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There are other metrics that can be considered [12]: the protocol duration, message overhead, and backbone robustness (does the backbone remains connected if one node fails ) For each metric, the evaluation can be performed, analytically or experimentally, using either average-case or worst-case performances The ultimate goal is certainly to have a winner in both categories (such as mergesort or heapsort for the sorting problem) However, so far such a winner has not emerged, and researchers have normally adopted one of the two ways for comparison Arguably, if a sensor network designer is presented with two protocols, one with excellent average-case, but occasionally quite bad performance, and the other with rm worst case guarantees (eg, theoretically provable constant bound for the.

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of 2E; we do not advocate this approach. The approach that we do advocate was already outlined in our section on determining a value for the expected variance. Third, tell the reader if a discrete or continuous rating scale was assumed. In our experience, most researchers have assumed a discrete rating scale. Given the number of categories that are typically employed ( 11), we advocate using a continuous rating scale approach. Fourth, summarize for the reader what the observed distributions were for the set of responses to each item. If the majority of the distributions are negatively skewed, tell the reader if you believe a response bias was at least partially responsible for the observed skewness. If you believe that a response bias was at least partially responsible for the observed skewness, tell the reader how the chosen value of 2E accounted for this bias. If you believe that the observed skewness was not due to any response bias and 2EU was used as the estimate for 2E, tell the reader this. None of the studies that we reviewed adequately addressed all four suggestions. The Myers, Payment, et al. (2004) study provides an example to build on, although it does not address the first or second suggestion. What to Do with Estimates of rwg Some researchers believe that there needs to be an acceptable level of consensus, as measured by rwg, within a team before athlete-level collective efficacy estimates can be aggregated to the team level. This belief is articulated by Moritz and Watson (1998, p. 287):

With these new de nitions, we can expand the wound wait algorithm to incorporate distributed control. The algorithm is shown in Figure 6.6. In this algorithm, Ta and Tb are both global transactions. As a result, we have to consider not only the local waits but also the global waits (which are denoted as awaits in this discussion). Local deadlock detectors can detect deadlocks among local transactions but cannot detect global deadlocks. To make sure that we do not have

multicast-enabled TCP/IP software, multicast-enabled infrastructure (routers, switches, etc.), and multicast-enabled applications. Do not confuse the operation of IP multicast as a standalone application. This operates on the very same workstation that you are using today to access IP applications and/or the Internet. IP multicast peacefully coexists with your IP applications. IP multicast usually does not operate alone; there are other protocols that are used in conjunction with it to provide for VVD (voice, video, and data) over IP. Such protocols are Real Time Protocol (RTP) and the Real Time Control Protocol (RTCP), Real Time Streaming Protocol (RTSP), and Resource Reservation Protocol (RSVP). Similar to this is IP. There are many components to the TCP/IP network and IP is simply one of the components. It provides for datagram delivery. IP multicast is also a component of many. IP multicast is based on a few protocols, but it is all the other necessary components that really make it work. Berkeley Sockets for Multicast provides an API set that easily enables most Unix hosts to become multicast ready, and Microsoft is supporting multicast in its sockets interface known as WinSock 2.0. With the APIs in place, multicast applications can be built.

can be approximated by the angle of steepest descent: (2.32)

= |A(n) (S)|2 n(H (S) ) .

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