In opportunistic network, the regular connectivity of nodes and their individual conditions significantly influence message transmission. To address this, we propose an improved spray and wait routing algorithm based on stable transmission capacity evaluation of nodes (referred to as STCESW), which is derived from traditional Prophet and spray and wait algorithms but surpasses them. STCESW incorporates network models that consider node connection stability and transmission capacity, aiming to enhance the update and punishment formulas for node delivery probability. Furthermore, it combines the residual message buffer size of nodes with the angle between motion directions of nodes to determine more efficient and extensive message spraying in the network. The simulation results clearly indicate that STCESW significantly outperforms the other three enhanced algorithms in both SPMBM and RWP. It demonstrates its robust performance in terms of delivery probability, showing an increase ranging from 2% to 87% across diverse node buffer sizes or simulation time intervals. Additionally, it compromises slightly on delivery latency while maintaining relatively stable message latency and buffer time.

Opportunistic mobile networks, as an important supplement to the traditional communication methods in unique environments, are composed of mobile communication devices. It is a network form that realizes message transmission by using the opportune encounter of these mobile communication devices. Consequently, mobile communication devices necessitate periodic contact detection in order to identify potential communication opportunities, thereby leading to a substantial reduction in the already limited battery life of such devices. Previous studies on opportunistic networks have often utilized geographic information in routing design to enhance message delivery rate. However, the significance of geographic information in energy conservation has been overlooked. Furthermore, previous research on energy-efficient routing has lacked diversification in terms of the methods employed. Therefore, this paper proposes a dynamic co-operative energy-efficient routing algorithm based on geographic information perception (DCEE-GIP) to leverage geographic information to facilitate dynamic co-operation among nodes and optimize node sleep time through probabilistic analysis. The DCEE-GIP routing and other existing algorithms were simulated using opportunistic network environment (ONE) simulation. The results demonstrate that DCEE-GIP effectively extends network service time and successfully delivers the most messages. The service time of DCEE-GIP increased by 8.05∼31.11%, and more messages were delivered by 14.82∼115.9%.