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Figure 6.4 improves on the simplistic modeling of future DC accumulations by allowing the future investment returns to be more realistically modeled. Using a Monte-Carlo simulation, we have projected the DC plan account balances into the future. Under the simplistic approach above, we assumed that every year the account would earn 7 percent. Here, we allow the future returns to vary every year. We put in the expected mean and variance of different asset classes. The computer then produces 5000 future possible return scenarios basically rolling the dice 5000 times. This produces, instead of a single line, a set of lines. With this information, you can answer questions like,

[ Team LiB ]

These techniques can be used mainly to protect the Position Computation component, since information gathered from untrusted nodes can simply be ignored when computing the position of the nodes For instance, in Liu et al [28] it is proposed a set of techniques for detecting malicious beacon nodes One technique detects malicious beacon nodes by comparing the distance estimated by using the location information provided by these beacon nodes and the distance estimated by means of the signal (eg, RSSI, TDoA, AoA) Another technique evaluates the round-trip time (RTT) between two neighbors, based on the observation that the replay of a (malicious) beacon signal introduces extra delay Then, the base station (or sink node) uses such information about malicious beacons to reason about the suspiciousness of each beacon node, and then it lters out malicious beacon nodes accordingly Srinivasan et al.

[ Team LiB ]

[29] extends the techniques proposed by Liu et al [28] by using a continuous scale and a reputation- and trust-based mechanism The result is the Distributed Reputation-based Beacon Trust System (DRBTS), which is a distributed security protocol for excluding malicious beacon nodes In DRBTS, each beacon node monitors its neighborhood for suspicious beacon nodes and provides information by maintaining and exchanging a Neighbor-Reputation Table, in such a way that other sensor nodes can choose trustworthy beacons based on a voting approach 1843 Robust Position Computation Robust position computation can be used in order to increase the security of a localization system This technique can be used when both cryptography and misbehavior detection is compromised.

In this case, a way to deal with malicious nodes is to accept that they will be present in the network and propose robust position computations that can still work in the presence of bogus information This is done mostly by using statistical and outlier ltering techniques In these cases, it is assumed that the benign nodes outnumber the malicious ones These techniques are used to protect against (or be robust to) attacks on the Position Computation and Distance/Angle Estimation components A technique proposed by Li et al [30] uses the principle behind the least squares data fusion technique to propose an adaptive least squares and least median squares.

What is the probability that the DC plan will be as good as the DB plan What is the median value of the DC plan that point with 50 percent chance above and 50 percent chance below What is the 25th percentile value of the DC plan Or the 75th percentile

<extension point="orgeclipsecoreresourcesmarkers" id="failure" name="Test Failure"> <super type="orgeclipsejdtcoreproblem"/> <persistent value="true"/> </extension> The test passes Notice that the marker id is plug-in relative Markers can persist across sessions or not This is enabled by the persistent attribute A failed test remains a failed test even if you restart the workbench, so we enable persistence Our marker is shown in Figure 163

position estimator The idea is to use the least squares in the absence of attacks and the least median squares in the presence of attacks, since the latter alternative tolerates up to 50% outliers and still provides correct estimates The authors show that the use of traditional Euclidean distance is not robust to intentional attacks against base stations, and they introduce robustness to ngerprinting localization by means of a median-based distance metric Liu et al [31] propose a method that uses the MMSE (Minimum Mean Squared Estimation), which is a data fusion technique for obtaining improved estimations, to identify and remove malicious location information In this method, sensor locations are estimated using the MMSE-based method Then, the method veri es whether the estimated location can be estimated from a set of consistent location references.

The task filter now shows our marker type as part of the Java problem marker hierarchy, as shown in Figure 164