This paper proposes and evaluates Neuronal TDMA, a TDMAbased signaling protocol framework for molecular communication, which utilizes neurons as a primary component to build in-body sensor-actuator networks (IBSANs). Neuronal TDMA leverages an evolutionary multiobjective optimization algorithm (EMOA) that optimizes the signaling schedule for nanomachines in IBSANs. The proposed EMOA uses a population of solution candidates, each of which represents a particular signaling schedule, and evolves them via several operators such as selection, crossover, mutation and offspring size adjustment. The evolution process is performed to seek Pareto-optimal signaling schedules subject to given constraints. Simulation results verify that the proposed EMOA efficiently obtains quality solutions. It outperforms several conventional EMOAs.
Researchers study the TDMA optimization in neuron-based MC, which employs neurons to communicate and built in-body sensor-actuator networks (IBSANs) REF .
Multiobjective TDMA Optimization for Neuron-based Molecular Communication
BODYNETS
Computer Science