In this study, bioactive hydroxyapatite nanoparticles were prepared by two different methods: wet chemical precipitation and biomimetic precipitation. The aim was to evaluate the morphology, particle-size, crystallinity and phases of the powders obtained by traditional wet chemical precipitation and the novel biomimetic precipitation using a supersaturated calcium solution. The nanoparticles were investigated by transmission electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction. The results revealed that the nanoparticles were formed by hydroxyapatite with a high crystallinity and controlled morphology. Additionally, it was found that the shape and size of the nanoparticles can be modified with each preparation method.

In a multihop wireless network, each node has a transmission radius and is able to send a message to one of its neighbors (one-to-one) or all of its neighbors (one-to-all) that are located within the radius. In a broadcasting task, a source node needs to send the same message to all the nodes in the network. In this paper, we propose to reduce the communication overhead of broadcasting algorithm for one-to-one model by applying the concepts of planar graphs such as RNG (relative neighborhood graphs) and connected dominating sets determined by internal nodes. Regular message exchanges between neighbors, which include location updates or signal strengths, suffice to maintain these structures, and they therefore do not impose additional communication overhead. In internal node based broadcasting, messages are forwarded on the edges that connect two internal nodes, and on edges that connect each non-internal node with its closest internal node. A neighbor elimination scheme is added to the internal node concept, to improve its performance. Similarly, only edges in a planar subgraph may be used for retransmissions. The reduction in communication overhead for broadcasting task, with respect to existing methods, is measured experimentally. The number of retransmissions is reduced to about 50% for sparse networks and to about 5% for dense networks, and the overhead with respect to ideal solution is up to 20% (for 100 nodes).

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