Probabilistic Flooding and Largest Eigenvalue/Principal Eigenvector
Probabilistic flooding was also studied borrowing tools from algebraic graph theory.
Coverage as a function of the forwarding probability q for various Geometric Random Graphs (GRG: rc = 0.3, 0.4, 0.5) and ErdosRenyi (ER: p = 0.2, 0.3, 0.4) topologies. The particular values of 4/d , 4/λ1 , 1/d and 1/λ1 are also depicted. Apparently, for q>4/λ1 full coverage is a achieved Under probabilistic Flooding, while for q<1/λ1 coverage is negligible.
Publications
[5] 
Konstantinos Oikonomou, George Koufoudakis, Sonia Aïssa, Ioannis Stavrakakis, “Probabilistic Flooding Performance Analysis Exploiting Graph Spectra Properties”, In IEEE/ACM Transactions on Networking, pp. 114, 2022.

[4] 
Andreana Stylidou, Alexandros Zervopoulos, Aikaterini Georgia Alvanou, George Koufoudakis, Georgios Tsoumanis, Konstantinos Oikonomou, “Evaluation of EpidemicBased Information Dissemination in a Wireless Network Testbed”, In Technologies, vol. 8, no. 3, 2020.

[3] 
George Koufoudakis, Konstantinos Oikonomou, Sonia Aïssa, Ioannis Stavrakakis, “Analysis of Spectral Properties for Efficient Coverage Under Probabilistic Flooding”, In 2018 IEEE 19th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM) (IEEE WoWMoM 2018), Chania, Crete, Greece, pp. 19, 2018.

[2] 
George Koufoudakis, Konstantinos Oikonomou, Konstantinos Giannakis, Sonia Aïssa, “Probabilistic Flooding Coverage Analysis for Efficient Information Dissemination in Wireless Networks”, In Computer Networks, vol. 140, pp. 51 – 61, 2018.

[1] 
Konstantinos Oikonomou, George Koufoudakis, Sonia Aïssa, “Probabilistic Flooding Coverage Analysis in Large Scale Wireless Networks”, In 2012 19th International Conference on Telecommunications (ICT), pp. 16, 2012.
