Uplink Data Transmission Based on Collaborative Beamforming in UAV-assisted MWSNs

Unmanned aerial vehicles (UAVs) have attracted growing attention in enhancing the performance of mobile wireless sensor networks (MWSNs) since they can act as the aerial base stations (ABSs) and have the autonomous nature to collect data. In this paper, we consider to construct a virtual antenna array (VAA) consists of mobile sensor nodes (MSNs) and adopt the collaborative beamforming (CB) to achieve the long-distance and efficient uplink data transmissions with the ABSs. First, we formulate a high data transmission rate multi-objective optimization problem (HDTRMOP) of the CB-based UAV-assisted MWSN to simultaneously improve the total transmission rates, suppress the total maximum sidelobe levels (SLLs) and reduce the total motion energy consumptions of MSNs by jointly optimizing the positions and excitation current weights of MSN-enabled VAA, and the order of communicating with different ABSs. Then, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with chaos initialization, average grade mechanism and hybrid-solution generate strategy to solve the problem. Simulation results verify that the proposed algorithm can effectively solve the formulated HDTRMOP and it has better performance than some other benchmark methods.

Automated summary

This paper proposes an improved non-dominated sorting genetic algorithm-III (INSGA-III) to solve the optimization problem of UAV-assisted MWSN with collaborative beamforming, achieving improved data transmission rates, suppressed sidelobe levels, and reduced energy consumption of sensor nodes. The algorithm utilizes chaos initialization, average grade mechanism, and hybrid-solution generate strategy to effectively optimize the performance of the network. Simulation results show that the proposed algorithm outperforms other benchmark methods in solving the formulated multi-objective optimization problem.