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This paper proposes a topology control method based on formation maintenance and nodes in different groups, which mainly involves issues on the network topology variation caused by the inconsistent velocities of all nodes and communication failure between local nodes and different groups in mobile robot team. It embodies the important role of formation maintenance associated with making groups when topology changes. The simulation result has demonstrated that this method can make effective preservation and restoration on the mobile network communication link.

This paper, based on the application background of the interaction of wireless communication network and UnderWater Sensor Networks(UWSNs), with a view to the positive influence on the performance of UWSNs while multi-gateways on surface as well as their positions vary, regarding the delay, energy consumption and packet loss ratio of network as the optimization goal, the relation model of the number of gateways and network performance is derived. Furthermore, the optimized position of gateways has been determined through a Genetic Algorithm(GA). The simulation results indicate that network delay, energy consumption and loss rate of packet can be efficiently improved by means of multiple gateways, verifying the effectiveness of the model proposed in this paper.

The wide prospects of application of Underwater Acoustic Sensor Networks (UASNs), promoting the technology of UASNs. Network topological structure makes a strong impact on the throughput, power consumption and stability of network, wherefore it is important to optimize topological structure of network for UASNs. The underwater acoustic channel is characterized by limited bandwidth, lower communication rate, resulting in the appearance of network bottleneck and energy greatly depleted of nodes. We aimed at this problem, and proposed a optimization algorithm of topological for UASNs, which uses the condition of optimization with node dump energy, load of network and edge-path respectively. It is distributed and top-down algorithm. We adopt graph theory towards adjusting topological structure. Simulation results show that our optimization method not only balance the energy consumption, but also prolong network lifetime and improve network throughout.