Path planning for ground-air fusion nodes in Opportunistic networks using Cuckoo search and Grey wolf algorithm for source seeking and Obstacle avoidance

When opportunity network nodes move in remote areas or rugged mountainous areas, the path avoidance planning in the node source finding process is a key factor affecting network performance as well as node lifetime. Therefore, this paper proposes a path planning strategy for opportunistic network ground-air fusion nodes based on cuckoo search and grey wolf algorithm for source seeking and obstacle avoidance(PO-CSGWO). The strategy has the ability of global search and fast convergence. We conduct two-dimensional and three-dimensional source-seeking and obstacle-avoidance tests on the land nodes of the opportunity network and the nodes with short-time flight capability in the air, respectively, and derive the optimal paths. The simulation experiments show that this experiment has very high adaptability for some special areas and improves the network performance substantially.