ABSTRACT The beam pattern of the virtual node antenna array (VNAA) in wireless sensor networks has a high maximum sidelobe level (SLL), thereby causing communication interference because of the uncontrollable node positions. A sidelobe and energy optimization array node selection (SEOANS) algorithm is proposed for optimally selecting the sensor nodes to form a VNAA that optimizes the beam pattern of VNAA and reduces the average energy consumption of nodes. SEOANS uses a calculation method to determine the optimal number of array nodes, proposes a node location selection optimization method based on concentric circular ring array and a novel swarm intelligence optimization algorithm called cuckoo search chicken swarm optimization (CSCSO) to optimize the excitation current of each array node. CSCSO uses chaos theory, introduces the inertia weight Lévy flight, and adopts the grade mechanism in chicken swarm optimization to improve the performance of the cuckoo search algorithm. In addition, the scheduling and fault tolerance mechanisms are designed and implemented in SEOANS. Simulation results show that the node position selection optimization method and the excitation current optimization based on CSCSO can effectively reduce the maximum SLL. Furthermore, compared with traditional clustering routing algorithms, SEOANS has advantages in the communication delay and average energy consumption of nodes, thus effectively improving network lifetime. INDEX TERMS Wireless sensor networks, random antenna arrays, cuckoo algorithm, energy efficient, beamforming.
Sun et al. REF propose an algorithm named CSCSO that combines the CS algorithm and the chicken swarm optimization (CSO) algorithm to optimize the beam pattern of virtual node antenna array.
A Sidelobe and Energy Optimization Array Node Selection Algorithm for Collaborative Beamforming in Wireless Sensor Networks
IEEE Access
IEEE Access
Computer Science