By Christos H. Papadimitriou (auth.), Viktor K. Prasanna, Sitharama S. Iyengar, Paul G. Spirakis, Matt Welsh (eds.)
The booklet constitutes the refereed lawsuits of the 1st overseas convention on disbursed Computing in Sensor structures, DCOSS 2005, held in Marina del Rey, California, united states in June/July 2005.
The 26 revised complete papers awarded have been conscientiously reviewed and chosen from eighty five submissions; additionally integrated are the abstracts of three invited talks, 2 brief papers, nine invited poster abstracts, and 10 contributed abstracts.The papers tackle all present points of disbursed computing matters in large-scale networked sensor platforms, together with systematic layout innovations and instruments, algorithms, and applications.
Read Online or Download Distributed Computing in Sensor Systems: First IEEE International Conference, DCOSS 2005, Marina del Rey, CA, USA, June 30 – July 1, 2005. Proceedings PDF
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Additional resources for Distributed Computing in Sensor Systems: First IEEE International Conference, DCOSS 2005, Marina del Rey, CA, USA, June 30 – July 1, 2005. Proceedings
In addition, we assume that each node has a unique reference direction, which can be easily obtained from electronic compass. Each node is able to monitor a unit-disk-shape area centered at itself, though our algorithm can work for other regular or irregular shapes of sensing area with only a minor modification. 1 The Ideal Node Layout for Maximum Coverage Similar to the deployment algorithms discussed in the previous section, one of the important goals of our algorithm is to maximize the coverage area.
By deﬁnition we have p1 = 1 because sensors belonging to the ﬁrst slice can do nothing else than transmitting to the sink. The number of sensors belonging to the i-th slice is denoted by Si . It might be the case that there is a strong relationship between Si , λi but this is not essential. The total energy available at the i-th slice is denoted by Ei , thus ei = Ei /Si is the available energy per sensor. The energy can be seen as a given amount of energy available at the start or as a rate of consumable energy.
5 Conclusions In this paper, we have proposed a new adaptive deployment algorithm for unattended mobile sensor networks, namely, adaptive triangular deployment (ATRI) algorithm. We have introduced an equilateral triangle deployment layout of sensor nodes and proved that it can produce the maximum no-gap coverage area in a plane. Although only the location information of one-hop neighbors is used for the adjustment of each node, the algorithm can make the overall deployment layout close to equilateral triangulations.