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Diffusion arises from a very simple idea: the most likely position of a node is at the centroid of its neighbors positions Diffusion algorithms require only radio connectivity data We describe two different variants below Bulusu et al [11] localize unknown nodes by simply averaging the positions of all beacons with which the node has radio connectivity Thus, Bulusu et al assume that nodes have no way of ranging to beacons This method is attractive in its blinding simplicity; however, the resulting positions are not very accurate, particularly when beacon density is low, or nodes fall outside the convex hull of their audible beacons Fitzpatrick and Meertens [12] describe a more sophisticated variant: each node is at the centroid of its neighbors, including nonbeacons The algorithm is as follows: Step 1 Initialize the position of all nonbeacon nodes to (0, 0) Step 2.

Open the Skins folder for a component in the library. Double-click the skin movie clip for which you want to create new skin elements. Modify the movie clip or delete it and create a new one. Break the graphic into separate skin elements, and save each element as a movie clip symbol. Give each skin element a unique name. For more information, see About creating and registering skin elements on page 310. Click on the first frame of the ReadMe layer of the skin movie clip that you selected in step 2. Open the Actions panel in expert mode. Replace the name of the original skin elements in the movie clip with the names of the new skin elements that you created in step 4.

Repeat the following until positions converge: Step 2a Set the position of each nonbeacon node to the average of all its neighbors positions This variant requires fewer beacons than Bulusu et al s algorithm; nevertheless, its accuracy is poor when node density is low, nodes are outside the convex hull of the beacons, or node density varies across the network In all of these cases, a more sophisticated algorithm would improve accuracy dramatically Fitzpatrick and Meertens s variant also uses substantially more computation than Bulusu et al s approach, since positions must be exchanged between adjacent nodes during step 2 However, this algorithm is quite useful in networks where nodes are capable of very little computation, but the network topology can be selectively changed to improve localization In particular, Savvides et al [13] recommend placing some beacons around the edges of the sensor network eld.

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Selectively adding additional beacons can also help resolve pathologies in the diffusion estimates Bulusu et al [11] describe an approach for adaptive beacon placement to improve diffusionbased localization..

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Hagenaars, J A, & McCutcheon, A L (Eds) (2002) Applied latent class analysis Cambridge, England: Cambridge University Press Hambleton, R K, Swaminathan, H, & Rogers, H J (1991) Fundamentals of item response theory Newbury Park, CA: Sage Hands, B, & Larkin, D (2001) Using the Rasch measurement model to investigate the construct of motor ability in young children Journal of Applied Measurement, 2, 101 120 Heinemann, A W, Linacre, J M, Wright, B D, Hamilton, B B, & Granger, C (1993) Relationships between impairment and physical disability as measured by the functional independence measure Archive of Physical Medicine and Rehabilitation, 74, 566 573 Hessen, D J (2004) A new class of parametric IRT models for dichotomous item scores Journal of Applied Measurement, 5, 385 397 Howell, D C (2002) Statistical methods for psychology (5th ed) Belmont, CA: Duxberry Jackson, S.

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A, & Marsh, H W (1996) Development and validation of a scale to measure optimal experience: The Flow State Scale Journal of Sport and Exercise Psychology, 18, 17 35 Kyllonen, P C, Lohman, D F, & Woltz, D J (1984) Componental modeling of alternative strategies for performing spatial tasks Journal of Educational Psychology, 76, 1325 1345 Lang, W S (2002) Ranking and meaning of sailing teams Rasch Measurement Transactions, 15(4), 851 Lang, W S, & Wilkerson, J R (2005) Measuring college sailing teams ability: An application of the many-facet Rasch model to ordinal data Journal of Applied Measurement, 6, 57 70 Langeheine, R, & Rost, J (Eds) (1988) Latent trait and latent class models New York: Plenum Press Lazarsfeld, P F (1950) Logical and mathematical foundations of latent structure analysis In S A Stouffer, L Guttman, E A Suchman, P F Lazarsfeld, S A.

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