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determine, as precisely as possible, their geographic coordinates in the area of deployment One simple solution to the localization problem is to use a GPS, where sensors receive signals from several satellites and decide their position directly However, for tiny sensors such direct position learning may not be possible or may not be suf ciently accurate enough (if a GPS signal is not provided with suf cient accuracy) for the assigned task However, due to limitations in form factor, cost per unit, and energy budget, individual sensors are not expected to be GPSenabled Moreover, in many occluded environments, including those inside buildings, hangars, or warehouses, satellite access is drastically limited Since direct reliance of GPS is speci cally proscribed, in order to obtain location awareness individual sensors exchange messages to collaboratively determine their own geographic position (absolute or relative) in the network.

Janelle, C M (2002) Anxiety, arousal and visual attention: A mechanistic account of performance variability Journal of Sports Sciences, 20, 237 251 Kane, M J, & Engle, R W (2000) Working-memory capacity, proactive interference, and divided attention: Limits on longterm memory retrieval Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 336 358 Kanfer, R, & Ackerman, P L (1989) Motivation and cognitive abilities: An integrative/aptitude-treatment interaction approach to skill acquisition [Monograph] Journal of Applied Psychology, 74, 657 690 Kelso, J A S, & Sch ner, G (1988) Self-organization of coordinative movement patterns Human Movement Science, 7, 27 46 Kimble, C E, & Rezabek, J S (1992) Playing games before an audience: Social facilitation or choking Social Behavior and Personality, 20, 115 120 Kimble, G A, & Perlmuter, L C (1970) The problem of volition Psychological Review, 77, 361 384 Langer, E.

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This concern is misguided because there is no particular reason to believe that the easiest variable to measure is the most fundamental for understanding the learning process Of course, it is important to specify precisely the transformations that are used because the conclusions that apply to a particular dependent variable may not apply to a transformation of the dependent variable For example, a signi cant interaction based on one dependent variable (such as time) may not be present on a transformation of that variable (such as speed) Summary Measures A measure of behavior is usually conceptualized as containing a true value plus random error The random error is assumed to have a mean of zero and a symmetrical (usually normal) distribution with some standard deviation If one examines the original measure of behavior, the true value can be lost in the random variability.

The vast majority of collaborative solutions to the localization problem are based on multilateration or multiangulation These solutions assume the existence of several anchors that are aware of their geographic position (eg, sinks or specialized sensors that can engage in satellite communication) By exchanging messages with their neighbors, individual sensors can conceivably measure signal strengths and/or time delays in communication Some approaches are based on hop-count distances to reference points Sensors receiving location messages from at least three sources can approximate their own locations For a good survey on localization protocols for wireless sensor networks, we refer the interested reader to the relevant 9 in this book In some other applications, exact geographic location is not necessary: all that individual sensors need is coarse-grain location awareness.

To reduce this random variability, a measure of central tendency is used The typical measure is the mean or the median, but variants of these measures of central tendency are sometimes used The median provides a way to reduce the effects of outliers The measure.

There is an obvious trade-off; coarse-grain location awareness is lightweight, but the resulting accuracy is only a rough approximation of the exact geographic coordinates One can obtain this coarse-grain location awareness by a training protocol that imposes a coordinate system onto the sensor network Olariu et al [1] have shown that an interesting byproduct of such a training protocol is that it provides partitioning into clusters and a structured topology with natural communication paths The resulting topology will make it simple to avoid collisions between transmissions of nodes in different clusters, between different paths, and also between nodes on the same path This is in contrast with the majority of papers that assume routing along spanning trees with frequent collisions In the training protocol of Olariu et al [1] the deployment area is endowed with a virtual infrastructure (for details see a dedicated 4 in this book).

(a) A' is strictly lower (upper) triangular. (b) A H is strictly lower (upper) triangular. (c) (d)

To make the presentation self-contained, however, we now outline the idea Referring to Figure 132, the coordinate system divides the sensor network area into equiangular wedges In turn, these wedges are divided into sectors by means of concentric circles or coronas centered at the sink The task of training the wireless sensor network involves establishing:.

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