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Abstract
In order to improve the throughput of opportunistic networks during congestion phase caused by multiple copies packet forwarding method, based on cellular learning automataa novel congestion control strategy was proposed. Different from conventional congestion control strategies, in which only particular information of nodes or packets are considered, this novel strategy takes into account the packets retain information from neighbor nodes. Each node is described as a cellular equipped with multiple learning automata in the network. According to the packets information stored in neighbor nodes, each node updates drop probability of packets under the rule of learning automata automatically. Furthermore, the buffer entropy of each neighbor node is taken into account when a packet is replicated, and a novel policy of dropping and replicating packets is also employed to increase nodes' entropy. The simulation results showed that the present approach effectively reduces the network overhead, packets delivery latency and improves packets delivery ratio. © 2016, Editorial Department of Journal of Sichuan University (Engineering Science Edition). All right reserved.
A distinctive cellular learning automata based routing algorithm is proposed which exploits the ambient nodes feature to polish up the performance of opportunistic networks. The factors of each phase in the routing procedure of store-carry-forward are taken into account. Messages would be dropped on the basis of the dropping probability when congestion occurs during the store phase. Energy consumption would be balanced according to the threshold set by the node itself which is used to accept messages in the carry phase. Connection duration between nodes has been estimated to reduce the energy waste caused by fragment messages transmission during the forwarding process. To evaluate the validity of our proposed algorithm, we conduct comprehensive simulation experiments on the ONE platform. The results show that the proposed routing algorithm achieves higher delivery ratio and less overhead ratio. In addition, it gains a balance of energy consumption and an enhancement of the whole network performances.
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