risk: Lognormal Distribution The tail of the lognormal distribution shown in Exhibit A8 shows how different it is from the normal distribution for this region In credit value at risk modeling, this is the most critical part of the distribution Consider the following question: What is the probability that we have greater than + 2 defaults occurring in a portfolio of N different names when we assume a binomial distribution and when we assume a lognormal distribution For the example we gave, we had = 8 and = 271, so + 2 = 1343 Of course we can t have fractional defaults, so we can round up to 14 and put this value into our CDF equations for the binomial and lognormal distributions.

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If the key pre-distribution does not rely on a random set but on some algorithmic chosen set, the secure communication between arbitrary devices can be guaranteed In [125], such an approach is presented The authors suggest physical contact1 for establishing the initial key set: the manufacturer could pre-distribute this initial set of keys according to their algorithm In their approach, when two devices do not share a key yet, they can establish a new key by sending key-shares along node-disjoint paths via secured point-to-point communication to each other When all key-shares are available at both nodes (which want to establish a new key) they just have to perform, for instance, a bitwise XOR operation on all key-shares: the result of this operation is the newly unique key, shared only by those two devices.

We nd that P(x 14) = 133% for the binomial distribution, and P(x 14) = 299% for the lognormal distribution Modeling default events using a lognormal distribution rather than a binomial distribution results in more frequent scenarios with a large number of total defaults (eg, greater than + ) even though the mean and standard deviation are the same Though the choice of lognormal distribution may seem arbitrary, in fact, one credit portfolio model, called Credit Risk+ (see 4 for a more detailed discussion), uses the gamma distribution, which is much more similar to the lognormal distribution than the binomial distribution.

Beta Distribution: Modeling the Distribution of Loss Given Default One speci c application for the beta distribution has been in credit portfolio risk management, where in two popular commercial applications it is used to model the distribution of loss given default (LGD also called severity), on an individual facility basis or for an obligor or sector Exhibit A10 shows.

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