The Internet of Bio-Nano Things (IoBNT) enables nanoscale devices to interact with biological systems offering promising applications. Molecular communication (MC), which uses molecules as information carriers, is essential for the implementation of IoBNT systems. As in conventional communication systems, a clock signal is vital for reliable operation. However, in MC, generating a stable clock signal remains a significant challenge. This paper proposes a protein-based clock signal model in which the signal is generated by a mutually inhibitory protein oscillator inside the cell. The signal is transported through the cell membrane surrounding the oscillator via cell-penetrating peptides (CPPs) and diffusion to the transmitters and receivers in a MC system, thereby regulating their periodic activities. The performance of the clock signal in terms of amplitude, clock period and diffusion characteristics is evaluated. Additionally, two triggering mechanisms are introduced to shape the protein clock signals. The simulation results show that the proposed protein circuit generates a stable and periodic clock signal, and effectively regulate the operations of transmitters and receivers. This work lays the foundation for developing stable and efficient clock signal in IoBNT.

The Internet of nano things (IoNTs) envisions thousands of nanomachine nodes that sense, compute, actuate and communicate at the nanoscale. With the development of nanotechnology, DNA technology, a promising way to achieve precise manipulation in the microscopic world, have shown their potential as multiple nanomachine nodes within IoNTs, such as sensing and actuation. However, current DNA nanomachine research mostly focuses on solutions for specific diseases and lacks a generalizable and universal design. This will not only undoubtedly hinder the realization of DNA-based IoNTs but also limit the development of DNA nanomachine. In recent years, researchers has begun to realize this problem and proposed some more integrated and versatile DNA nanomachine designs, incorporating with logic and communication functions. Among these, the integration of molecular communication gives DNA nanomachine the potential to realize IoNTs, making it more promising for future applications. Therefore, this review comprehensively sorts out recent researches of DNA nanomachine over the past decade and discusses them from the perspective of IoNTs systems. In detail, we review and analyze existing DNA nanomachine researches from communication fundamental to various nodes within the network (computing, sensing, actuation, etc.), introduce the technology behind and evaluate the performance of DNA nanomachines. Finally, we present the development prospects of DNA-based IoNTs and discuss the challenges. We hope that this review will help inspire the design of more integrable DNA nanomachines and promote the progress of both DNA nanomachine and IoNTs research.

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.

Molecular communication (MC) is a prominent technique within the Internet of Bio-Nano Things (IoBNT), which aims to interconnect artificial and biological devices for pioneering healthcare applications. Despite its rapid advancements in theoretical study, the experimental development of MC is still in the infancy stage, and most existing testbeds require intruding hardware into the channel during the signal detection process, potentially harming the organism. Against this background, the noninvasive interface across the internal and external information exchange of organisms is of great necessity. Specifically, this work adopts a capacitive sensing mechanism to recover information from the sensed chemical signals. Our testbed demonstrates a notably higher transmission rate than previously reported noninvasive MC approaches, underscoring its potential to advance IoBNT research.

Neural communication systems utilize human neurons to transmit data among biological entities and engineered nanodevices, forming the Internet of Nanothings (IoNT). As a novel research topic, the interference with neural communication systems is not well investigated and modeled. In this article, a simplified neural communication model with external and internal interference is proposed. In our model, external interference from neighboring neurons is characterized by a Poisson distribution, capturing the stochastic nature of interference spikes. Internal interference, attributed to relative refractoriness, which is a neural property reflecting the reduced probability of spike generation shortly after neuron activation, is modeled using a dynamic threshold. The simulation results show that although the only internal interference scenario causes minimal, it still introduces some extra symbol error when external and internal interference both exist. Enhancing the intensity of neural signal stimulation and lengthening the signal period are effective strategies for mitigating the effects of external and internal interference, respectively. The work contributes to a better understanding of neural communication systems and paves the way for the future potential applications of healthcare.

Cooperative molecular communication (MC) is a key enabler for communication between nanomachines in the Internet of Bio-Nano Things (IoBNT). However, its performance is significantly constrained by the limited availability of free energy, which is essential for molecular transport. This paper introduces a novel transmitter model that encodes information by utilizing free energy to transport molecules from a reservoir to the external environment, creating specific concentration ratios in the reservoir for reliable information transmission. The transmitter’s performance is primarily influenced by energy consumption, which directly impacts the system’s bit error rate (BER) in IoBNT. To address these challenges, this study focuses on optimizing energy allocation among multiple transmitters in cooperative MC systems to enhance BER performance. For scenarios with two transmitters, a theoretical analysis of optimal energy allocation is performed, while reinforcement learning (RL) is utilized to determine optimal energy allocation strategies for systems with more than two transmitters. Numerical results demonstrate the effectiveness of the proposed strategies in minimizing BER and improving the overall performance of cooperative MC systems under energy constraints.

Molecular communication (MC) leverages molecules as information carriers, offering advantages such as biocompatibility and low energy consumption. Currently, MC’s research focuses on signal detection using chemical sensors, nanoparticles or biomolecules. However, challenges remain in accurately demodulating sequences of bits, particularly due to the influence of system parameters such as channel length, background flow rate, and transmitter-side actuation settings, including injection volume and valve control timing. To address these challenges, this paper introduces a MC testbed based on particle speed detection, which transforms molecular signals into particle speed signals for communication. Using hydrogen peroxide (H2O2) as the information carrier, the signal is demodulated by mixing the solution at the receiving end with specially prepared active particles and detecting the particle movement speed. Sequential transmission experiments were conducted to analyze the effects of various parameters on system performance. Experimental results demonstrate that the system accurately transmits information within a tested range, validating the theoretical model and highlighting its potential for microscopic communication applications.

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.

Systematic and scientific training is an essential aspect of improving athletes' performance and has garnered significant attention. Athletes are continuously seeking new technologies to aid in training and prevent injuries. Digital twin technology, which integrates the Internet of things (IoT), big data, and artificial intelligence (AI), shows potential for providing athletes with continuous monitoring and in-depth analysis to support their training. This study employs wireless body area network (WBAN) and digital twin (DT) technology to design a novel system for assisting athletes training. On the hardware level, the system utilizes a WBAN with multiple sensors to collect physical and physiological data from athletes. On the software level, the system comprises three modules: motion capture, behavior recognition, and motion correction modules. These modules enable the digital twin to reflect the athlete's movements, behaviors, and physiological attributes in real-time, helping athletes understand their physical condition, correct erroneous movements, and prevent potential health hazards. Furthermore, this study demonstrates how to construct a digital twin human, providing a method that can be transplanted to other scenarios.

Molecular communication (MC) is a promising way to achieve the Internet of nano things (IoNTs), and the theoretical system has been gradually improved in recent years. However, previous studies have more concentrated on system modeling, often simplifying the transceiver into an ideal point model due to the absence of a detailed transceiver design. In this paper, we choose DNA, the natural material to design the MC transceiver benefited from the promoting DNA technology. Specifically, we use DNA origami technology to design the transceiver and DNA strands as the information molecules (IMs). DNA strand displacement (DSD) reactions make up to the mechanism of the transmission and reception of IMs. Furthermore, amplifier is designed based on strand displacement amplification (SDA) mechanism, and the whole MC system works under the control of a timer mechanism which is achieved by the synergy of$\lambda$exonuclease ($\lambda$Exo) and fuel strands, one special DNA strands. For our designed nano transceiver, we modeled the MC system mathematically and analyzed the noise based on the properties of the transceiver. Based on the simulation results, we find the nano transceiver works well in nano-scale which promises the potential for further application in the future.

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.

Neuro-spike communication is a promising communication technique in future nano-scale applications. For example, it can be used as the communication means between biocompatible nanomachines, which sheds light on new solutions to achieve new medical diagnosis and treatment for neurological diseases. The information transmission in the neuro-spike communication channel is regulated by the ability of neurons to change synaptic strength over time, i.e., synaptic plasticity. In this paper, we consider one kind of typical synaptic plasticity, i.e., spike-time dependent plasticity (STDP), where the synaptic weights can be enhanced or decreased depending on temporal correlations between presynaptic spike arrival and postsynaptic firing. The STDP is integrated in the modeling of neuro-spike communication channel, and the mutual information of the system is derived theoretically. The mutual information is also evaluated through simulations under the synaptic noise and different vesicle release probabilities. Through simulations, it is observed that STDP can increase the single-input single-output (SISO) communication rates between synapses.

The Internet of Bio-Nano Things (IoBNT) is a groundbreaking concept in communication and network engineering, aiming to establish connections between biological cells, tissues, nanodevices and the Internet. Within IoBNT, bio-inspired molecular communication (MC) is a promising communication paradigm for facilitating information exchange between biological cells and nanodevices. Blood vessels are essential channels for MC systems in IoBNT within the human body. Typically, cylindrical channels with Newtonian fluid flows are utilized to model vascular channels in MC research. However, blood behaves as a non-Newtonian fluid at low shear rates, resulting in distinct flow velocity profiles that affect the transport of IMs. To analyze the impact of non-Newtonian fluid flow velocity profiles on the transmission of IMs and the performance of MC systems within IoBNT, we develop an MC system focusing on a cylindrical channel with non-Newtonian fluid flow. We obtain the distribution of molecules by deriving the cross-sectional average concentration of IMs and formulate the expression for the probability of IMs observed by the receiver. Furthermore, we introduce a simple channel coding scheme to mitigate the influence of inter-symbol interference (ISI). Moreover, the impacts of the non-Newtonian fluids on MC systems are analyzed in terms of probabilities of detection and false alarm, probability of error, mutual information, and achievable rate. Finally, simulations are performed to validate the analytical results. The findings in this paper highlight the importance of considering the rheological behavior of the fluid in optimizing the system design.

Cooperative molecular communication (MC) is a promising technology for facilitating communication between nanomachines in the Internet of Bio-Nano Things (IoBNT) field. However, the performance of IoBNT is limited by the availability of energy for cooperative MC. This paper presents a novel transmitter design scheme that utilizes molecule movement between reservoirs, creating concentration differences through the consumption of free energy, and encoding information on molecule types. The performance of the transmitter is primarily influenced by energy costs, which directly impact the overall IoBNT system performance. To address this, the paper focuses on optimizing energy allocation in cooperative MC for enhanced transmitter performance. Theoretical analysis is conducted for two transmitters. For scenarios with more than two users, a genetic algorithm is employed in the energy allocation to minimize the total bit error rate (BER). Finally, numerical results show the effectiveness of the proposed energy allocation strategies in the considered cooperative MC system.

Blood vessels are essential communication channels for in-vivo molecular communication (MC) systems. Typically, MC researchers model vascular channels as cylindrical tubes with Newtonian fluid flows. However, blood exhibits as a non-Newtonian fluid at a low shear rate. The characteristics of flow in a non-Newtonian fluid differ from those in a Newtonian fluid, resulting in the transport of information molecules (IMs) in the blood being different from that in Newtonian fluids. To analyze the influences of non-Newtonian fluid flows on the performance of MC systems, we develop an MC system in a tube with a non-Newtonian fluid flow in this paper. We obtain the distribution of molecules by deriving the asymptotic equation for the cross-sectional averaged concentration of IMs and formulate expressions for the first passage time probability (FPTP) and first passage time probability density function (FPTPDF). The derived expressions are validated through particle-based simulations. Moreover, the impacts of the non-Newtonian fluids on MC systems are examined in terms of the average bit error rate (BER) and the data transfer rate. Simulation results are presented to verify the theoretical results.

In some molecular communication (MC) designs, signaling molecules are held in reservoirs of different concentrations. This paper explores two thermodynamic implications of creating such a transmitter. First, it requires energy to generate reservoirs at different concentrations, since their chemical potential is different from the environment. Second, it requires an enormous energy cost to create a pure (or nearly pure) reservoir, so the transmitter is necessarily imperfect. Drawing from the Maxwell's Demon thought experiment, we consider the separation of an environmental mixture into reservoirs of differing concentrations, which requires free energy, but which allows information to be encoded in the difference of concentration. Both theoretical and simulation results indicate that the performance of the transmitter is positively correlated with the amount of consumed free energy. Furthermore, our simulation results indicate that there may be a fundamental thermodynamic tradeoff between energy per bit and bit error rate in MC.

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.

The Internet of Bio-Nanothings (IoBNT) is one of the potential application scenarios in the 6th generation (6G) mobile network, which envisions the interaction between biological cells or nanodevices and the Internet. Molecular communication (MC) may offer an appropriate communication method for the nanodevices using chemical molecules as the information carriers. However, due to the complexity of experiments and insufficient interdisciplinary cooperation, MC study mainly focuses on theoretical research, which seriously hinders the MC’s advancement and further applications. Therefore, it is crucial to explore methods for constructing transmitter and receiver nanomachines to realize practical MC systems. Based on the research progress of micro/nanomachines (MNMs) in recent years, this article summarizes the possible technologies for implementing MNMs used for MC systems within 6G application scenarios.

For a long time, people have carried out various studies on human body communication (HBC) in order to establish a suitable communication link through human body. However, in the galvanic coupled method of HBC, the high current intensity is rarely used to implement the communication link. In the medical field, functional electrical stimulation (FES) is often used to send high intensity electrical pulses to make muscles contract, and this contraction phenomenon will generate surface electromyography (sEMG) signals on the surface of human skins. According to this principle and the galvanic coupling method of HBC, we propose a new digital communication system based on FES and sEMG signal detection with human body as communication channel in this paper. We modulate the transmitted signal into electrical stimulation to stimulate the muscles and detect the sEMG signal caused by it to achieve a complete communication process. The framework of the entire communication system is proposed. Its error performance for different stimulation parameters is tested and evaluated by experiments. Using FES and sEMG signal detection, our work makes a new exploration of HBC at high current intensities and enables a complete communication link. This work is expected to be applied to the HBC design combined with electrical stimulation in medical field.

Terahertz based in-vivo Wireless NanoSensor Networks (iWNSNs) is a new type of NanoSensor Networks which take terahertz wave as its carrier and works in the human body. Multiple nano devices in the network are connected by wireless communication. The propagation characteristics of terahertz wave in-vivo are different from those in free space, and there are more serious molecular absorption noises and path losses. Besides, the nano devices are limited in battery energy, so the existing MAC (Medium Access Control) protocols cannot be directly utilized in the Terahertz based in-vivo Wireless NanoSensor Networks. To investigate the MAC protocol which is suitable for the Terahertz based on iWNSNs Networks, this paper proposes a Prioritized Contention Access Based MAC protocol (PCAB-MAC). Nanosensor nodes access the channel through competition, and a two-way handshake is established to ensure nodes can transmit data undisturbed. Considering that data has different priorities, the PCAB-MAC adopts a policy based on priority, and sets different backoff windows for data of different priorities to ensure priority transmission. The simulation results show that the PCAB-MAC protocol can ensure data transmission without conflict, and has excellent performance in delay and throughput.

Molecular communication and the Internet of Nanothings (IoNT) 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 article proposes a novel concept that one can use the modified nervous system to communicate between IoNT devices andin vitroequipments. 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. This article 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 IoNTin vivoto external networks.

Terahertz based in-vivo Wireless NanoSensor Networks (iWNSNs) is a novel sensor network using electromagnetic waves in terahertz band as carrier and nanotechnology work in human body. Due to the different propagation characteristic of in-vivo terahertz channel and the limited resource of nano device, current MAC (Medium Access Control) protocol cannot be applied to Terahertz based iWNSNs. In this paper, a MAC protocol based on energy scheduling, called ES-MAC, is proposed. This protocol adopts nano energy harvesting system, designs a reward function based on the amount of transmitted data and the priority of data. Nano sensor nodes adopt Sarsa learning algorithm to make dynamic channel access decision according to their status. Simulation results show ES-MAC protocol can decrease average end-to-end delay and prolong lifetime of the network while providing differentiated services.

Molecular communication is a recent research hot spot, which uses molecules as information carrier. It can be used to model targeted drug delivery system. Because drug releasing nanomachine and disease site, e.g., tumor, are always mobile, constant drug rate is not an efficient way. In this paper, an adaptive release rate scheme for targeted drug delivery based on mobile molecular communication is proposed. The mobility features of the drug releasing nanomachine and the disease site are modeled. The drug releasing nanomachine changes its release rate adaptively according to the sensed biomarker concentration which is released by the disease site. The simulation results show that the proposed scheme can effectively control the drug load stable at the disease site.

In recent years, molecular communication and internet of nanothings (IoNTs) are studied intensively for potential biomedical applications inside the human body. However, the communication through the human body, which could exchange data between the IoNTs and the outside human body, is seldomly studied. It is known that the neural system can send signals and receive feedback between the inside body and the outside body. Based on that, this paper proposes a novel concept that people can utilize and modify the existing neural system to transmit signals from the IoNTs to the outside. The nanomachine sends signals via stimulating the nerve fiber by electrodes. The signals propagate through nerves and generate surface electromyography (sEMG) signals which can be used as information received by body surface receiver. The framework of the entire through body communication system is presented and each part under the framework is modeled. The communication performance is evaluated. The study will pave the way for the implementation of connecting the in-body IoNTs with the outside networks.

Synthetic Molecular communications (SMC), as one of the most promising communication paradigms for internet of nano-things (IoNT), is expected to advance many revolutionary areas such as precision drug delivery and biological engineering. Many of the envisioned applications of SMC are in microscale. However, the state-of-the-art SMC testbeds reported in the literature are mostly in macroscale. The lack of microscale communication sub-systems to enable connectivity between individual nanomachines for basic coordination in IoNT is its key technology hindrance. To solve this issue, we propose a microscale SMC modulator which is a key component of microscale SMC system. The proposed microscale SMC modulator is a signal conversion interface to link the macroworld to microworld. It translates an electric signal into biological DNA signal by electrochemical and electrodissolution technique. The modulator is realized by a layer-by-layer assembly technique to immobilize DNA on the surface of gold thin film. And it triggers the selective release of DNA upon the exclusive control of external electric potential signal. The amount of released DNA depends on the amplitude of applied electric stimuli. The DNA release process can be switched off by removing the external electric stimuli and reactivated by reapplying the stimuli. To examine the effectiveness of the proposed modulator, the released DNA concentration is measured by using Nanodrop which is capable to quantify DNA, RNA, and protein samples. And a detection scheme is proposed for signal detection based on the measured data. Experiments show that the proposed setup is able to successfully convert an electric signal representing a sequence of binary symbols into a DNA biological signal with a bit rate of 1 bit/min. This work may help SMC and IoNT to advance from theoretical research towards practical applications.

With the high penetration of large-scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this article, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL-based real-time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL-based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with the deterministic policy gradient algorithm and DDPG, our proposed method has lower time complexity.

Information embedded in the fluid dynamic properties undergo stochastic behaviour when propagating from transmitter (Tx) to receiver (Rx). This is due to the high dimensionality and continuous dynamic forces of the environment, which erodes the achievable mutual information. Quantifying the statistical noise distribution and mutual information with respect to the key fluid dynamic parameters is important to molecular communication. Here, we empirically study macro- scale molecular signal propagation using a planar laser induced fluorescence (PLIF) method. We first statistically characterize both the additive and jitter noise distribution. We show that mutual information is maximized under certain transmission strategies and varies with the receiver size. The statistical results can benefit future studies to analyse the impact on communication reliability, and design superior modulation coding schemes.

Recently, research on mobile molecular communication (MC) has become a trend and hotspot. The timevarying channel impulse response (IR) resulting from the mobile feature makes signal detection schemes for static MC no longer applicable. In this paper, a low complexity and non-coherent detection scheme based on the energy difference between two adjacent symbols is proposed for blind signal detection in mobile scenario providing high data rates. Specifically, we consider the non-coherent signal detection scheme with respect to both the moving transmitter and the moving receiver for the first time. In contrast to existing methods for low data rates and without considering inter-symbol interference (ISI), the proposed method utilizes the ISI to achieve quality detection results at high data rates without knowing the channel state information (CSI). The influences of the mobility parameters and data rates on the bit error rate (BER) are evaluated by simulations. The simulation results demonstrate that the BER performance of the proposed scheme is much lower at the same signal intensity compared with a non-coherent scheme, and the computational complexity can be significantly reduced as well. Consequently, the proposed scheme can reduce the effect of the mobility of the nanomachines in signal detection and has the potential to implement molecular communications, especially in mobile scenarios.

Molecular communication (MC) is a promising paradigm for conveying information at a micro- to nano-scale. Particles such as molecules are emitted and sensed by nanomachines. MC system for fixed nano-machines have been extensively researched while literature about the scenario where nanomachines are mobile is very limited. In this paper, a diffusion-based mobile MC system is considered. Both the transmitter and the receiver move randomly in a free diffusion manner. The closed-form expressions for the mean and the variance of the received signal by considering the randomness of both the mobility of nanomachines and particle counting noise are derived using double integrals. Simulation results validate the derived expressions for the mean and variance of the received signal.

Molecular communication is a very promising research field. It has a broad range of prospective potential applications in biomedical engineering, material manufacturing, etc. Getting the knowledge of the diffusive molecular channel is important for the design and analysis of the molecular communication system. This paper investigates the channel parameter estimation based on a typical inverse Gaussian distributed channel. The estimator of propagation distance, medium velocity and diffusion coefficient are derived by maximum likelihood estimation method. The simulation results validate the effectiveness of the proposed estimators.

Nano communication is a novel communication paradigm. The nano-devices communicate with each other by molecular communication at the nano- or micro-scale. Clock synchronization is an essential issue for the collaboration of the nano-devices. How to synchronize the nanomachine in a mobile scenario is a challenge. This paper propose a method to estimate the clock offset in mobile molecular communication systems. First, we introduce a simple model of clock synchronization in mobile molecular communication system. Next, we propose a method to estimate the clock offset base on Maximum-Likelihood Estimation (MLE) in mobile molecular communication system. Finally, simulations by MATLAB are performed and the results show that the mean square error (MSE) of the estimated clock offsets can be reduced and converges after a number of rounds of message exchange, which manifests the effectiveness of the proposed solution.

With the development of sensor and wireless communication technologies, the concept of body sensor networks (BSNs) is proposed in recent years. BSNs are used to measure a large number of vital signs of the human body. Body sensor devices are typically powered by batteries and difficult to replace, so energy efficient medium access control (MAC) protocols, which can perform medium access mechanism with less energy are necessary. In this paper, we propose a novel energy saving MAC protocol for mobile BSNs based on time division multiple access (TDMA). An uplink, downlink asymmetric network architecture is introduced. A changeable access and sleep frame format as well as a routing algorithm are proposed. Simulation results show that the proposed MAC protocol achieves lower energy consumption than IEEE 802.15.6.