By Qiaohong Zu, Maria Vargas-Vera, Bo Hu
ISBN-10: 3319092642
ISBN-13: 9783319092645
ISBN-10: 3319092650
ISBN-13: 9783319092652
This ebook constitutes the completely refereed post-conference court cases of the Joint overseas convention on Pervasive Computing and net Society, ICPCA/SWS 2013, held in Vina de Mar, Chile, in December 2013. The fifty six revised complete papers offered including 29 poster papers have been rigorously reviewed and chosen from 156 submissions. The papers are equipped in topical sections on infrastructure and units; carrier and resolution; info and data; in addition to community.
Read or Download Pervasive Computing and the Networked World: Joint International Conference, ICPCA/SWS 2013, Vina del Mar, Chile, December 5-7, 2013. Revised Selected Papers PDF
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Extra resources for Pervasive Computing and the Networked World: Joint International Conference, ICPCA/SWS 2013, Vina del Mar, Chile, December 5-7, 2013. Revised Selected Papers
Example text
Similarly we set other nodes as ”active nodes”. By this way, we approximately simulate the real-life scenarios. In most cases, the diameter of a community is about 5 hops, we control this parameter by adjusting radio range. 100 data objects are manually distributed to all communities. Community members would generate the published data at any random time point in a limited time period once simulation begins. Whereas subscriptions of these 100 data objects are randomly released by any community member, each node should generate 2 different randomly selected subscriptions in simulation duration, thus 120 subscriptions would be released.
Fig. 1. There are two main traditional algorithms in the study of community structure. The first one is based on graph theory algorithms; the second one is based on clustering algorithms. The algorithm, is based on greedy principles, and it is the classical algorithm to solve the problem of graphics in computer science division. The second algorithm is spectrum split method [5], which is based on the characteristic value of the Laplace. The GN algorithm [6] is proposed by Girvan and Newman later.
5. O and ContentPlace. The hops in Fig. 6(a) means the total hop number that each subscription takes before it is successfully matched to the local existing data in the specific node. The hops in Fig. 6(b) means the hop number that each data packet takes from source node to destination node. HRM, SAO and ContentPlace perform slightly different in these two indicators. HRM has more subscription locating on higher horizontal axis position. That is because HRM trends to make full use of relay nodes by entrusting relay nodes with as much efficient subscriptions as possible.