Error Performance and Mutual Information for IoNT Interface System

Autor: Yu Li, Lin Lin, Weisi Guo, Dingguo Zhang, Kun Yang
Rok vydání: 2022
Předmět:
Zdroj: IEEE Internet of Things Journal. 9:9831-9842
ISSN: 2372-2541
Popis: Molecular communication and the internet of nanothings (IoNTs) are emerging research hotspots recently, which show great potential in biomedical applications inside the human body. However, how to transmit information from inside body IoNTs to outside devices is seldomly studied. It is well known that the nervous system is responsible for perceiving the external environment and controlling the feedback signals. It exactly works like an interface between the external and internal environment. Inspired by this, this paper proposes a novel concept that one can use the modified nervous system to communicate between IoNT devices and in vitro equipments. In our proposed system, nanomachines transmit signals via stimulating the nerve fiber by the electrode. Then the signals transmit along nerve fibers and muscle fibers. Finally, they cause changes in surface electromyography (sEMG) signals which can be decoded by the body surface receiver. The paper presents the framework of this entire through-body communication system. Each part of the framework is also mathematically modeled. The error probability and mutual information of the system are derived from the communication theory perspective, which are evaluated and analyzed through numerical results. This study can pave the way for the connection of IoNT in vivo to external networks.
Databáze: OpenAIRE