• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Dynamic Adjustment Strategy of n-Epidemic Routing Protocol for Opportunistic Networks: A Learning Automata Approach


Abstract

In order to improve the energy efficiency of n–Epidemic routing protocol in opportunistic networks, in which a stable end-to-end forwarding path usually does not exist, a novel adjustment strategy for parameter n is proposed using learning atuomata principle. First, nodes dynamically update the average energy level of current environment while moving around. Second, nodes with lower energy level relative to their neighbors take larger n avoiding energy consumption during message replications and vice versa. Third, nodes will only replicate messages to their neighbors when the number of neighbors reaches or exceeds the threshold n. Thus the number of message transmissions is reduced and energy is conserved accordingly. The simulation results show that, n-Epidemic routing protocol with the proposed adjustment method can efficiently reduce and balance energy consumption. Furthermore, the key metric of delivery ratio is improved compared with the original n-Epidemic routing protocol. Obviously the proposed scheme prolongs the network life time because of the equilibrium of energy consumption among nodes.


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Cite this article

[IEEE Style]
Feng Zhang, Xiaoming Wang, Lichen Zhang, Peng Li, Liang Wang and Wangyang Yu, "Dynamic Adjustment Strategy of n-Epidemic Routing Protocol for Opportunistic Networks: A Learning Automata Approach," KSII Transactions on Internet and Information Systems, vol. 11, no. 4, pp. 2020-2037, 2017. DOI: 10.3837/tiis.2017.04.011

[ACM Style]
Zhang, F., Wang, X., Zhang, L., Li, P., Wang, L., and Yu, W. 2017. Dynamic Adjustment Strategy of n-Epidemic Routing Protocol for Opportunistic Networks: A Learning Automata Approach. KSII Transactions on Internet and Information Systems, 11, 4, (2017), 2020-2037. DOI: 10.3837/tiis.2017.04.011