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Respiratory diseases pose a significant threat to global public health, as exemplified by the COVID-19 pandemic. Molecular communication (MC), as a new method in communication systems, provides a framework for the modeling of diseases. Current studies, however, largely restrict MC models to transmission scenarios involving a single source and single receiver, leaving scenarios with multiple receivers insufficiently explored. This study investigates respiratory virus transmission through air, applying a single-input-multiple-output (SIMO) MC model to analyze the in vitro transmission process. In this context, a COVID-19-positive individual can transmit the virus to multiple recipients, modeled as a SIMO MC system where the affected person is the transmitter, susceptible individuals are receivers, and the intervening air serves as the communication channel. A theoretical model is developed to elucidate the virus transmission process, yielding foundational analytical expressions for the absorption probability. Numerical data validate the model and reveal factors influencing the cumulative reception probability. The results indicate that both the distance and angle between the transmitter and receiver significantly impact the absorption probability, which decreases with increasing distance and angle. Optimal absorption occurs when the receiver is directly in front of the emitter. These findings introduce a new perspective on viral transmission mechanisms and provide a scientific basis for future prevention and control measures. [ABSTRACT FROM AUTHOR]

In the field of wireless communication, there is growing interest in molecular communication (MC), which integrates nano-, bio-, and communication technologies. Inspired by nature, MC uses molecules to transmit data, especially in environments where EM waves struggle to penetrate. In MC, signals can be distinguished based on molecular concentration, known as concentrated-encoded molecular communication (CEMC). These molecules diffuse through an MC channel and are received via ligand–receptor binding mechanisms. Synchronization in CEMC is critical for minimizing errors and enhancing communication performance. This study introduces a novel preamble-based noncoherent synchronization method, specifically designed for resource-constrained environments like nanonetworks. The method's simple, low-complexity structure makes it suitable for nanomachines, while machine learning (ML) techniques are used to improve synchronization accuracy by adapting to the nonlinear characteristics of the channel. The proposed approach leverages ML to achieve robust performance. Simulation results demonstrate a synchronization probability of 0.8 for a transmitter-receiver distance of 1 cm, given a molecular collection time duration four times the pulse duration. These results confirm the significant benefits of integrating ML, showcasing improved synchronization probability and reduced mean square error. The findings contribute to the advancement of efficient and practical MC systems, offering insights into synchronization and error reduction in complex environments. [ABSTRACT FROM AUTHOR]

Molecular Communication (MOC), a novel communication method between nano-sized devices, has gained attention in recent literature. Numerous MOC models have been utilized to analyze factors such as the number of molecules reaching the receiver and the molecule-interference ratio. However, a common trend observed in the existing MOC models is the predominant use of the Normal distribution function to describe the movement of carrier molecules within the diffusion medium. In contrast to the existing literature, this study aims to thoroughly investigate alternative distribution functions for the diffusion of molecules in the medium, taking into consideration the number of received molecules, in order to identify the MOC model with optimal performance. In this study, the performance of various distribution functions including extreme value distribution (EVRND), normal distribution (NRND), t-distribution (TRND), generalized extreme value distribution (GEVRND), and generalized Pareto distribution (GPRND) were compared using different system parameters to identify the best MOC model. The analysis revealed that the GPRND distribution exhibited the highest performance, while the NRND distribution demonstrated the lowest performance. Given the prevalent use of the NRND distribution in analyzing MOC models within the literature, the significance of this study is further underscored. [ABSTRACT FROM AUTHOR]

Molecular communication (MC) represents a paradigm shift in communication technologies, extending beyond traditional electromagnetic methods by incorporating advances in nanotechnology, biotechnology, and communication theory. This innovative approach holds promise for groundbreaking applications in diverse fields such as medicine, military operations, and environmental monitoring. MC employs molecules to carry and transmit data. In concentration-encoded molecular communication (CEMC), information is represented through the concentration levels of the transmitted molecules. In this study, we introduce a novel method named time-hopping concentration-position shift keying (TH-CPSK), designed to facilitate multiple access within MC networks. The TH-CPSK method encodes information based on the position of the transmitted molecular concentration, significantly enhancing the accuracy of data transmission by reducing interference in MC channels. Numerical simulations reveal that an increase in the number of users adversely affects communication performance. Furthermore, our findings indicate that augmenting the number of hops improves communication performance at transmitter-receiver distances of 1 cm and 2 cm. Conversely, at a distance of 3 cm, performance degradation is observed, attributed to the increased complexity. Therefore, it is important to carefully select the number of hops considering the molecular channel's characteristics. Overall, TH-CPSK can enhance the efficiency and reliability of CEMC systems, offering a significant step forward in the realization of MC's potential applications. [ABSTRACT FROM AUTHOR]

Recently, nano-systems based on molecular communications via diffusion (MCvD) have been implemented in a variety of nanomedical applications, most notably in targeted drug delivery system (TDDS) scenarios. Furthermore, because the MCvD is unreliable and there exists molecular noise and inter symbol interference (ISI), cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells, especially if the separation distance between the nano transmitter and nano receiver is increased. In this work, we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme, while accounting for blood flow effects in terms of drift velocity. The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by the Modified Central Force Optimization (MCFO) algorithm. Unlike the previous work, the probability of bit error is expressed in a closed-form expression. It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver. The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity, detection threshold, location of the nano-relay in the proposed nano system, and molecular drug budget. [ABSTRACT FROM AUTHOR]

Ultra high user density is one of the key requirements in 6G and beyond. Also, in future, THz based communication system and Molecular Communication (MC) will coexist. In such coexist scenarios, Electromagnetic (EM) radiation and in subsequent molecular absorption leads to Radiation Absorption Noise (RAN). However, the RAN and in turn Molecular Information Transfer (MIT) greatly depends on different realistic parameters of human tissues and polarization factor of incident EM radiation. On the other hand, in MC, limited computational capabilities of the information molecules limit them to discriminate against the presence of Bob or Eve. Effect of different parameters of human tissues and polarization factor of incident EM radiation on the secrecy of MC system is modelled and analysed in this research. Basically, this research presents different secrecy measures of the MC system under RAN and therefore, it's effect on MIT is presented. In particular, closed form expressions of different secrecy metrics such as, Secrecy Outage Probability (POUT (Rs)), and Probability of Non-Zero Secrecy Capacity (PNZ-SC (Rs)) are derived in this research work. The effect of impedance of various human tissues like brain, skin and bloods, the effect of polarization factor of incident EM radiation and the effect radiation frequency on each of the proposed secrecy metrics is demonstrated. Different simulated results show a perfect match with the theoretical background. The proposed research is useful in implementation of a secure MC system in a scenario where THz based communication system coexists with MC. [ABSTRACT FROM AUTHOR]

Molecular communication (MC) is a concept in communication engineering, where diffusive molecules are used to transmit information between nano or micro-scale chemical reaction systems. Engineering MC to control the reaction systems in cells is expected for many applications such as targeted drug delivery and biocomputing. Toward control of the reaction systems as desired via MC, it is important to transmit signals without distortion by MC since the reaction systems are often triggered depending on the concentration of signaling molecules arriving at the cells. In this paper, we propose a method to analyze signal distortion caused by diffusion-based MC channels using frequency response of channels. The proposed method provides indices that quantitatively evaluate the magnitude of distortion and shows parameter conditions of MC channels that suppress signal distortion. Using the proposed method, we demonstrate the design procedure of specific MC channels that satisfy given specifications. Finally, the roles of MC channels in nature are discussed from the perspective of signal distortion. [ABSTRACT FROM AUTHOR]

To solve the problem that the existing laminar channel models could not be directly applied to the complex laminar channels with targets, a model-driven channel modeling approach was proposed. The system model for laminar diffusion channels with targets was extended by incorporating additional parameters in the absence of target advection models, and the effects of laminar flow and targets on received molecules were considered. Based on the simulation results, the complex laminar channel with target was approximated as two steady laminar channels, and the point-source-receiver laminar diffusion channel model with considering the presence of target was established. The Levenberg-Marquardt algorithm was employed to learn and predict channel model parameters by the neural network, and the combination of data and model driven (CDMD) detection method was proposed for target identification. The results show that the accuracy of the channel model can be validated through the comparison of formula data and simulated data with correlation coefficient of 0.999 15, which confirms the feasibility of the neural network model. The proposed target detection method can be verified by the binary classification algorithm within the neural network with detection accuracy rate of 98.8%. The CDMD- based detection method requires approximately one-sixth of the data volume needed for data-driven detection methods for maintaining high detection performance. [ABSTRACT FROM AUTHOR]

In the field of wireless communication, there is growing interest in molecular communication (MC), which integrates nano-, bio-, and communication technologies. Inspired by nature, MC uses molecules to transmit data, especially in environments where EM waves struggle to penetrate. In MC, signals can be distinguished based on molecular concentration, known as concentrated-encoded molecular communication (CEMC). These molecules diffuse through an MC channel and are received via ligand–receptor binding mechanisms. Synchronization in CEMC is critical for minimizing errors and enhancing communication performance. This study introduces a novel preamble-based noncoherent synchronization method, specifically designed for resource-constrained environments like nanonetworks. The method’s simple, low-complexity structure makes it suitable for nanomachines, while machine learning (ML) techniques are used to improve synchronization accuracy by adapting to the nonlinear characteristics of the channel. The proposed approach leverages ML to achieve robust performance. Simulation results demonstrate a synchronization probability of 0.8 for a transmitter-receiver distance of 1 cm, given a molecular collection time duration four times the pulse duration. These results confirm the significant benefits of integrating ML, showcasing improved synchronization probability and reduced mean square error. The findings contribute to the advancement of efficient and practical MC systems, offering insights into synchronization and error reduction in complex environments.

On 25 July 2023, a workshop entitled "Molecular Communication Approaches for Wetware Artificial Life" took place as a satellite event at the international conference ALIFE 2023 (The 2023 Conference on Artificial Life). In this report, we comment on the workshop by focusing on the main theme and the motivations that led us to develop this initiative. In particular, we highlight how recent progress in synthetic biology and in the study of molecular communication from an engineering perspective can be fruitfully joined to provide a powerful platform to develop frontier research lines in "wetware" Artificial Life. The talks presented at the workshop are briefly summarized. This report is, ultimately, an opportunity to promote an emerging field that calls for collaborative efforts of scholars from multiple disciplines, from chemistry to molecular biology, from communication engineering to nanotechnology, and up to those interested in more theoretical aspects about complex artificial systems that mimic natural ones. [ABSTRACT FROM AUTHOR]

Various applications of molecular communications (MCs) feature an alarm-prompt behavior for which the prevalent Shannon capacity may not be the appropriate performance metric. The identification capacity as an alternative measure for such systems has been motivated and established in the literature. In this paper, we study deterministic K-identification (DKI) for the discrete-time Poisson channel (DTPC) with inter-symbol interference (ISI), where the transmitter is restricted to an average and a peak molecule release rate constraint. Such a channel serves as a model for diffusive MC systems featuring long channel impulse responses and employing molecule-counting receivers. We derive lower and upper bounds on the DKI capacity of the DTPC with ISI when the size of the target message set $K$ and the number of ISI channel taps $L$ may grow with the codeword length $n$ . As a key finding, we establish that for deterministic encoding, assuming that $K$ and $L$ both grow sub-linearly in $n$ , i.e., $K = 2^{\kappa \log n}$ and $L = 2^{l \log n} $ with $\kappa + 4l \in [0,1)$ , where $\kappa \in [0,1)$ is the identification target rate and $l \in [0,1/4) $ is the ISI rate, then the number of different messages that can be reliably identified scales super-exponentially in $n$ , i.e., $\sim 2^{(n\log n)R}$ , where $R$ is the DKI coding rate. Moreover, since $l$ and $\kappa $ must fulfill $\kappa + 4l \in [0,1)$ , we show that optimizing $l$ (or equivalently the symbol rate) leads to an effective identification rate [bits/s] that scales sub-linearly with $n $ . This result is in contrast to the typical transmission rate [bits/s] which is independent of $n$ .

Molecular communication (MC) represents a paradigm shift in communication technologies, extending beyond traditional electromagnetic methods by incorporating advances in nanotechnology, biotechnology, and communication theory. This innovative approach holds promise for groundbreaking applications in diverse fields such as medicine, military operations, and environmental monitoring. MC employs molecules to carry and transmit data. In concentration-encoded molecular communication (CEMC), information is represented through the concentration levels of the transmitted molecules. In this study, we introduce a novel method named time-hopping concentration-position shift keying (TH-CPSK), designed to facilitate multiple access within MC networks. The TH-CPSK method encodes information based on the position of the transmitted molecular concentration, significantly enhancing the accuracy of data transmission by reducing interference in MC channels. Numerical simulations reveal that an increase in the number of users adversely affects communication performance. Furthermore, our findings indicate that augmenting the number of hops improves communication performance at transmitter-receiver distances of 1 cm and 2 cm. Conversely, at a distance of 3 cm, performance degradation is observed, attributed to the increased complexity. Therefore, it is important to carefully select the number of hops considering the molecular channel’s characteristics. Overall, TH-CPSK can enhance the efficiency and reliability of CEMC systems, offering a significant step forward in the realization of MC’s potential applications.

The ubiquity of information transmission via molecular communication between cells is comprehensively documented on Earth; this phenomenon might even have played a vital role in the origin(s) and early evolution of life. Motivated by these considerations, a simple model for molecular communication entailing the diffusion of signaling molecules from transmitter to receiver is elucidated. The channel capacity C (maximal rate of information transmission) and an optimistic heuristic estimate of the actual information transmission rate ℐ are derived for this communication system; the two quantities, especially the latter, are demonstrated to be broadly consistent with laboratory experiments and more sophisticated theoretical models. The channel capacity exhibits a potentially weak dependence on environmental parameters, whereas the actual information transmission rate may scale with the intercellular distance d as ℐ ∝ d−4 and could vary substantially across settings. These two variables are roughly calculated for diverse astrobiological environments, ranging from Earth's upper oceans (C ∼ 3.1 × 10 bits/s; ℐ ∼ 4.7 × 10−2 bits/s) and deep sea hydrothermal vents (C ∼ 4.2 × 10 bits/s; ℐ ∼ 1.2 × 10−1 bits/s) to the hydrocarbon lakes and seas of Titan (C ∼ 3.8 × 10 bits/s; ℐ ∼ 2.6 × 10−1 bits/s). [ABSTRACT FROM AUTHOR]

The molecular communication system (MCS) is mainly based on the design structure of the nanodevices which are employed as nano-transmitter (Nano-TX) and nano-receiver (Nano-RX), owing to the limited drug-reservoir capacity. The current work addresses the physical design of such nanodevices and the coordination of molecular communication to accomplish an end-to-end capacity system model, which can be employed in the targeted drug delivery system (TDDS). In MCS, Nano-RX is a spherical structure with a cylindrical shell with adapting receptors that enables an increase in the received number of drug molecules according to the transmission rate. On the other hand, the more realistic structure of Nano-TX is a cylindrical reservoir capable of controlling the emitted nanocarriers in the blood vessel. The analytical framework and the performance of the proposed MCS are presented by using a compartmental model while a closed-form expression of the proposed end-to-end channel capacity is obtained. The performance evaluation is evaluated by applying network performance metrics such as channel capacity, throughput, and efficiency. The simulation results show that the proposed model can enhance the delivery of the therapeutic dose and thus minimize the side effects on healthy cells compared with conventional schemes. [ABSTRACT FROM AUTHOR]

On 25 July 2023, a workshop entitled "Molecular Communication Approaches for Wetware Artificial Life" took place as a satellite event at the international conference ALIFE 2023 (The 2023 Conference on Artificial Life). In this report, we comment on the workshop by focusing on the main theme and the motivations that led us to develop this initiative. In particular, we highlight how recent progress in synthetic biology and in the study of molecular communication from an engineering perspective can be fruitfully joined to provide a powerful platform to develop frontier research lines in "wetware" Artificial Life. The talks presented at the workshop are briefly summarized. This report is, ultimately, an opportunity to promote an emerging field that calls for collaborative efforts of scholars from multiple disciplines, from chemistry to molecular biology, from communication engineering to nanotechnology, and up to those interested in more theoretical aspects about complex artificial systems that mimic natural ones. [ABSTRACT FROM AUTHOR]

Molecular communication (MOC), which is proposed as a new communication method between nano-sized devices (nanomachines), holds the potential to be used for diagnosing and treating diseases such as Alzheimer's, Spinal Cord, and Parkinson's in the future. Several methods for analyzing MOC models can be found in the literature. However, many of them propose using constant transmitters and receivers in a diffusion environment. In contrast to the existing literature, this study proposes a MOC model that considers the transmitter, receiver, and molecule as mobile entities in the diffusion environment. Furthermore, the system's performance has been improved by identifying optimal parameters, such as the diffusion coefficient that controls mobility and the receptor radius that directly affects the system's performance, using the equilibrium optimization (EO) algorithm and enhanced equilibrium optimization (E 2 O) algorithms. All optimized results are presented with mean and standard deviation values, demonstrating the reproducibility and consistency of the findings. In conclusion, solving the stochastic MOC problem with deterministic approaches such as the EO and E 2 O algorithms offers several advantages. [ABSTRACT FROM AUTHOR]

Recent nanomedical applications, particularly targeted drug delivery system (TDDS) scenarios, have made use of molecular communications via diffusion (MCvD) based on nanosystems. In order to improve the performance of such nanosystems, nanonetworks-based molecular communication is investigated. Employing a nanorelay approach and cooperative molecular communications, we provide a method for optimizing the performance of the nanosystem while taking blood flow effects into consideration in terms of drift velocity. Unlike the earlier studies, the position of the nanorelay and the allocated amount of molecular drugs will be optimized. We provide closed-form expressions for molecular channel capacity and the error probability of a molecular frame. According to the simulation results, it is possible to significantly reduce error probability of a molecular frame and thus increase channel capacity by optimizing the drift velocity, detection threshold and the location of the nanorelay within the proposed nanosystem. [ABSTRACT FROM AUTHOR]

Relaying conformational change over several nanometers is central to the function of allosterically regulated proteins. Replicating this mechanism artificially would provide important communication tools, but requires nanometer‐sized molecules that reversibly switch between defined shapes in response to signaling molecules. In this work, 1.8 nm long rigid rod oligo(phenylene‐ethynylene)s are scaffolds for switchable multi‐squaramide hydrogen‐bond relays. Each relay can adopt either a parallel or an antiparallel orientation relative to the scaffold; the preferred orientation is dictated by a director group at one end. An amine director responded to proton signals, with acid‐base cycles producing multiple reversible changes in relay orientation that were reported by a terminal NH, which is 1.8 nm distant. Moreover, a chemical fuel acted as a dissipative signal. As the fuel was consumed, the relay reverted to its original orientation, illustrating how information from out‐of‐equilibrium molecular signals can be communicated to a distant site. [ABSTRACT FROM AUTHOR]

Information transmission via communication between agents is ubiquitous on Earth, and is a vital facet of living systems. In this paper, we aim to quantify this rate of information transmission associated with Earth's biosphere and technosphere (i.e., a measure of global information flow) by means of a heuristic order-of-magnitude model. By adopting ostensibly conservative values for the salient parameters, we estimate that the global information transmission rate for the biosphere might be ∼ 10 24 bits/s, and that it may perhaps exceed the corresponding rate for the current technosphere by ∼9 orders of magnitude. However, under the equivocal assumption of sustained exponential growth, we find that information transmission in the technosphere can potentially surpass that of the biosphere ∼90 years in the future, reflecting its increasing dominance. [ABSTRACT FROM AUTHOR]

Molecular communication is a novel approach for data transmission between miniaturised devices, especially in contexts where electrical signals are to be avoided. The communication is based on sending molecules (or other particles) at nanoscale through a typically fluid channel instead of the "classical" approach of sending electrons over a wire. Molecular communication devices have a large potential in future medical applications as they offer an alternative to antenna-based transmission systems that may not be applicable due to size, temperature, or radiation constraints. The communication is achieved by transforming a digital signal into concentrations of molecules that represent the signal. These molecules are then detected at the other end of the communication channel and transformed back into a digital signal. Accurately modeling the transmission channel is often not possible which may be due to a lack of data or time-varying parameters of the channel (e.g., the movements of a person wearing a medical device). This makes the process of demodulating the signal (i.e., signal classification) very difficult. Many approaches for demodulation have been discussed in the literature with one particular approach having tremendous success – artificial neural networks. These artificial networks imitate the decision process in the human brain and are capable of reliably classifying even rather noisy input data. Training such a network relies on a large set of training data. As molecular communication as a technology is still in its early development phase, this data is not always readily available. In this paper, we discuss neural network-based demodulation approaches relying on synthetic simulation data based on theoretical channel models as well as works that base their network on actual measurements produced by a prototype test bed. In this work, we give a general overview over the field molecular communication, discuss the challenges in the demodulations process of transmitted signals, and present approaches to these challenges that are based on artificial neural networks. [ABSTRACT FROM AUTHOR]

Molecular communication (MC) is a modern communication paradigm inspired by biological mechanisms and systems. Due to the short range of molecular diffusion, MC systems necessitate a multi-hop diffusion-based network to transmit information. Finding the optimal routing path is one of the most critical challenges in MC. The main goal is to transfer information through the diffusion of molecules within an optimal state by detecting the shortest route and the proper relays. In this paper, finding the optimal routing path using a genetic algorithm (GA) is investigated in order to find the shortest and the most energy-efficient path. Our model intelligently plans the optimum trajectory between the transmitter (TX) and the receiver (RX) by identifying the appropriate relays both locally and globally. Our GA implementation uses a variable-length chromosome encoding to obtain the optimal path by selecting an appropriate fitness function. We also examine and compare the performance of the proposed algorithm with Dijkstra’s algorithm (DA), which is one of the deterministic algorithms. Finally, various simulations for different sizes of MC networks are performed to verify the accuracy of the proposed method. Our simulation results demonstrate that the presented GA offers an accurate routing path within an excellent time, even in large-sized environments.

The differential equations describing reaction-diffusion systems in molecular communication are non-linear and do not admit closed-form solutions in general. Consequently, characterizing optimal modulation and coding strategies is a key challenge in molecular communication. In this paper, we consider a concrete example and illustrate the application of tools from real and complex analysis to study this difficult problem. We show that the optimal release pattern is a finite sum of Dirac’s delta functions in our working example. Simulation results validate the discrete nature of the optimal release pattern. We also provide an upper bound on the numbers of delta functions under some assumptions.

In this paper, Molecular Communication (MC) system, which is likely to be used in a variety of domains including targeted medicine delivery and health monitoring, is investigated over the channel under dynamic scenario. A diffusive mobile MC system with a pair of transmitter and receiver nano- machines suspended in a fluid medium with a uniform flow is considered. It is noteworthy to mention that the Channel Impulse Response (CIR) changing over time and irregular movements of the transmitter as well as receiver nano-machines make MC system most challenging. Establishing reliable communication between mobile nano-machines in such scenario is even more challenging as its statistics change with time. However, Signal to Noise Ratio (SNR) is most suitable parameter which is used to quantify the system reliability. Therefore, this research investigates the effect of mobility of transmitter and/or receiver nano machines on SNR. In particular, SNR under three dynamic scenarios is derived and analysed with the help numerical quantification. In the first scenario, both the transmitter and receiver are in motion. Whereas, in the second scenario, the transmitter is in motion and the receiver is fixed. Finally, in the third scenario, the transmitter is fixed and the receiver is in motion. For each scenarios, effect of different physical parameters such a diffusion coefficient, emission time, concentration of information molecules, radius of receiver and delay time are included in the analysis. The simulation results shows perfect agreement with the theoretical background. Simulation is performed in Maple-18. [ABSTRACT FROM AUTHOR]

As manipulating the self-assembly of supramolecular and nanoscale constructs at the single-molecule level increasingly becomes the norm, new theoretical scaffolds must be erected to replace the thermodynamic and kinetics based models used to describe traditional bulk phase syntheses. Like the statistical mechanics underpinning these latter theories, the framework we propose uses state probabilities as its fundamental objects; but, contrary to the Gibbsian paradigm, our theory directly models the transition probabilities between the initial and final states of a trajectory, foregoing the need to assume ergodicity. We leverage these probabilities in the context of molecular self-assembly to compute the overall likelihood that a specified experimental condition leads to a desired structural outcome. We demonstrate the application of this framework to a toy model in which N identical molecules can assemble into oligomers of different lengths and conclude with a discussion of how the high computational cost of such a fine-grained model can be overcome through approximation when extending it to larger, more complex systems. [ABSTRACT FROM AUTHOR]

This paper proposes a feedback-controlled adaptive method for detecting signals in diffusion-based molecular communication (MC) systems. Signal detection via a receiver nanomachine is a critical challenge for the exchange of information in MC systems. Incorrect estimations or small errors in signal detection can lead to high data detection errors. Existing methods for improving detection performance require high time costs or computational complexity. This paper proposes a simple and practical method that enables receiver nanomachines to automatically estimate signal detection times according to the measured molecular concentrations and weighted feedback errors. The proposed method adjusts the detection time even when the initial parameter values of the system are unknown to the receiver nanomachines. Simulations were performed to evaluate the bit error rate performance of the proposed and existing methods in terms of different data rates, transmission distances, and estimation error lengths under different initial conditions. The simulation results reveal that the implementation of the proposed method is simpler and demonstrates superior performance compared with that of existing methods. [ABSTRACT FROM AUTHOR]

Within many chemical and biological systems, both synthetic and natural, communication via chemical messengers is widely viewed as a key feature. Often known as molecular communication, such communication has been a concern in the fields of synthetic biologists, nanotechnologists, communications engineers, and philosophers of science. However, interactions between these fields are currently limited. Nevertheless, the fact that the same basic phenomenon is studied by all of these fields raises the question of whether there are unexploited interdisciplinary synergies. In this paper, we summarize the perspectives of each field on molecular communications, highlight potential synergies, discuss ongoing challenges to exploit these synergies, and present future perspectives for interdisciplinary efforts in this area. [ABSTRACT FROM AUTHOR]

The importance of communication technology in day-to-day life and the impact of state-of-the-art miniaturization at the nanoscale on all-round progress are well recognized. Molecular communication, which is ubiquitous in natural biological systems, is a hybrid of these. We have described the basic concepts involved, the architectural aspects, and the applications of this emerging area of immense interest. The applications, both realized in practice as well as envisioned, cover biomedical (mainly healthcare), environmental, industrial and information technology realms. [ABSTRACT FROM AUTHOR]

With the advancement of bioengineering and nano-technology, next-generation network architecture is being equipped with nano-devices to improve its scalability. Inspired by naturally existing biological phenomena of communication, molecular communication-based nano-networks are designed on the same principles. In existing communication systems, information is transmitted by electromagnetic or electrical signals. However, these methods of communication are inconvenient for many applications where the ratio of antenna size to the wavelength of a signal is a constraint. In such scenarios, a molecular communication scheme can be employed to solve such issues. Here chemical signals act as information carriers. These signals are biocompatible and can be used in body area networks (BANs). In this paper, a nanonetwork in which communication through diffusion takes place is simulated and evaluated for various signal metrics (delay, distortion, bit rate). The concentration of molecules in pheromone signaling communication can be used as a channel transfer function in the respective molecular communication model used in nano-devices. A stochastic Single-Input-Single-Output (SISO) communication system is simulated for purpose of analysis. [ABSTRACT FROM AUTHOR]

Molecular communication (MC) is one of the emerging technologies enabling nanonetworks and the Internet of Everything (IoE). The practical implementation of the intra-body MC systems is crucial for realizing smart healthcare applications, i.e., drug delivery, early detection, and health monitoring, through communication between nanomachines. A Flexure field-effect transistor (FET) based MC receiver, providing high sensitivity by utilizing nonlinear electromechanical coupling, has recently been proposed. It can also identify neutral molecules, unlike bioFETs. Thus, virus or pathogen detection can be performed with onboard computing by these receivers placed in the Edge. To date, biosensor-based MC receivers have been analyzed only for concentration shift keying (CSK), although weight shift keying (WSK) is a very robust modulation technique. The Flexure-FET-based MC receiver is a great candidate for use in a WSK-based MC system since its transduction mechanism relies on the molecular weight. This work presents the first practical approach to a WSK-based MC system with an improved Flexure-FET-based MC receiver. Its key performance metrics are analyzed from a theoretical MC perspective, also considering biological interference to obtain a more realistic simulation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-layer heterogeneous network to realize the remote monitoring and advanced warning for infectious diseases spread by airborne pathogens, whose detection can be considered as a binary detection problem. To intuitively study the detection process, we abstract it as a molecular communication via diffusion (MCvD) model and uncover that one of the key factors to impact the detection performance is inter-symbol interference (ISI), i.e., the viral aerosol from other biological entities. Therefore, overcoming ISI is imperative to ensure reliable detection. In the abstracted MCvD model, the detection performance can be described by the bit error rate (BER) performance. Following this assumption, we propose to optimize the detection interval to minimize the impact of ISI while ensuring the accurate detection of the transmitted information symbol, which is suitable for both the absorbing and passive receivers. For tractability, based on the signal-to-interference difference (SID) and signal-to-interference-and-noise amplitude ratio (SINAR), we design a modified-SINAR (mSINAR) to measure BER performance for the MCvD system with a variable detection interval. Besides, we derive the optimal detection interval in closed-form. Using simulation results, we show that in terms of BER, our proposed mSINAR scheme is superior to the competitive schemes, and performs similarly to the scheme with optimal intervals determined by the exhaustive search. [ABSTRACT FROM AUTHOR]

In this letter, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval. Its rationale is to exploit the discarded duration (i.e., the symbol duration outside this optimal interval) to relieve ISI in the target system. Following this idea, we formulate an objective function to quantify the impact of the discarded time on bit error rate (BER) performance. Besides, an optimally reusable interval within the discarded duration is derived in closed form, which applies to both the absorbing and passive receivers. Finally, numerical results validate our analysis and show that for the considered MCvD system, significant BER improvements can be achieved by using the derived reusable duration. [ABSTRACT FROM AUTHOR]