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Sensor networks are typically composed of hundreds to thousands of small collaborating wireless sensor nodes that have limited computation and communication capabilities Communication patterns within a sensor network are data delivery from sensor nodes to one or a subset of selected information sinks, and information sinks requesting a certain physical phenomenon or requesting sensor nodes lying within a sensed area In general, addressing is achieved by utilizing sensor locations or querying all sensor nodes matching a certain criterion instead of utilizing individual node addresses Ease of deployment and the fact that sensor nodes are small and closely located at the measured phenomenon makes an external power supply, recharging batteries, or replacing depleted batteries impractical or even impossible Consequently, the lifetime of a sensor node is directly related to its on-board power supply, and thus energy is the most sensitive resource with respect to the whole network lifetime.

Personality is a dif cult concept to pin down. By necessity it is a very broad concept because personality impinges on virtually all aspects of human behavior. This breadth is viewed differently by different theorists, however. As a result, many different approaches have been taken to thinking about and conceptualizing personality. The diversity in focus among the chapters in the rst part of this volume attests very clearly to that fact. We were both trained as personality psychologists. Throughout our careers, however, our research interest has

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Communication between sensor nodes and information sinks can be achieved by setting an appropriate transmission power (if possible) and sending data or control messages directly to the desired recipient However, this simple communication form may degrade the bandwidth of the limited shared wireless communication media, and moreover will drastically increase energy consumption at sender nodes, since signal attenuation increases signi cantly with the distance to the message recipient If no xed networked infrastructure is additionally available, a resource-saving communication may only be achieved by multihop ad hoc routing techniques, where communication between any two network nodes requires collaborating with intermediate next-hop forwarding nodes Since location information is often available due to the very nature of sensor networks, the special class of geographic routing algorithms may be a good choice in order to build a scalable resource-saving communication infrastructure.

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Geographic routing algorithms allow routers to be nearly stateless, since packet forwarding is achieved by utilizing location information about candidate nodes in the vicinity and the location of the nal destination only By their localized nature, geographic routing algorithms are highly scalable solutions that do not require any additional control overhead when network topology changes due to mobility or energyconserving sleep cycles In particular, due to the addressing scheme of sensor networks there is no need for an additional location service (producing an additional network load), which is used in other ad hoc network scenarios in order to acquire location information about individual network nodes before communication can take place Finally, location information about all neighbor nodes can be used in order to estimate the signal strength needed to reach a certain neighbor node Recent work investigated that location information can be utilized to de ne new.

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. . . . . . . . . . . . . . . .

That is, the response probabilities (in logits) are equal to the difference between a person s ability v and the item difficulty i. In contrast to the classical error model, in which an observable manifest variable, X, of a person v for an item i is decomposed additively into the unobservable variables (true score) and e (error component), the decomposition in the Rasch model refers to the logits of the response probabilities (equation 33.1). A simple transformation of equation 33.1 leads to the better known response function of the Rasch model: p ( Xvi = 1) = e(

(23.16)

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