Diffusion-based Molecular Communication (MC) is inherently challenged by severe inter-symbol interference (ISI). This is significantly amplified in mobile scenarios, where the channel impulse response (CIR) becomes time-varying and stochastic. Obtaining accurate Channel State Information (CSI) for traditional model-based detection is intractable in such dynamic environments. While deep learning (DL) offers adaptability, its complexity is unsuitable for resource-constrained micro/nanodevices. This paper proposes a low-complexity Reservoir Computing (RC) based detector. The RC architecture utilizes a fixed, recurrent non-linear reservoir to project the time-varying received signal into a high-dimensional state space. This effectively transforms the complex temporal detection problem into a simple linear classification task, capturing ISI dynamics without explicit channel modeling or complex retraining. Evaluated in a realistic 3D mobile MC simulation environment (Smoldyn), our RC detector significantly outperforms classical detectors and achieves superior performance compared to complex ML methods (LSTM, CNN, MLP) under severe ISI. Importantly, RC achieves this with significantly fewer trainable parameters (e.g., 300 vs. up to 264k for MLP) and ultra-low latency inference (approx. 1 $μ$s per symbol).

The Internet of Bio-Nano Things (IoBNT) promises to revolutionize healthcare by interfacing the cyber domain with living systems at unprecedented resolution. Realizing this vision hinges on the development of Bio-Nano Things (BNTs), i.e., functional nodes capable of sensing, actuation, and communications within biological environments. Existing BNT architectures, e.g., nanomaterial-based, biosynthetic, and passive molecular agents, face significant limitations, including toxicity, lack of autonomy, or metabolic burdens. This paper posits a fourth paradigm: transient hijacking of living cells via non-genetic cell surface engineering (NG-CSE) to enable living BNTs. NG-CSE allows for the precise, reversible functionalization of cell membranes with synthetic molecular machinery, reprogramming cellular functions and interactions without genomic modification. It uniquely combines the biocompatibility and agency of cells with the nanotechnology-enabled programmability, circumventing key risks of genetic engineering. We review the NG-CSE toolbox and explore the opportunities it unlocks for IoBNT, including programmable cell-cell communication, dynamic network topologies, and improved bio-cyber interfacing. We propose novel IoBNT architectures that leverage these capabilities, such as circulating sentinel networks exploiting cellular agency for liquid biopsy, and rationally designed,in vitrobiocomputers exploiting interkingdom interactions. Finally, we introduce tractable mathematical frameworks and performance metrics for lifecycle degradation and agency-aware networking, providing a conceptual roadmap to guide future experimental validation and effective utilization of NG-CSE-enabled living BNTs within IoBNT.

Molecular Communication (MC) is a bio-inspired communication paradigm utilizing molecules for information transfer. Research on MC has largely transitioned from theoretical investigations to practical testbed implementations, harnessing microfluidics and sensor technologies. Accurate models for input-output relationships on these platforms are crucial for optimizing MC methods and understanding the impact of physical parameters on performance. Our study focuses on a practical microfluidic MC system with a graphene field effect transistor biosensor (bioFET)-based receiver, developing an end-to-end frequency-domain model. The model provides insights into the dispersion, distortion, and attenuation of received signals, thus potentially informing the design of new frequency-domain MC techniques, such as modulation and detection methods. The accuracy of the developed model is verified through particle-based spatial stochastic simulations of pulse transmission and ligand-receptor reactions on the receiver surface.

Molecular Communications (MC) is a bio-inspired communication paradigm using molecules as information carriers, necessitating novel transceivers and modulation/detection techniques. In realizing practical MC receivers (MC-Rxs), biosensor field-effect transistor (bioFET)-based architectures are promising, having surface receptors that undergo reversible reactions with ligands. These interactions are converted into electrical signals via field effect, enabling the decoding of transmitted information. A significant challenge in these receivers is the limited specificity of receptors to target ligands, which leads to molecular cross-talk from similar interfering ligands co-existing in the MC channel. Decoding transmitted symbols under such interference is challenging in the time domain, especially when MC-Rx lacks prior knowledge of interferer statistics or operates near saturation. To address this, we introduce a frequency-domain detection (FDD) technique for bioFET-based MC-Rxs, which exploits the distinct binding reaction rates of different ligand types, reflected in the power spectrum of binding noise. Compared to conventional time-domain detection (TDD) technique, this method offers improved detection performance under stochastic molecular interference. We analyze the bit error probability (BEP) of FDD, confirming its superior performance in various interference scenarios. Moreover, the theoretical performance limits of FDD are validated through a particle-based spatial stochastic simulator, simulating binding reactions on MC-Rx within microfluidic channels.

Molecular Communications (MC) is a bio-inspired communication paradigm which uses molecules as information carriers, thereby requiring unconventional transmitter/receiver architectures and modulation/detection techniques. Practical MC receivers (MC-Rxs) can be implemented based on field-effect transistor biosensor (bioFET) architectures, where surface receptors reversibly react with ligands, whose concentration encodes the information. The time-varying concentration of ligand-bound receptors is then translated into electrical signals via field-effect, which is used to decode the transmitted information. However, ligand-receptor interactions do not provide an ideal molecular selectivity, as similar types of ligands, i.e., interferers, co-existing in the MC channel can interact with the same type of receptors, resulting in cross-talk. Overcoming this molecular cross-talk with time-domain samples of the Rx's electrical output is not always attainable, especially when Rx has no knowledge of the interferer statistics or it operates near saturation. In this study, we propose a frequency-domain detection (FDD) technique for bioFET-based MC-Rxs, which exploits the difference in binding reaction rates of different types of ligands, reflected to the noise spectrum of the ligand-receptor binding fluctuations. We analytically derive the bit error probability (BEP) of the FDD technique, and demonstrate its effectiveness in decoding transmitted concentration signals under stochastic molecular interference, in comparison to a widely-used time-domain detection (TDD) technique. The proposed FDD method can be applied to any biosensor-based MC-Rxs, which employ receptor molecules as the channel-Rx interface.

Inspired by nature, molecular communications (MC), i.e., the use of molecules to encode, transmit, and receive information, stands as the most promising communication paradigm to realize the nanonetworks. Even though there has been extensive theoretical research toward nanoscale MC, there are no examples of implemented nanoscale MC networks. The main reason for this lies in the peculiarities of nanoscale physics, challenges in nanoscale fabrication, and highly stochastic nature of the biochemical domain of envisioned nanonetwork applications. This mandates developing novel device architectures and communication methods compatible with MC constraints. To that end, various transmitter and receiver designs for MC have been proposed in the literature together with numerable modulation, coding, and detection techniques. However, these works fall into domains of a very wide spectrum of disciplines, including, but not limited to, information and communication theory, quantum physics, materials science, nanofabrication, physiology, and synthetic biology. Therefore, we believe it is imperative for the progress of the field that an organized exposition of cumulative knowledge on the subject matter can be compiled. Thus, to fill this gap, in this comprehensive survey, we review the existing literature on transmitter and receiver architectures toward realizing MC among nanomaterial-based nanomachines and/or biological entities and provide a complete overview of modulation, coding, and detection techniques employed for MC. Moreover, we identify the most significant shortcomings and challenges in all these research areas and propose potential solutions to overcome some of them.

Molecular communication (MC) uses molecules as information carriers between nanomachines. MC channel in practice can be crowded with different types of molecules, i.e., ligands, which can have similar binding properties causing severe cross-talk on ligand receptors. Simultaneous sensing of multiple ligand types provides opportunities for eliminating interference of external molecular sources and multi-user interference, and developing new multiple access techniques for MC nanonetworks. In this paper, we investigate channel sensing methods that use only a single type of receptors and exploit the amount of time receptors stay bound and unbound during ligand-receptor binding reaction to concurrently estimate the concentration of multiple types of ligands. We derive the CramérRao Lower Bound for multi-ligand estimation, and propose practical and low-complexity suboptimal estimators for channel sensing. We analyze the performance of the proposed methods in terms of normalized mean squared error (NMSE), and show that they can efficiently estimate the concentration of ligands up to 10 different types with an average NMSE far below 10-2. Lastly, we propose a synthetic receptor design based on modified kinetic proofreading scheme to sample the unbound and bound time durations, and a chemical reaction network to perform the required computations in synthetic cells.

Molecular communication (MC) is the most promising communication paradigm for nanonetwork realization since it is a natural phenomenon observed among living entities with nanoscale components. Since MC significantly differs from classical communication systems, it mandates reinvestigation of information and communication theoretical fundamentals. The closest examples of MC architectures are present inside our own body. Therefore, in this paper, we investigate the existing literature on intrabody nanonetworks and different MC paradigms to establish and introduce the fundamentals of molecular information and communication science. We highlight future research directions and open issues that need to be addressed for revealing the fundamental limits of this science. Although the scope of this development encompasses wide range of applications, we particularly emphasize its significance for life sciences by introducing potential diagnosis and treatment techniques for diseases caused by dysfunction of intrabody nanonetworks.

Molecular devices, which consist of single or a few molecules, are envisioned to perform advanced tasks such as molecular information processing and collaborative sensing/actuating if they are operated in a cooperative manner. To connect these nanoscopic primitive devices with each other and with macroscale networks, and thus, to realize the internet of molecular devices, requires fundamentally different and novel approaches, other than the molecular or electromagnetic nanocommunications. Recently, we proposed and studied the use of Förster resonance energy transfer (FRET), which is a short-range nonradiative energy transfer process between fluorophores, as a high-rate and reliable wireless communication mechanism to connect fluorophore-based photoactive molecular devices. In this paper, we provide an in-depth architectural view of this new communication paradigm with a focus on its peculiarities, fundamental principles, and design requirements by comprehensively surveying the theoretical and experimental positions and ideas. We give an overview of networking opportunities offered by the intrinsic capabilities of fluorophores under the novel concept of Internet of Molecular Things. We present some prospective applications, theoretical modeling approaches, and experimental opportunities, and finally discuss the implementation challenges.

Molecular communication (MC) is a bio-inspired communication method based on the exchange of molecules for information transfer among nanoscale devices. MC has been extensively studied from various aspects in the literature; however, the physical design of MC transceiving units is largely neglected with the assumption that network nodes are entirely biological devices, e.g., engineered bacteria, which are intrinsically capable of receiving and transmitting molecular messages. However, the low information processing capacity of biological devices and the challenge to interface them with macroscale networks hinder the true application potential of nanonetworks. To overcome this limitation, recently, we proposed a nanobioelectronic MC receiver architecture exploiting the nanoscale field-effect transistor-based biosensor (bioFET) technology, which provides noninvasive and sensitive molecular detection while producing electrical signals as the output. In this paper, we introduce a comprehensive model for silicon nanowire FET-based MC receivers by integrating the underlying processes in MC and bioFET to provide a unified analysis framework. We derive closed-form expressions for the noise statistics, the signal-to-noise ratio (SNR) at the receiver output, and the symbol error probability (SEP). Performance evaluation in terms of SNR and SEP reveals the effects of individual system parameters on the detection performance of the proposed MC receiver.

The traffic congestion problem is a common issue for the residents of metropolises. Although expanding the capacity of transportation systems and stimulating the public transportation may decrease the traffic congestion, they cannot completely solve the traffic congestion problem. As a solution for the worsening traffic congestion problem in urban areas, road pricing systems have been employed. In this paper, we propose a radically different road pricing scheme to prevent and decrease the traffic congestion in metropolises. Unlike designating a small congestion charge zone in a city, we propose to employ a road pricing system over the entire city. Thus, our road pricing system can control the traffic flow in the entire traffic network of the city. Furthermore, the road prices are adjusted dynamically based on the instantaneous traffic densities of each road in the city in order to rapidly and efficiently control the traffic flow and to prevent the traffic congestion. Moreover, we propose to change the road prices according to the past usage statistics of the road by predicting a possible congestion. The simulation results of our road pricing algorithm show that traffic congestion is prevented over the entire traffic network and the traffic densities of the roads are homogenized.

Nanoscale communication based on Forster Resonance Energy Transfer (FRET) is a promising paradigm that allows future molecular-size machines to communicate with each other over distances up to 10 nm using the excited state energies of fluorescent molecules. In this study, we propose a novel nanoscale communication method based on multi-step FRET using identical fluorophores as relay nodes between communicating nanomachines, and utilizing multi-exciton transmission scheme in order to improve the limited range of the communication and achievable transmission rate over the nanoscale channel. We investigate two communication scenarios: immobile nanomachines communicating through a channel in a host material with linearly located relay nodes, and mobile nanomachines communicating through a channel in a 3-dimensional aqueous environment with randomly deployed relay nodes. We simulate the communication over these channels with realistic algorithms considering the high degree of randomness intrinsic to FRET phenomenon. Using the simulation results and following a Monte Carlo approach, we evaluate the performance of the channels by means of information theoretical capacity and interference probability. We show that multi-step FRET-based communication significantly outperforms the other biologically inspired nanocommunication techniques proposed so far in terms of maximum achievable data transmission rates. The results underline the compatibility and practicality of the FRET-based communication for several applications ranging from molecular computers to nanosensor networks.

Nanoscale communication based on Förster Resonance Energy Transfer (FRET) enables nanomachines to communicate with each other using the excited state of the fluorescent molecules as the information conveyer. In this study, FRET-based nanoscale communication is further extended to realize FRET-based nanoscale broadcast communication with one transmitter and many receiver nanomachines, and the performance of the broadcast channel is analyzed information theoretically. Furthermore, an electrically controllable routing mechanism is proposed exploiting the Quantum Confined Stark Effect (QCSE) observed in quantum dots. It is shown that by appropriately selecting the employed molecules on the communicating nanomachines, it is possible to control the route of the information flow by externally applying electric field in FRET-based nanonetworks.