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.
