Noise-aware evolutionary TDMA optimization for neuronal signaling in medical sensor-actuator networks

Junichi Suzuki, Pruet Boonma
2014 Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14  
Neuronal signaling is one of several approaches to network nanomachines in the human body. This paper formulates a noisy optimization problem for a neuronal signaling protocol based on Time Division Multiple Access (TDMA) and solves the problem with a noise-aware optimizer that leverages an evolutionary algorithm. The proposed optimizer is intended to minimize signaling latency by multiplexing and parallelizing signal transmissions in a given neuronal network, while maximizing signaling
more » ... ss (i.e., unlikeliness of signal interference). Since latency and robustness objectives conflict with each other, the proposed optimizer seeks the optimal trade-offs between them. It exploits a nonparametric (i.e.. distribution-free) statistical operator because it is not fully known what distribution(s) noise follows in each step/component in neuronal signaling. Simulation results show that the proposed optimizer efficiently obtains quality TDMA signaling schedules and operates a TDMA protocol by balancing conflicting objectives in noisy environments.
doi:10.1145/2598394.2609854 dblp:conf/gecco/SuzukiB14 fatcat:buea5yixsrapjc6pz7g77j7bse