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Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

Department of Informatics, Ionian University, 49100 Corfu, Greece
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J. Sens. Actuator Netw. 2018, 7(3), 39; https://doi.org/10.3390/jsan7030039
Received: 16 July 2018 / Revised: 30 August 2018 / Accepted: 31 August 2018 / Published: 6 September 2018
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. View Full-Text
Keywords: wireless sensor networks; energy harvesting; Markov chain; Robust Probabilistic Flooding wireless sensor networks; energy harvesting; Markov chain; Robust Probabilistic Flooding
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Koufoudakis, G.; Oikonomou, K.; Tsoumanis, G. Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks. J. Sens. Actuator Netw. 2018, 7, 39.

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