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A Full-time-utilization Optimization Approach for Computational Nanobiosensing

We have proposed a novel framework of computational nanobiosensing (CONA) by transforming the early tumor detection process into an in vivo function optimization problem. The tumor is viewed as the global optimal solution, which is to be searched for by the nanorobots that act as the computational agents. The external control and tracking devices are used to perform actuation and imaging of nanorobots, which correspond to the optimization and learning processes of CONA, respectively. Our previous investigations are based on the protocol that the nanorobot control and tracking are alternating processes, which means that the optimization procedure does not fully utilize the available time for computing. Considering that the parallel execution of control and tracking is a superior way to realize real-time steering of nanorobots, we propose a full-time-utilization (FTU) optimization approach in this paper to improve the performance of CONA. Some in silico experiments are carried out in a 3D search space to demonstrate the superiority of the proposed FTU optimization approach compared to the previous partial-time-utilization (PTU) one.