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Each node periodically turns on the radio circuitry for the wake-up channel to hear whether any other node has attempted to communicate with it Once a node detects such an activity in the wake-up signaling channel, it turns on its radio circuitry for the data channel The increased latency due to the sleep state is thus bounded by the sleep listen period in the wake-up channel STEM is especially well suited for applications with most operations in the monitoring state For instance, in a re alarm system, the network only senses the environment in an energy-ef cient way and the system stays in the monitoring state most of the time Once an event takes place, the system quickly changes to the transfer state and reports the event to the data sink in a timely manner Ye et al.

De ne t as the probability that a station transmits in a randomly chosen slot time. Then, according to [17], t = 2/(W+1). On the other hand, it is obvious that p=1 (1 t)n 1. Thus, these two equations can be solved jointly to obtain a unique solution for unknown variables t and p. Finally, the saturation throughput can be calculated as S Ps Ptr E P 1 Ptr s Ptr Ps Ts Ptr 1 Ps Tc

60 50 40 30 20 10

[39] achieve energy saving via adjusting the modulation scaling factor, that is, the number of bits in a modulated symbol In general, a smaller modulation scaling factor reduces the power required in the transmission, but increases the transmission time over a link, although the relationship between the power required and the link delay thus increased is not necessarily monotonic The authors consider a multiple-source single-sink data-aggregation tree, and formulate the problem of nding an optimal schedule of packet transmission to minimize the total transmission energy incurred at all nodes in the aggregation tree, subject to the given propagation latency constraints A numerical optimal algorithm, a pseudopolynomial, a dynamic programming-based approximation algorithm, and a distributed on-line protocol are developed to solve the problem Scaglione and Servetto [36] discuss the trade-off between the bandwidth usage and the decoding delay.

of these pollutants (e.g. sedimentary organic matter content, sorption to which can powerfully affect the mobility of pesticides). Vulnerability designated with respect to particular pollutants is termed speci c vulnerability. The determination of vulnerability has in practice been approached differently in many different countries (Vrba and Zaporozec 1994). One school of thought argues for the use of formal point-scoring systems, where, for every square kilometer (say) of an area underlain by an aquifer, factors such as depth to water table and thickness of clay above aquifer are assigned numerical rankings, with the sum total of rankings for all factors being used to index overall vulnerability. This is the logic behind two vulnerability assessment methodologies with particularly resonant acronyms:

# go get the xml file my $xml = `curl -s -u $delUser:$delPW http://del.icio.us/api/posts/get dt=$date`; # get a new XPath object my $xp = XML::XPath->new(xml => $xml);

They argue that data aggregation along a path leads to better bandwidth usage, but if the aggregation is conducted along multiple parallel paths, the delay incurred in aggregating and sending data to destinations is reduced, but the bandwidth usage (or the corresponding energy consumption) is increased They then suggest that these two quantities should be linked together by the routing strategy chosen 155 DATA TRANSPORT ISSUES.

Meta-Analysis An alternative procedure to combining data from several individuals or units is meta-analysis. Procedures for performing a meta-analysis have been well developed for traditional experimental designs (Glass, McGaw, & Smith, 1981; Hedges & Olkin, 1985; Hunter & Schmidt, 1990; Tobler, 1994). However, meta-analysis procedures have not been widely applied to single-subject designs. Busk and Serlin (1992) present a discussion of the problems of applying meta-analysis to this area. Two problems are (a) primary research reports have often relied on visual analysis (Parsonson & Baer, 1992) rather than time series analysis, resulting in a lack of basic statistical information in the published research reports (O Rourke & Detsky, 1989); and (b) alternative de nitions of effect size must be developed that are appropriate for time series data. Allison and Gorman (1992) and Busk and Serlin (1992) review some alternative effect size calculations appropriate for time series designs.

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