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of the number of conferences, workshops and seminars I have attended where one or more of the presenters is a representative of an engineering or a video company who, for other reasons, have developed a clever bit of software or a valuable database and then thought what a good idea it would be to use it for running simulations Then, having designed the simulation, as an afterthought they begin to consider how they might superimpose some sort of assessment regime on the simulation Usually, in these cases, even less thought is given to whether the assessment has good technical characteristics or to whether the same assessment goal could not be achieved at half the cost in a third of the time using some other method With this in mind, it is interesting to note the trials and tribulations Melnick and Clauser report in the development of the MIDS simulation Almost all of the common lessons for simulator developers are there: Don t start with scenarios Do start with what it is possible to assess Think about the volume and quality of evidence that the simulation is capable of providing at the outset Decide in advance how you will score or measure performance Decide how stripped down you can make the simulation without losing quality, credibility or key performance information Don t assess everything you can the correlations between different measures are often so high that only one measure is needed Optimise the value you get from the simulation: - Don t fall back on tired old multiple choice assessment when there is the possibility of obtaining high quality performance evidence - Use the simulation for as many purposes as possible, eg training, task rehearsal, recruitment, competence assessment, etc



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The rst approach does not consider any speci c adversarial models Instead the assumption here is that some fraction of localization-related information generated during the ranging phase such as signal strengths or time of ight is corrupted The corruption can be either due to network faults or to malicious attacks Typically this approach proposes the use of statistical methods to lter out the corrupted data during the computation phase The location is then estimated by considering only a subset of data that is statistically expected to be trustworthy Thus, this approach generally focuses on ensuring the robustness of the computing phase of localization against corrupt data The second approach to secure localization is to propose techniques while considering speci c adversarial models This approach involves examining the effects of potential attacks that an adversary can launch against the proposed localization techniques and illustrating the strengths of the proposed techniques to withstand particular attacks We would like to remark here, though, that for some schemes the distinction between these two approaches is blurry Such schemes try to ensure that corrupt data generated during the ranging phase is detected and ltered out Thus, the data that the computation phase makes use of is guaranteed to be authentic with a high degree of probability Note that use of robust algorithms during the computation phase will strengthen the security of the entire system Secure localization consists of several subproblems other than secure location determination Such problems include location privacy and location reporting Location privacy aims to maintain the privacy of the location so that the granularity of the location information of a node made available to the various entities in the network can be controlled The objective of location reporting is to ensure that the nodes report their location information securely A secure location service may consist of one or more of these components Note that enabling a node to compute its location securely requires a different set.

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Standardisation and Equivalence of Forms As noted previously, standardisation and equivalence can be a problem for simulation if the scenario underpinning it can unfold in a myriad different ways depending on the candidate There are a number of ways of controlling this You can minimise the degree of interaction between the candidate and the simulator Responses can be recorded but have no effect on how the scenario unfolds The simulation can be split into short, manageable segments with the status of the scenario being reset at the end of each segment The cost of each of these solutions is to reduce the reality of the simulation and to restrict the sorts of behaviour that can be observed, for example, how well individuals recover from making sub-optimal decisions or errors However, it is unnecessary to strive for very high levels of reality Carefully constructed scaled simulations can cover all the bases while allowing the assessor to maintain a high level of control over assessment conditions

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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

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