TY - GEN
T1 - On the Social Network Centrality Principle for Human Centric Efficiency
AU - Papanikolaou, Katerina
AU - Mavromoustakis, Constandinos X.
AU - Mastorakis, George
AU - Batalla, Jordi Mongay
AU - Dobre, Ciprian
AU - Katzis, Konstantinos
PY - 2020/6
Y1 - 2020/6
N2 - The use of Social Network Centrality alongside Content Centric Networking is used towards improving user's access to content, as well as maximizing the efficient exploitation of network resources. This work demonstrates how online Social Network Centrality can be used with content centric solutions for efficient content distribution in AAL environments. The benefits of this approach are highlighted, by conducting simulations and providing results on the performance of the proposed research approach. In particular, this work shows how the proposed resource allocation method improves the regular IP-based content delivery allocation in the distribution of content. Facebook data is used as a super-set of AAL data, in order to map users to a network graph and using their social proximity to determine where content can be effectively cached. The efficiency of the proposed scheme is validated for its performance through simulations, indicating the level of the offered efficiency in contrast to content distribution.
AB - The use of Social Network Centrality alongside Content Centric Networking is used towards improving user's access to content, as well as maximizing the efficient exploitation of network resources. This work demonstrates how online Social Network Centrality can be used with content centric solutions for efficient content distribution in AAL environments. The benefits of this approach are highlighted, by conducting simulations and providing results on the performance of the proposed research approach. In particular, this work shows how the proposed resource allocation method improves the regular IP-based content delivery allocation in the distribution of content. Facebook data is used as a super-set of AAL data, in order to map users to a network graph and using their social proximity to determine where content can be effectively cached. The efficiency of the proposed scheme is validated for its performance through simulations, indicating the level of the offered efficiency in contrast to content distribution.
KW - Empirical studies in collaborative and social computing
KW - Human-centered Computing
KW - Social content sharing in AAL
KW - Social networking
KW - Theory of computation
UR - http://www.scopus.com/inward/record.url?scp=85089690243&partnerID=8YFLogxK
U2 - 10.1109/IWCMC48107.2020.9148079
DO - 10.1109/IWCMC48107.2020.9148079
M3 - Conference contribution
AN - SCOPUS:85089690243
T3 - 2020 International Wireless Communications and Mobile Computing, IWCMC 2020
SP - 1685
EP - 1688
BT - 2020 International Wireless Communications and Mobile Computing, IWCMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020
Y2 - 15 June 2020 through 19 June 2020
ER -