scholarly journals Probabilistic flooding for efficient information dissemination in random graph topologies

2010 ◽  
Vol 54 (10) ◽  
pp. 1615-1629 ◽  
Author(s):  
Konstantinos Oikonomou ◽  
Dimitrios Kogias ◽  
Ioannis Stavrakakis
Keyword(s):  
Random Graph ◽  
Information Dissemination ◽  
Efficient Information ◽  
Probabilistic Flooding

Probabilistic flooding coverage analysis for efficient information dissemination in wireless networks

2018 ◽  
Vol 140 ◽  
pp. 51-61 ◽  
Author(s):  
George Koufoudakis ◽  
Konstantinos Oikonomou ◽  
Konstantinos Giannakis ◽  
Sonia Aïssa
Keyword(s):  
Wireless Networks ◽  
Information Dissemination ◽  
Coverage Analysis ◽  
Efficient Information ◽  
Probabilistic Flooding

scholarly journals Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

2018 ◽  
Vol 7 (3) ◽  
pp. 39 ◽  
Author(s):  
George Koufoudakis ◽  
Konstantinos Oikonomou ◽  
Georgios Tsoumanis
Keyword(s):  
Markov Chain ◽  
Energy Harvesting ◽  
Information Dissemination ◽  
Markov Chain Model ◽  
Wireless Sensor ◽  
Chain Model ◽  
Network Coverage ◽  
Termination Time ◽  
Probabilistic Flooding ◽  
Network Environments

Technological advantages in energy harvesting have been successfully applied in wireless sensor network environments, prolonging network’s lifetime, and, therefore, classical networking approaches like information dissemination need to be readdressed. More specifically, Probabilistic Flooding information dissemination is revisited in this work and it is observed that certain limitations arise due to the idiosyncrasies of nodes’ operation in energy harvesting network environments, resulting in reduced network coverage. In order to address this challenge, a modified version of Probabilistic Flooding is proposed, called Robust Probabilistic Flooding, which is capable of dealing with nodes of about to be exhausted batteries that resume their operation after ambient energy collection. In order to capture the behavior of the nodes’ operational states, a Markov chain model is also introduced and—based on certain observations and assumptions presented here—is subsequently simplified. Simulation results based on the proposed Markov chain model and a solar radiation dataset demonstrate the inefficiencies of Probabilistic Flooding and show that its enhanced version (i.e., Robust Probabilistic Flooding) is capable of fully covering the network on the expense of increased termination time in energy harvesting environments. Another advantage is that no extra overhead is introduced regarding the number of disseminated messages, thus not introducing any extra transmissions and therefore the consumed energy does not increase.

scholarly journals Evaluation of Epidemic-Based Information Dissemination in a Wireless Network Testbed

Technologies ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 36
Author(s):  
Andreana Stylidou ◽  
Alexandros Zervopoulos ◽  
Aikaterini Georgia Alvanou ◽  
George Koufoudakis ◽  
Georgios Tsoumanis ◽  
...  
Keyword(s):  
Epidemic Model ◽  
Information Dissemination ◽  
Low Cost ◽  
University Campus ◽  
Network Coverage ◽  
Sir Epidemic Model ◽  
Sir Epidemic ◽  
Dissemination Process ◽  
Efficient Alternative ◽  
Probabilistic Flooding

Information dissemination is an integral part of modern networking environments, such as Wireless Sensor Networks (WSNs). Probabilistic flooding, a common epidemic-based approach, is used as an efficient alternative to traditional blind flooding as it minimizes redundant transmissions and energy consumption. It shares some similarities with the Susceptible-Infected-Recovered (SIR) epidemic model, in the sense that the dissemination process and the epidemic thresholds, which achieve maximum coverage with the minimum required transmissions, have been found to be common in certain cases. In this paper, some of these similarities between probabilistic flooding and the SIR epidemic model are identified, particularly with respect to the epidemic thresholds. Both of these epidemic algorithms are experimentally evaluated on a university campus testbed, where a low-cost WSN, consisting of 25 nodes, is deployed. Both algorithm implementations are shown to be efficient at covering a large portion of the network’s nodes, with probabilistic flooding behaving largely in accordance with the considered epidemic thresholds. On the other hand, the implementation of the SIR epidemic model behaves quite unexpectedly, as the epidemic thresholds underestimate sufficient network coverage, a fact that can be attributed to implementation limitations.

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