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Examples of tree-based schemes are AMRIS (Wu and Tay, 1999) (which proactively builds a shared multicast tree among a set of sources and destinations), multicast ad hoc on-demand distance vector (MAODV) (Royer and Perkins, 2000) [which extends the well-known ad hoc on-demand distance vector (AODV) reactive protocol by adding an activable multicast mode on top of paths built by AODV], and adaptive demand-driven multicast routing (ADMR) (Jetcheva and Johnson, 2001) (which constructs an overlay multicast tree from each source to its destinations, with parts of the trees being possibly shared by different sources) Examples of mesh-based approaches include CAMP (GarciaLuna-Aceves and Madruga, 1999) (an extension of the proactive core-based trees protocol that adds redundant links) or ODMRP (Lee et al, 1999) (a reactive protocol that computes several paths among sources and destinations) Some protocols, such as AMROUTE (Xie et al, 2002), combine tree- and mesh-based approaches.

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Joyce, a 36-year-old woman who was trying to overcome a problem with Xanax (alprazolam), had found that things were going quite well in her life. She had a much better relationship with her children since she stopped using the Xanax, work was much easier than before, and she was dating a person who seemed much nicer than the others she had dated before she quit the drugs. It was not always easy, though. When she first stopped, she felt so anxious that she thought her insides were going to shake out, and she found herself alternating between being really irritable and being really tearful. After a while, her moods leveled out and she began to experience great satisfaction in her life. That joy scared her and she thought about using again because she was not sure how to cope with this experience of joy. In some ways she felt guilty about feeling so good after feeling numb for so long. Today, she is grateful to have that joy, and all the other emotions including the not-so-good ones, because they remind her that she is alive instead of numb.

Finally, some protocols such as multicast core-extraction distributed ad hoc routing (MCEDAR) (Sinha et al, 1999) or the one in Jaikaeo and Shen (2002) rely on a backbone structure While using the wireless multicast advantage correctly, these protocols focus more on nding quick and robust multicast paths than on minimizing the energy consumption This is mainly due to the fact that building an energy-ef cient multicast tree consumes additional time, which is critical in highly dynamical contexts, because an energy-ef cient tree may no longer exist by the time it is computed In near-static scenarios such as sensor and actuator networks, however, taking the time to build an energy-ef cient path is relevant The problem of nding a minimum cost multicast tree in a wired network (ie.

This chapter considers the impact of knowledge management (KM) on the workplace This is still a matter of some debate, and the limited evidence currently available makes it dif cult to draw any conclusive judgements Despite this caveat, however, one of the recurring themes of this chapter will be the dramatic contrast between the level of interest and expectation surrounding KM and its concrete achievements at workplace level The explanations for that contrast are many and various As we will see, however, they have something to do with the inherent scope of the design of KM systems, but also the limited success of such designs, especially as they relate to the use of IT and the pursuit of management control.

, without considering the wireless advantage) is known to be NP-complete, even when every link has the same cost [this problem is similar to the Steiner tree problem, where the weights of all edges are equal to 1 (Karp et al, 1972)] In a wireless network, where the purpose is to minimize the number of retransmissions instead of the number of hops, this problem is still NP-complete, as proven in Ruiz and Gomez-Skarmeta (2005) Some heuristics have been considered, such as in Ruiz et al (2007) However, as for the protocols described before, the routing strategy considered here rely on an overlay structure that covers the whole network Constructing and maintaining such structure may be very costly in message overhead and does not scale well in large networks When the positions of nodes are known, a better and more ef cient routing can be achieved..

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