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remaining users obtain zero throughput. In the second kind, there are multiple sel sh devices, all of which set their contention windows to 1. Consequently, the tragedy of the commons occurs all users, sel sh or not, obtain zero throughput. Cagalj et al. [10] then considered a dynamic game in which the users repeatedly make their decisions in multiple rounds. In this dynamic game, the Nash equilibria are of the form Wi = ti, where tiA(0,1) for all sel sh user i. In essence, the sel sh users cooperate to avoid system collapse and, thus, they set their contention windows judiciously. However, there is a possibility that some sel sh users would deviate from this cooperation so as to gain more bandwidth. Thus, cooperative detection and penalty schemes are needed. Speci cally, each sel sh user proactively checks whether some other sel sh users deviate from the Nash equilibrium point. If such a user is detected, all remaining sel sh users penalize the deviated user by jamming the latter s transmission for a certain period of time. This penalty scheme provides an incentive for each sel sh user not to deviate from the Nash equilibrium point. Konorski [11] also worked on a game-theoretic investigation of the sel sh random backoff problem. Unlike previous approaches, Konorski focused on the idea that devices might have different quality of service (QoS) sensitivity, that is, different valuation of the same offered throughput. A detailed mathematical analysis demonstrated that there is a trade-off between bandwidth utilization and fairness, pretty much like the problem of channel quality adaptation. 3.3.4 Reputation-Based Wireless Link Access

(x) Freeze 1 ml per cryovial at a rate of 1 C/min in a freezing container for 6 h at 80 C (y) Transfer cells into liquid nitrogen freezer (b).

2

pseudo-column named Cn to represent the nonexistent column to its right. In our calculations, both C0 and Cn have an af nity of zero. Therefore, when we consider adding C3 to the clustered af nity matrix for our example, there are three ordering options: (C3, C1, C2) , (C1, C3, C2) , or (C1, C2, C3). To determine which ordering yields the highest value for our af nity measure, we must calculate the contribution of C3 being added to the left of C1, between C1 and C2 and to the right of C2. Therefore, we need to calculate Cont(C0, C3, C1) , Cont(C1, C3, C2), and Cont(C2, C3, Cn) and compare the results. To calculate the contribution of (C0, C3, C1), we need to gure out the bonds for (C0,C3), (C3, C1), and (C0, C1). Once we have determined these bonds, we can use Equation 2.1 to calculate Cont(C0, C3, C1) = 2 Bond(C0, C3) +2

The following example adds a notation to the cell s comment each time the cell is changed (as determined by the Change event). The state of a CheckBox, embedded on the worksheet, determines if the change is added to the comment. Figure 19-5 shows an example of a comment for a cell that has been changed several times.

authentication scheme. It can be seen that the proposed SAS-UAB yields the shortest interdomain handoff latency, while the authentication delay caused by two other UAB variants are very close to each other. In addition, the average hop count between the MAPs and the MGW plays an important role in the average authentication delay when a centralized authentication method is in place. The delay of the full IEEE 802.1x authentication scheme increases signi cantly with the hop count from an MAP to the WMG. On the other hand, the hop count is seen with very little impact on the authentication cost of the proposed UAB schemes. This further demonstrates that achieving localized authentication could be very critical to seamless mobility support. 22.5.3 Workload on Roaming Broker

the trick. As in most cases of build-vs.-buy choices, RooTooth is more expensive than building it yourself. Otherwise it s functionally identical to the circuit presented here and will work with all the example programs in this book.

22CCITT is looking at the problem from an international switching center gateway. It will connect via digital trunks to many national networks, each with their own primary reference source (PRS).

HISTORY, RATIONALE, AND PHILOSOPHY OF EDA John Tukey and the Origins of EDA The tradition of EDA was begun and nurtured by John Tukey and his students through his many years at Princeton University and Bell Laboratories As a young academic, Tukey was a prodigious author and formidable mathematical statistician He received his PhD in mathematics from Princeton at the age of 25 and at 35 reached the rank of full professor at the same institution (Brillinger, Fernholz, & Morgenthaler, 1997) A sense of Tukey s breadth and impact can be gleaned from examination of the eight volumes of his collected works Volumes 1 and 2 (Brillinger, 1984, 1985) highlight his contributions to time-series analysis (especially through spectral decomposition) Volumes 3 (Jones, 1986a) and 4 (Jones, 1986b) address Philosophy and Principles of Data Analysis, and volume 5 is devoted to graphics (Cleveland, 1988).