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7.1.2.1 Energy and Other Resources Sensor-network applications often require sensor nodes to be small and cheap. This has a number of important implications. First of all, the amount of energy that can be stored in or scavenged by small devices is typically very limited due to the low-energy density of available and foreseeable technology. To ensure longevity despite this limited energy budget, energyef cient design both in hardware and software becomes a dominating goal. Additionally, computing, storage, and communication capabilities of individual sensor nodes are rather limited due to size and energy constraints. These constraints may preclude the use of GPS or other technologies for out-ofband synchronization of NTP master nodes. NTP is also not optimized for energy ef ciency, simply because this is not an issue in infrastructure-based distributed systems. Energy overhead in NTP results from several sources. First, the service provided by NTP typically cannot be dynamically adapted to the varying needs of an application. Hence, with NTP all nodes would be continuously synchronized with maximum precision, even though only subsets of nodes might occasionally need synchronized time with less-than-maximum precision. Second, NTP uses the processor and the network in ways that would lead to signi cant overhead in energy expenditure in sensor networks. For example, NTP maintains a synchronized system clock by regularly adding small increments to the system-clock counter. This behavior precludes the processor from being switched to a power-saving idle mode. In addition, NTP servers must be prepared to receive synchronization requests at any point in time. However, constantly listening is an energywise costly operation in sensor networks; many sensor network protocols therefore switch off the radio whenever possible.

transition behaviour. The initial probabilities remain p(O). The operation can now be interpreted as follows. When entering state i, the CTMC will

A red background not recommended for those with a weak stomach. You can also write multiline values. You can use this syntax in your entry data:

Table 6.2 Role of groundwater systems in the operation of the ve dynamic environmental factors which govern the sustained healthy functioning of all freshwater ecosystems. Factor

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s with any emerging technology, organizations actively seek knowledge, insights, and best practices for how to begin their cloud computing initiatives. Organizations want to know how to prepare for their own cloud journey and how to avoid the risks and potholes associated with the early adopter stages of this emerging technology. This chapter offers insights and recommendations for the best path toward beginning your cloud computing journey. First, we must review the Cloud Adoption Lifecycle Model presented in 5. This is the rst step to beginning with cloud.

2. The optimal solution wo can be determined recursively via a steepest-descent method. Start with any initial guess w-1 (e.g., w-1 = 0) and iterate

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Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear regression models (3rd ed.). Chicago: Irwin. Pedhazur, E. J. (1997). Multiple regression in behavioral research (3rd ed.). Fort Worth, TX: Harcourt Brace. Pitts, S. C., & West, S. G. (2001). Alternative sampling designs to detect interactions in multiple regression. Unpublished manuscript. Rosenstock, I. M. (1990). Explaining health behavior through expectancies. In K. Glanz, F. M. Lewis, & B. K. Rimer (Eds.), Health behavior and health education (pp. 140 157). San Francisco: Jossey-Bass. Ryan, T. P. (1997). Modern regression methods. New York: Wiley. Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman and Hall. Schmitt, N., Coyle, B. W., & Rauschenberger, J. A. (1977). A Monte Carlo evaluation of three formula estimates of cross-validated multiple correlation. Psychological Bulletin, 84, 751 758. Wainer, H. (2000). The centercept: An estimable and meaningful regression parameter. Psychological Science, 11, 434 436. West, S. G., & Aiken, L. S. (1997). Toward understanding individual effects in multiple component prevention programs: Design and analysis strategies. In K. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 167 209). Washington, DC: American Psychological Association. West, S. G., Aiken, L. S., & Krull, J. (1996). Experimental personality designs: Analyzing categorical by continuous variable interactions. Journal of Personality, 64, 1 47. Wilcox, R. R. (1997). Introduction to robust estimation and hypothesis testing. San Diego, CA: Academic Press. Wilkinson, L., & the Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594 604. Winer, B. J., Brown, D. R., & Michels, K. M. (1991). Statistical principles in experimental design (3rd ed.). New York: McGraw Hill.

The Layer 2 Forwarding (L2F) protocol is used to create a secure tunnel between a LAN and a remote user. L2F permits the tunneling of information at the Data Link layer (Layer 2) of the OSI Reference Model. L2F allows the encapsulation of Point-to-Point Protocol (PPP) packets within the tunnel. This protocol was later merged with the Point-to-Point Tunneling Protocol (PPTP) to make L2TP. RFC 241 covers the L2F protocol. Figure 1-25 shows the L2F header. As shown in Figure 1-25, the bits in the L2F packet header are as follows:

Other formats supported are the following: DIF (Data Interchange Format) file format was used by VisiCalc. I haven t seen a DIF file in ages. Excel can read and write these files. SYLK (SYmbolic LinK) file format was used by MultiPlan. SYLK files, too, are quite rare these days. Excel can read and write these files.

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