Energy-Efficient Data Collection in Molecular Nanonetworks: An Optimization Framework

Molecular communication (MC), which utilizes molecules to transmit data via diffusion channels, is a prominent system in nanonetworks. In particular, Data-collection (DC) scenarios are a challenging area of research that remains open for further investigation. In this letter, we focus on optimizing the energy efficiency (EE) of a molecular DC nanonetwork comprising a mobile nanorobot (NR) and energy-constrained nanosensors (NSs), taking into account the constraints on the molecular concentration, data rate, and available molecular resources. The defined optimization problem is a nonlinear fractional program that is difficult to solve. To determine the optimal solution, we use Dinkelbach's approach and Lagrangian analysis. The simulation results demonstrate the promising performance of the proposed framework.