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[155] S Brands and D Chaum, Distance-bounding Protocols, in Workshop on the Theory and Application of Cryptographic Techniques on Advances in Cryptology Springer, New York, 1994, pp 344 359 [156] S Capkun and J Hubaux, Secure Positioning of Wireless Devices with Application to Sensor Networks, in Proceedings of Infocom, 2005 [157] R J Fontana, E Richley, and J Barney, Commercialization of an Ultra Wideband Precision Asset Location System, in Proceedings of IEEE Conference on Ultra Wideband Systems and Technologies, November 2003 [158] N Sastry, U Shankar, and D Wagner, Secure Veri cation of Location Claims, in Proceedings of ACM WISE, 2002, pp 1 10 [159] B Waters and E Felten, Proving the Location of Tamper Resistent Devices, //wwwcsprincetonedu/~bwaters/research/location_provingps [160] S Capkun and J Hubaux, Secure Positioning in Wireless Networks, IEEE JSAC, special issue on security in ad-hoc networks, 24(2) 221 232 (2006) [161] S Capkun, M Srivastava, and M Cagalj, Securing Localization With Hidden and Mobile Base Stations, NESL-UCLA Technical Report, March 2005 [162] D Shaw and W Kinsner, Multifractal Modeling of Radio Transmitter Transients for Clasi cation, in Proceedings of the IEEE Conference on Communications, Power and Computing, May 1997, pp 306 312 [163] L Lazos and R Poovendran, SeRLoc: Robust Localization for Wireless Sensor Networks, ACM Sensor Networks, 2005 [164] L Lazos and R Poovendran, HiRLoc: Hi-resolution Robust Localization for Wireless Sensor Networks, in IEEE JSAC, special issue on wireless security, 24(2), 233 246 (2006) [165] D Niculescu and B Nath, Ad-hoc Positioning Systems (APS), in Proceedings of IEEE GLOBECOM, Vol 5, 2001, pp 2926 2931 [166] F Anjum, S Pandey, and P Agrawal, Secure Localization in Sensor Networks using Transmission Range Variation, 1st International Workshop on Resource Provisioning and Management in Sensor Networks (RPMSN05), IEEE Conference on Mobile Ad Hoc and Sensor Systems, November 2005 [167] Wireless Integrated Network Systems (WINS), http://winsrscrockwellcom/ [168] L Lazos, S Capkun, and R Poovendran, ROPE: Robust Position Estimation in Wireless Sensor Networks, The Fourth International Conference on Information Processing in Sensor Networks (IPSN 0 05), April 2005 [169] D Liu, P Ning, and W Du, Detecting Malicious Beacon Nodes for Secure Location Discovery in Wireless Sensor Networks, in Proceedings of the The 25th International Conference on Distributed Computing Systems (ICDCS 05), June 2005, pp 609 619 [170] D Liu, P Ning, and W Du, Attack-resistant Location Estimation in Sensor Networks, in Proceedings of The Fourth International Symposium on Information Processing in Sensor Networks (IPSN 05), April 2005, pp 99 106 [171] Z Li, Y Zhang, W Trappe, and B Nath, Robust Statistical Methods for Securing Wireless Localization in Sensor Networks, in International Symposium on Information Processing in Sensor Networks (IPSN), 2005, pp 91 98 [172] W Jones, Building Safer Cars, IEEE Spectrum, 39(1), 82 85 (2002) [173] B Parno and A Perrig, Challenges in Securing Vehicular Networks, in Workshop on Hot Topics in Networks (HotNets-IV), 2006 [174] P Samuel, Of Sticker Tags and 59 GHz, ITS International, 2004 [175] J P Hubaux, S Capkun, and J Luo, The Security and Privacy of Smart Vehicles, IEEE Security and Privacy, May/June 2004.

This formula expresses most pointedly how multifractal analysis goes beyond second order statistics: with T (q) we capture the scaling of all moments The relation (490), here derived for zero-mean processes, can be put on more solid grounds using wavelet estimators of the LRD parameter [ABR 95] which are more robust than the estimators through variance The same formula (490) also reappears for certain multifractals (see (4100)) In this context it is worthwhile pointing forward to (496), from which we conclude that T BH (q) = qH 1 if q > 1 The fact to note here is that FBM requires indeed only one parameter to capture its scaling while multifractal scaling, in principle, is described by an array of parameters T (q) 48 Multifractal processes The most prominent examples where we nd coinciding, strictly concave multifractal spectra are the distribution functions of cascade measures [MAN 74,

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