In diffusive molecular communications (DMC), the relay serves to address the challenge in long-distance DMC scenarios. Currently, the relay used in DMC mainly includes the amplify-and-forward relay and decode-and-forward relay. To facilitate information transmission, both of these relays are required to actively release information molecules, while consuming the energy stored by themselves and introducing a transmission delay. In this paper, a passive relay is proposed to support long-distance communications without excess energy consumption. Specifically, we consider a multi-user communication system with both near and far users, where it is assumed that specific types of molecules serve designated users and are reflected by other users during propagation. Due to this molecule reflection, the near user can function as a passive relay, aiding in long-distance transmission for the far user. Besides, two detectors-namely, the ideal and actual maximum likelihood detectors-are designed for the proposed system, and their detection performance is analyzed respectively. Moreover, energy consumption is studied for the proposed scheme. Numerical results indicate that the bit error rate (BER) performance can be greatly improved at a low synthesis cost.
Molecular communication (MC) is an emerging paradigm for exchanging information via chemical signals. It holds great promise for building nanoscale networks within biological organisms. However, the presence of counting noise originating from the molecular diffusion mechanism severely limits the signal detection performance. Currently, research on reducing such signal-dependent noise (SDN) in MC via diffusion (MCvD) remains scarcely explored. Besides, the lack of channel models in MCvD hinders the effects of model-based denoising approaches. Against this background, we resort to the data-driven machine learning (ML) methods that fit MCvD systems for noise mitigation. Additionally, the simulated numerical results show that such an ML-based scheme outperforms its classical model-based counterparts.
Molecular communication (MC) has offered a method to establish connectivity between micro/nanodevices and construct multiscale heteronetworks within the body. It is beneficial for achieving the ubiquitous connectivity and precise coverage of 6th Generation (6G) networks, while also expanding the service boundaries of traditional communication networks. In this perspective article, we have summarized the existing technological challenges for MC-based heteronetworking in terms of end-to-end trials, which includes MC transmission theories, development of highly integrated microtransceivers, and interoperability between MC systems and conventional cellular systems. Furthermore, some practical and compromised approaches on MC-based heteronetworks have also been given.
This paper introduces a novel bio-inspired wideband antenna, meticulously designed for medical microwave imaging applications, spanning a compact dimension of 0.3 × 0.26 λ₀ at 3 GHz, this flexible antenna is engineered to operate over an extensive frequency range from 2.6 GHz to 8 GHz. Drawing inspiration from natural structures, the antenna design incorporates bio-mimetic principles to optimize its performance for use in the demanding field of medical imaging, where precision and reliability are paramount. The proposed antenna achieves a peak gain of 1.6 dBi, ensuring good-quality imaging by facilitating deep tissue penetration and enhanced resolution capabilities. This work not only demonstrates the feasibility of integrating bio-inspired designs, but also highlights the significant potential of such antennas in improving the efficacy of medical diagnostic tools. Through rigorous simulation and testing, the antenna exhibits exceptional performance metrics, making it a promising solution for next-generation medical imaging technologies. This research paves the way for future advancements in antenna design, leveraging the untapped potential of bio-inspired concepts to meet the critical needs of medical imaging applications.
In the scenario of molecular communication, targeted drug delivery is usually used for the treatment of tumor cells because of its low consumption point with few side effects. Therefore, drug-carrying nanomachines are important for targeted delivery to tumor cells. In this paper, a method optimizing the location of relay nodes on the basis of the relaying method is proposed, which results in a shorter transportation path. There are two types of nanomachines, relay nanomachines aim to form relay nodes in a straight line from the base to the destination and delivery nanomachines to carry drugs. This method first forms an optimal region and then determines the optimal position. Parameters such as the number of nanomachines and the number of attractants in the system were then analyzed and discussed.
This paper introduces an approach that combines molecular communication (MC) concepts with waveform modulation techniques to regulate drug concentrations at targeted lesion sites, aiming to enhance therapeutic outcomes and minimize side effects. We focus on keeping drug levels within the therapeutic window by grounding our method in MC theory. Our study is primarily the analytical process of selecting the most effective pathways in vascular networks, considering factors such as blood vessel characteristics and the frequency of their branching. This investigation is critical for a deeper understanding of the complexities of the dynamic nature of pathway selection in vascular networks and for seeking to refine drug administration based on these insights. Our research pinpoints the optimal timing for drug administration using continuous-release formulations, ensuring consistent locoregional drug concentrations. The simulations validate our approach, indicating its potential to maintain stable drug levels, thereby underscoring the importance of adapting to variations in the intravascular delivery pathway. Our method, which merges MC principles with waveform modulation, contributes a nuanced perspective on enhancing the precision of drug delivery, supporting the development of an effective and personalized treatment strategy.
Transmission of information from within the body to the external environment has become a popular issue in making the Internet of Nanothings (IoNTs) a reality. Neural communication has been proposed as a promising solution, utilizing action potentials (APs) as the fundamental units for information transmission. Most of the existing research simplifies the membrane potential into two states: an excited state that generates an AP upon stimulation and a resting state absent of stimulation. This assumption neglects both the intrinsic oscillation of membrane potential and the uncertainty it introduces into information transmission. Therefore, neural communication requires further investigation into the biological similarity of channel models and the reliability of transmission. In this paper, we employ the Izhikevich model as the channel model to characterize membrane potential oscillations. Additionally, we devise an enhanced code division multiplexing (CDM) scheme based on this model, enabling multiple signals to share a single neuron channel. In contrast to previous CDM methods, this scheme employs further encoding of superimposed signals. The performance is evaluated in terms of bit error rate (BER), and the results indicate a significant improvement in interference resistance. This research improves the communication efficiency of engineered neural systems and achieves more precise and reliable communication.
Early detection of tumors remains a critical challenge in oncology, necessitating the development of innovative and efficient detection techniques. This study proposes a novel approach for tumor targeting employing a swarm of nanorobots (NS) and their light-driven aggregation capabilities. By exploiting the interaction behavior of NS aggregation under light exposure, we aimed to improve tumor detection capabilities. The effectiveness of this approach was assessed through NS dispersion and search efficiency under various light conditions, including both periodic and aperiodic sources. The complex tumor microenvironment, characterized by a dense capillary network, leads to the formation of biological gradient fields (BGFs) within Manhattan-geometry vasculature (MGV). This research thoroughly investigates the behavior of NS within these specific environments, mimicking the navigational constraints imposed by MGV. Our findings validate the feasibility of utilizing light-driven NS aggregation for precise and efficient tumor targeting. The results establish a foundation for innovative tumor detection methodologies, emphasizing the significant potential of NS in clinical applications, especially within navigating MGV and responding to BGFs.
To address the challenges of image encryption and decryption in the field of information security, this paper proposes a multi-channel fusion image encryption and decryption algorithm based on DNA sequences. Initially, a random key is generated using the Chen chaotic system. This key is then combined with eight DNA mapping methods that comply with the Watson-Crick rule to encode the original image data and a disordered matrix, resulting in corresponding base sequences. Boolean operations are performed on these base sequences, and the results are decoded using the DNA mapping methods to obtain the encrypted image. Finally, the encrypted image undergoes analysis for information entropy and gray value correlation. Simulation results demonstrate that transforming the three-dimensional matrix of a color image into a two-dimensional matrix for data processing significantly enhances encryption performance.
Molecular communication (MC) utilizes the release, diffusion and reception of molecules to transmit information. It has promising prospects in the field of drug delivery. The detection time estimation of the receiver in MC system plays important roles in the resource consumption at the receiver. Existing strategies of traditional detection time mainly focus on known channel state information (CSI). In this paper, we propose a method for estimating the detection time of the receiver in MC system with unknown CSI by using deep neural network (DNN) model. We employ the Monte Carlo simulation to capture the positions of molecules in the three-dimensional environment. The dataset is generated based on the coordinates of the molecules at each position. The numerical results show that the detection time can be accurately estimated by the DNN model which exhibits good detection and generalization abilities. In addition, the number of molecules released by the transmitter and the minimum distance between the transmitter and the boundary of the receiver have impacts on the accuracy of detection time estimation of the receiver.
The availability of molecular resources is of paramount importance in molecular communication (MC), where information is intricately encoded within the properties of molecules. In MC, the acquisition of molecules from the environmental mixture is followed by the essential process of purifying these molecules through controlled transfers between reservoirs, especially for molecular shift keying (MoSK). This paper focuses on a transmitter featuring dual reservoirs designated for the storage of information molecules, with the transference of a specific molecular species from one reservoir to another facilitated by the consumption of free energy. Given the constrained energy resources within the transmitter, we investigate an energy-efficient mechanism for transmitter creation, aimed at optimizing overall transmitter performance. Theoretical analyses are systematically conducted to explore diverse strategies for the movement of molecules between reservoirs. Ultimately, numerical results substantiate the efficacy of the proposed molecular movement strategy in the transmitter creation.
We recently introduced a new and innovative framework named "in vivo computation" by modeling the tumor targeting problem as a natural computation problem. Nanorobots play the role of computing agents are guided by the information of biological gradient field (BGF) induced by the emerging of tumor for the searching of tumor location which is the optimal solution for the in vivo computational problem. To overcome the in vivo constraints encountered in previous research, which primarily concentrated on tumor detection, several computational strategies have been suggested for achieving tumor targeting. This work concentrates on the exploration of tumor boundary with nanorobots, which is a novel and valuable research point. To overcome this challenge, we resort to the spontaneous motion of nanorobots in liquid environment, where the local hydrodynamic flows are used to actuate the nanorobots to keep a balance to the effect of BGF. In order to showcase the efficacy of the proposed methods, we conduct in silico experiments across three BGF landscapes that vary in terms of their optimization complexity.
This paper presents a focused analysis of enhancing the Internet of Bio-Nano Things (IoBNT) network performance by addressing congestion and improving throughput. We introduce a propagation model for a high-throughput DNA-based track-hopper channel to study congestion using molecular hopper dynamics and Markov state transitions alongside a novel approach for throughput measurement. Our research aims at reducing congestion by increasing information density, allowing for more efficient data transmission with fewer packets. Initially, attempts to amplify density encountered challenges related to bio-compatibility and increased decoding errors. Moreover, congestion precipitated the formation of problematic DNA structures such as hairpins during sequencing. We adopted the yin-yang coding (YYC) to overcome these hurdles, encoding two binary bits into one nucleotide for sequences compatible with synthesis and sequencing technologies. Simulation results validate the YYC coding mechanism's effectiveness and propagation models' robustness, significantly advancing IoBNT network performance optimization.
With the increasing number of diabetes patients, the developments of continuous glucose monitoring (CGM) techniques and glucose prediction models become increasingly important. In this study, we propose a personalized blood glucose prediction approach via glycemic oscillation decomposition based on CGM data. We first utilize advanced data analytics techniques to decompose CGM data into multiple patterns through the oscillation pattern mining module. The temporal pattern learning module is then developed to capture the temporal dependency of glucose levels. We further aggregate multiple outputs into glucose predictions with improved accuracy. We conduct a comparison study between the proposed approach and other existing models using OhioT1DM dataset. Experimental results show that the proposed work can provide more accurate predictions for diabetic glucose levels compared to other methods. With improved prediction performance, the proposed approach facilitates personalized blood glucose management services for diabetes patients.
Molecular communication is a novel method of communication that utilises nano-engineering and bio-engineering to enable temporary communication in harsh environments. As nano-technology and bio-engineering continue to advance, the feasibility of molecular communication is constantly improving. This paper examines the feasibility of using magnetic nanomaterials as information carriers for medium-distance transmission driven by magnetic field force, based on the existing molecular communication theory of diffuse channel. The study compares the two situations with and without relay. After conducting derivation and simulation verification, it can be concluded that the transmission rate and bit error rate data results are excellent when under the influence of magnetic field force drive. Additionally, the molecular communication system with relay is significantly more effective than the system without relay.
This study leverages Steady-State Visual Evoked Potentials (SSVEP) and Brain-Computer Interface (BCI) technology to classify responses to different visual stimuli among various subjects. Real-time collected SSVEP data was utilized, and based on a sliding window data collection method, a new classification optimization method based on a weighted voting mechanism was designed and proposed. By comparing the accuracy and Information Transfer Rate (ITR) between the traditional and new methods, the new method significantly improved performance metrics, with accuracy increasing from an average of 77.2
Molecular communication via diffusion (MCvD), in which molecules are used to transmit information by the movement of diffusion, is one of the most prominent systems in nanonetworks. In particular, the research on end-to-end mobile MCvD system is even more challenging. In this paper, we investigate the error probability of three dimensional (3D) multi-hop mobile MCvD system by proposing two relay schemes including multi-molecule-type (MMT) and single-molecule-type (SMT). Under MMT, the mathematical expression of optimal detection threshold can be derived. Especially under SMT, we propose the adaptive detection threshold method to alleviate the self-interference caused by the information molecules with the same type. Based on the two relay schemes, the mathematical expressions of error probability of this system are derived. Numerical results show the impacts of different parameters on the error probability performance.
The theorem of Bayes is applied in a straightforward manner to investigate if Covid-19 and Monkeypox 2022 can coexist together. According to realistic scenarios and global data it was verified that Covid-19 is a kind of main pandemic whereas Monkeypox can be accepted a mini pandemic with a low lethality and a short period of existence. This would suggest that two global pandemics might not coexist at same time from the fact that people would acquire a disease belonging to all those pandemics with a strong capabilities of geographical translation and stability at long periods. From simulations, it is seen that Covid-19 would remain against Monkeypox that exhibits a noteworthy capability to produce infections but a weak lethality.
In this paper, a neuro-spike synaptic cooperative communication channel model is exploited. In the considered model, a neuro-spike relay (NSR) is placed in the synaptic gap of two neurons to extend the range of communication. For the analysis, a time-slotted channel is exploited, where we transmit a binary bit in each time-slot for the transmission of information from the pre-synaptic neuron called neuro-spike source (NSS) to the post-synaptic neuron called neuro-spike destination (NSD). Further, the considered model is analyzed in terms of the probability of detection and probability of false alarm. Moreover, the effect of ISI due to the transmission of molecules from the previous time-slots, and noise arises from unintended neurons are also considered in the analysis. Furthermore, the closed-form expression for the end-to-end probability of error is also computed for the cooperative link. Above all, the analytical expressions are validated using Monte-Carlo simulations.
Most existing food delivery platforms lack responsibility when it comes to route planning. This often results in uneven assignment of orders or difficulty in arranging orders for delivery drivers. These issues have led to loss of consumer rights and reduced revenue for delivery platforms, as well as negative feedback and evaluations. To address this problem, it is necessary to first resolve the issue of uneven distribution of orders. In this paper, we propose using the Genetic Algorithm (GA) to solve the order assignment optimization problem. By utilizing GA’s strong global search ability, we can achieve fair assignment of orders, optimize delivery routes, and balance revenue distribution. This approach creates a fair competition environment for delivery drivers and improves service quality, ultimately leading to positive feedback from consumers and creating a win-win situation.
