A joint optimization approach for distributed collaborative beamforming in mobile wireless sensor networks.

The nodes in mobile wireless sensor networks (MWSNs) are usually with limited hardware resources, which leads to the limitation of transmission range and energy. To extend the communication distance of a single sensor node, distributed collaborative beamforming (DCB) based on a virtual node antenna array (VNAA) can be used in MWSNs. The locations and excitation current weights are the key factors that affect the performance of DCB, thus the nodes of a MWSN can move to better locations to achieve a lower maximum sidelobe level (SLL) of the beam pattern, thereby reducing the communication interferences and enhancing the directivity. However, the moving energy consumption will be increased. In this paper, a joint optimization problem for optimizing the maximum SLL of beam pattern, transmission power and moving energy consumption of DCB nodes in MWSNs is proposed, and the NP-hardness of the formulated problem is proven. Then, we propose a distributed parallel cuckoo search algorithm (DPCSA), which is a nature-inspired approach, to solve the formulated joint optimization problem. Simulation results verify that the maximum SLL, transmission power and moving energy consumption of DCB nodes can be optimized effectively. Moreover, the performance and stability of the proposed DPCSA are evaluated.