With advances in molecular communication and the Internet of Nanothings (IoNT), in-body nanodevice networks have shown great promise in the medical field, yet transmitting information from IoNT to external devices remains the major challenge. As a promising approach, neural communication leverages the nervous system as a data transmission interface, but comprehensive experimental validation remains limited. To address this gap, this paper proposes an enhanced neural communication experimental platform for through-body data transmission. Using the bullfrog sciatic nerve–gastrocnemius muscle as a communication channel, we first investigate the channel impulse response (CIR) and measure neuromuscular responses under varying input intensities and frequencies to determine suitable input parameters. Building on these findings, the platform transmits signals by stimulating the nerve fiber, ultimately inducing surface electromyography (sEMG) signals that are detected by external devices to recover the transmitted data. The results demonstrate that this platform achieves effective information transmission, laying the groundwork for connecting IoNT within the body to external networks.

The Internet of Nanothings (IoNT) enables in-body communication, but transmitting signals to external devices remains a key challenge. Neural communication provides a promising interface, yet existing models often oversimplify membrane potentials as binary states, ignoring their subthreshold oscillatory dynamics. To address this, we propose a biologically realistic neural communication model that incorporates the resonate-and-fire (RF) neuron model, capturing the damped oscillations in membrane potential. Accordingly, we design two coding and modulation schemes: enhanced dual-pulse on-off keying (EDP-OOK), which aligns pulse intervals with the neuron’s oscillatory period for optimal excitation or suppression, and tunable dual-pulse on-off keying (TDP-OOK), which flexibly adjusts pulse intensity for energy-efficient suppression. The transmission efficiency is evaluated using the bit error rate (BER). Simulation results show the proposed schemes achieve reliable transmission with lower power consumption compared to conventional methods. This research opens up possibilities for efficiently connecting IoNT to external networks.

Neural communication and the internet of nanothings (IoNTs) promise to transmit information inside and outside the body, as revolutionary communication paradigms. Undoubtedly, the channel parameter estimation of neural communication is crucial in ensuring reliable information transmission. However, there is currently limited theoretical research on neural communication channel and even fewer related experimental studies. In this paper, we take pH as the primary channel parameter, and propose a channel parameter estimation scheme for neural communication channel based on deep learning (DL). Here, compound action potentials (CAPs) are employed for studying the channel, playing an important role in neural communication. To establish the relationship between CAPs and pH values, we develop an experimental platform to collect CAPs at different pH values. Then, we use the experimental data to train a DL model. The corresponding pH value can be accurately detected when new CAPs are fed into the trained model. Experimental results reveal that the proposed scheme is feasible and highly accurate. This study paves the way for conducting experimental research to ensure reliable information transfer from IoNTs in-vivo to external networks.

The Internet of Nan-othings (IoNTs) have been extensively explored as potential communication technologies for in-body applications. The transmission of information from within the body to the external environment has become a popular and pressing issue that needs to be addressed. Neural communication has been proposed as a promising method, utilizing an action potential (AP), transient changes in membrane potential, as a fundamental unit for communication. Current research conceptualizes the membrane potential into two states: an excited state that generates an AP upon stimulation and a resting state absent of stimulation. However, this assumption overlooks the inherent oscillatory characteristics of membrane potential, which exhibit a discernible sensitivity to specific input frequencies. In this paper, we incorporate the oscillations characterized by the Izhikevich model into the channel model. Here, APs are generated when the inter-spike interval closely aligns with, or is a multiple of, the oscillatory period. Following this, we introduce an adaptive coding and modulation strategy. To represent “1”, a pair of pulses, separated by one period, are used to stimulate an AP. In contrast, to represent”0”, two consecutive pulses, distanced by half a period, are employed to inhibit the membrane potential. The transmission efficiency is evaluated by bit error rate (BER) and mutual information (MI). Numerical simulation results demonstrate that the proposed neural communication system is biologically plausible and exhibits higher resistance to interference. This research opens up possibilities for connecting IoNTs to external networks.

Molecular communication and the Internet of Nanothings (IoNTs) have been extensively studied as potential in-body communication technologies. Communication between the inside and outside of the body, specifically the exchange of data between IoNTs and the external environment, has gradually become a research hotspot. Neural communication theory has been proposed as a promising method for transmitting information between the body's interior and exterior. However, there is currently limited theoretical research on neural communication and even fewer related experimental platform studies. To address this gap, we have constructed a digital communication system based on the sciatic nerve of a bullfrog. This platform is capable of stimulating the nerve trunk to produce electrical signals and receiving signals at the receiving end. This experiment investigates the fundamental characteristics of neural trunk channels, measuring the impulse response and the signal conduction rate. The experimental results demonstrate that signal transmission via the nerve trunk as a channel could be achieved using our experimental platform, thereby demonstrating the possibility of information transmission through the nervous system. This paper paves the way for the implementation of experiments connecting IoNTs within the body to external networks.

In this paper, we propose an adaptive algorithm for sampling and reconstructing two-dimensional fields using mobile sensors that can move to designated locations to collect measurements. During each step, the algorithm selects the most desirable sampling sites from a pool of site candidates based on a Bayesian framework. Simulations show that the algorithm works effectively.

This paper studies the problem of combining tree routing and data compression with explicit side information in wireless sensor networks. We first present our network flow and data rate model based on the observation that in many practical situations, side information providing the most coding gain comes from a few nearby sensors. An optimization problem is then formulated and shown to be NP hard. It is subsequently cast as a mixed integer program. For our particular model, we examine several approximation algorithms, and compare their performances through simulations. Improvement over shortest path trees, which completely ignore the source correlation, is achieved only by judiciously merging flows of correlated data based on the coding gain information.

We implement a two-stage DPCM coding scheme for wireless sensor networks. The scheme consists of temporal and spatial stages that compress data by making predictions based on samples from the past and helping sensors. It continuously monitors the additional gain provided by samples from other sensors, and therefore can be combined with data-centric routing algorithms for joint compression/routing optimization. Backward /spl epsi/-NLMS adaptation is used to better track changing environments and avoid coefficient transmissions. Several simulations are conducted to demonstrate the effectiveness of this coding scheme.

In a sensor network, the data collected by different sensors are often correlated because they are observations of related phenomena. This property has prompted many researchers to propose data centric routing to reduce the communication cost. In this paper, we design heuristic algorithms for combined routing and source coding with explicit side information. We build a data rate model upon the observation that in many physical situations the side information that provides the most coding gain comes from a small number of nearby sensors. Based on this model, we formulate a problem to determine the optimal routes for transmitting data to the fusion center. The overall optimization is NP hard because it has minimum Steiner tree as a sub-problem. We then propose a heuristic algorithm that is inspired by balanced trees that have small total weights and reasonable distance from each sensor to the fusion center. The average performance of the algorithm is analyzed and compared to other routing methods through simulations.