An Opportunistic Networks Load Distribution Model Based on Forwarding Assistance

Active nodes in opportunistic networks have a greater mobility range and undertake more message-forwarding tasks. This leads to issues of uneven traffic load and delayed cache space release in opportunistic networks. This paper proposes a load distribution model based on forwarding assistance (LDMFA) which integrates the node delivery prediction value and traffic idleness index to mitigate the blind selection of relay nodes and reduce message forwarding delay. To identify a selfish node, the message throughput rate of the node is selected as the evaluation index. The most appropriate relay node is selected by comparing the traffic idle index of neighbour nodes and calculating their forwarding assistance. This paper also introduces a cache optimization mechanism to address network performance degradation due to overloaded node traffic. Simulation results show that, compared with the prevailing opportunistic routing algorithms, the model improves the message delivery success ratio by 20%.