Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint

Abstract

Network topology construction plays an important role in the application of large-scale unmanned aerial vehicle (UAV) swarm. Current researches usually perform the topology construction in terms of criteria of nodes energy consumption, transmission delay and network throughput, etc. However, another important criterion, the stability of swarm network topology, which is much critical for dynamic scenarios, has not been fully considered. In this paper, a novel topology construction method for UAV swarm network based on the criterion of topology duration is proposed. Specially, the topology construction of swarm network is formulated as an optimization problem of maximizing the topology duration while satisfying the constraints of certain network throughput, end-to-end delay, and nodes energy consumption. Then, a novel Group Trend Similarity based double-head Clustering method(GTSC) is employed to solve this problem, in which group similarity of movement, intra- and inter-cluster distance, node forwarding delay, and energy strategy are comprehensively taken into account. The proposed method is effective when used to perform the network topology construction for UAV swarm, which is verified by the simulation results. Furthermore, in comparison with representative algorithms, the proposed GTSC method exhibits better performance on topology duration, network throughput, end-to-end delay and energy consumption balance especially in a large-scale swarm scenarios.

Wireless Communication: Applications Security and Reliability—Present and Future

“…The authors also designed an approximation algorithm to solve the formulated problem. In [21], a novel topology construction method for a UAV swarm network that takes into account the criterion of topology duration in addition to other important criteria such as network throughput, end-to-end delay, and node energy consumption is proposed. The paper aims to formulate the topology construction of a swarm network as an optimization problem and solve it using a double-head clustering method that considers group similarity of movement, intra-and inter-cluster distance, node forwarding delay, and energy strategy.…”

Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things

“…We iterate this log * |η| time; therefore, the symbol at a location, i, is based on log * |η| for the left labels. As soon as we accomplish the last step of reducing the ∑ form ∑ of six (6) to ∑ of three (3), the last label at a location, i, is based on a max of three extra labels to its right and left. We must tag every local maximum location and, formerly, every local minimum position not contiguous to a local maximum; therefore, we must inspect max two symbols to the right of i and two labels to the left, which, in order, depends on three labels to the right and log * |η| + 3 labels to the left.…”

“…The researchers tried to minimize energy utilization in other wireless networks, but energy utilization still exists. Due to the flying speed of UAVs, the rapid variation in topology, terrain structure, and diverse directions make it difficult to collect and route information [6]. The researchers proposed energy-efficient schemes by considering different parameters such as reducing the communication distance, computation cost, mobility, and degree.…”