Bio-inspired Information and Communications Technologies

Molecular communication (MC) has become a new communication technology between nano-scale devices due to its biocompatibility and low energy consumption. Calcium signaling gradually becomes a hot research topic as a typical case of molecular communication in biological cells, but the system performance of \(\hbox {Ca}^{2+}\) signal-based molecular communication is low because the intracellular \(\hbox {Ca}^{2+}\) concentration decays with time and space. In this work, we firstly introduce a hybrid communication scheme based on electromagnetic and molecular communication to investigate the mechanism of action of cytoplasmic calcium ions induced by alternating fields. Secondly, the \(\hbox {Ca}^{2+}\) signal is analyzed in a multimodal analysis using an external electromagnetic device that emits electromagnetic waves as a control wave to drive the transmitter. In addition, we propose a network coding scheme based on \(\hbox {Ca}^{2+}\) signal frequency and a high-efficiency communication system with XOR logic gates. The proposed network coding communication system reduces the number of information exchanges and has a higher communication efficiency compared to conventional communication.

In view of the above-mentioned problems, this paper is based on STEM education and constructs teaching aids for smart farms, allowing students to practice the teaching aids developed by this paper in the field. The teaching aids of this paper are mainly based on the detection of farm and honeycomb status. Determine whether there are any abnormalities between the farm and the activity status of bees, such as: crop growth, bee reproduction, etc. Students can increase their interest in IT practical learning through teaching aid assembly and program operation. In addition, students should correct the teaching aid parameters during actual operation. Improving the recognition accuracy will further arouse students’ interest in artificial intelligence theory learning and achieve STEM education concepts. This paper will mainly use traditional artificial intelligence practical teaching methods and the innovative teaching methods proposed by this paper for learning comparison. The teaching method will be evaluated through the T test method through front and back questionnaires, teaching evaluation and student achievement scores. Help show whether the teaching method of this paper has achieved the expected goal. This article aims to cultivate more talents through Artificial Intelligence of Things (AIoT) teaching aids, analyzing data using t-tests in Statistical Product and Service Solutions (SPSS). Therefore, the experimental results prove that the STEM 4.0 approach proposed in this study can enhance students’ learning performance and willingness.

This paper implements a reinforcement learning (RL) targeting strategy for multifocal tumour lesions in the framework of computational nanobiosensing (CONA). Multi-tumours are promoted by the metastatic interaction between the surrounding tissues and the tumour suppressor. Nanorobots, regarded as computing agents, aim to search the multi-tumour lesions within the complicated vessel network. The Biological information gradient fields (BGFs) indicate the formation of the tumour microenvironment regulated by the nearby vessel network. By using reinforced learning and applying the knowledge of BGFs, this work achieves a higher tumour targeting efficiency than the previous work. The Markov and BGFs rewards are included in the total RL reward, in which the Markov reward is utilized for training nanorobots to find the path and avoid colliding with vessel walls, allowing them to learn the vascular network’s topology, whereas the knowledge of BGFs incentive benefits faster convergence of the searching process. Therefore, this method enables the discovery of the path planning for the multi-tumour in a heterogeneous vessel network by combining viable vessel path planning with BGFs information.

Farm produce is essential to feed growing world population, even as the area of land available for agriculture decreases. Farmers tend to over apply water and fertilizer to maximize crop yield, since knowledge of soil conditions is insufficient for a more targeted application. The over application of water needlessly uses a scarce resource, especially in drier climates. The over application of fertilizer wastes the fertilizer, increases greenhouse gas (GHG) emissions, and degrades downstream water as algae increases and oxygen decreases. The purpose of our project was to develop modules for soil testing and transmitting of data to the hub computer that would be accessible by the farmer. The sensors and transmitter were developed and tested to be mounted on a stake that would be implanted in the soil of the field, and results transmitted to a hub computer that would provide a dashboard of results and control for the farmer to use in making decisions. Prototype modules were developed for soil nutrients and pH. Modules were tested for monitoring moisture and wireless data transmission. Such a system would provide soil condition information that the farmer could use to more apply appropriate amounts of water and fertilizer, and not over apply.

When the tumour grows, the microvessel density in the surrounding area increases and exhibits irregular curvature, which shows a difference from the regular vascular network. Therefore, a model is needed to describe the vascular network around the tumour. However, the existing models can not provide a good representation of the vascular network. This paper proposes a Vessel Matrix Model (VMM), a visualization vascular network model which has the potential to resemble the complicated vessels networks around the tumour microenvironment. VMM is conducive to the works such as drug delivery and tumour search and can perform a tumour-targeting search by combining with the computational nanobiosensing (CONA) framework. CONA uses nanorobots as computing agents to learn the surrounding environment to regulate the path-planning to the tumour location through algorithms such as reinforcement learning. A CONA method is performed in searching for a tumour to verify the feasibility of this vascular network. In order to seek optimal routing in the vascular network, VMM provides distance reward and weight reward for the agents, where the rewards are determined by the distance of starting point to the tumour lesion and the gradient of BGF, respectively. Therefore, VMM enables the tumour search with the CONA method. By introducing different weights between the destination and weights rewards, it is found that targeting efficiency can be affected by branch rate and size of the network.

The response of heterogeneous groups of fish including a few leaders, several followers, and a few fish initiating escape reaction is investigated. This alarm response is often observed in animal groups where exposure to strong stimuli such as a predator can force a few individuals to initiate sudden and abrupt turns to move to safer locations. In this work, a coupled stochastic process is leveraged to recreate this behavior and investigate their effects on the group collective dynamics. At the vicinity of a synchronized state, for small perturbations introduced by startled fish, a closed-form expression of the polarization order parameter is determined and shown effective in predicting group alignment. A numerical analysis suggests that a variation of the frequency and the amplitude of the jumps introduced by escaping fish can result in a transition to several states including an ordered state where individual align their heading direction, a disorganized state where they move in random direction, and two other states where the group split up resulting either into a change of leadership or individuals swimming away from the startled fish and therefore recovering their initial synchronized state. The findings from this work are in line with observations on fish groups exposed to a predator where initially a completely disordered state can be observed but groups tend to progressively recover a synchronized state.

Current targeted drug delivery systems like passive targeting or active targeting are still inefficient because they mainly depend on blood circulation and extravasation. It is significant that drug delivery carriers are capable of autonomously swimming towards target site (e.g., diseased cells or tumors) and releasing drugs. In recent years, targeted drug delivery depending on autonomous swimmers such as nanorobot has been actively studied and a number of solutions have been proposed. In the paper, we propose a nanorobot-based system comprising of nanorobot behavior planning algorithm, drug reception model and adjusting method of release rate for a simulation of local targeted drug delivery. In this system, nanorobots can move and accumulate at target site by simulating bacterial chemotaxis, and determine the timing of drug release relying on quorum sensing. In addition, nanorobots can dynamically adjust the rate of drug release depending on the concentration of tumor biomarker. A simulation environment is established in order to evaluate the nanorobotic drug delivery system. The simulation results show that the nanorobotic drug delivery system can not only deliver drugs effectively at desired location but also enhance efficiency of drug utilization.

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.

From the fact that phage takes a random decision to opt either by lysis or lysogeny, this paper has carried out a Montecarlo simulation of the attack of phage against bacteria. When it is assumed random variables along the attack, then it would produce a indecision for having lysis or lysogeny that is quantitatively seen as a small probability for both scenarios. Thus a delay emerges that is favorable to bacteria in the sense that phages might to be disorganized and destabilize because their ambition. This can also be understood as a scenario of interference by which the molecular messengers emitted by phages is abundant, so that more subprocesses together to lysis and lisogeny might be manifested. It has been assumed that messengers become negatively or positively charged, yielding to attraction or repulsion among genes. Thus, the decision for lysis and lysogeny would have a random origin in conjunction to Coulomb’s forces.

Instant messaging (IM) has been widely used for many years since Internet technology developed and all-IP network architecture proposed. 5G mobile network launched worldwide a few years ago for pursuing ultra-low latency and high speed data transmission. In this paper, an IM application is developed in an open source 5G core (5GC) network framework, i.e. the free5GC. To validate the developed IM application operates properly in free5GC, we utilized the Tshark to examine and capture packets. Experimental results showed that the messages of the developed IM application passed through the free5GC network.

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.

Fractures are common injuries causing pain and morbidity. Stable fractures with acceptable initial alignment are treated by immobilization. The alignment and angulation need to be controlled during the first 1–3 weeks (depending on the fracture), because the alignment may worsen. This is performed by taking X-ray images of the fracture site. Repetitive X-rays expose the patient to ionizing radiation repetitively. We have studied techniques to monitor the alignment of the fracture continuously and without the routinely taken control X-rays. The idea is to place sensors underneath the cast and on to the skin of the patient that would follow the angulation and alignment of the fracture and alert if a change is detected. Two approaches with radio technology are made: transmitter-receiver pairs and radar pairs. The challenges and their possible solutions are discussed.

The life-sustaining function of respiration becomes impaired by diseases that occur more with old. A system that monitors the inhalations and exhalations of the respiratory cycle could raise an alert when abnormal patterns or prolonged disruptions are detected. Noninvasive methods are suitable to chronically monitor respiration. Methods include analyzing audio sounds generated during respirations and analyzing changes in the volume of the thorax or abdomen. In casual observations of eupneic breathing, inhalation often sounds different from exhalation, though may be quite similar. One of the challenges for signal processing is to distinguish inhalation from exhalation based on only the audio. The purpose of this study was to find a method of analyzing the audio frequency content that could differentiate the inhalation and exhalation. Volunteer subjects were recruited to record audio during eupneic respiration for analysis. To classify the timing of each inhalation and exhalation, both respiratory sounds and volume changes of the thorax were simultaneously recorded. The audio files were analyzed by Fast Fourier Transform (FFT) to determine the frequency content. Features of the frequency power spectrum were found that appear promising for distinguishing inhalation and exhalation. Such differences could be used to characterize audio respiratory signals and improve the monitoring of individuals at risk for impaired respiratory function.

Electronic Health Records offer real advantages for accessing and storing patient health information, which can improve the management of patient care. However, the attractive features of electronic records (accessibility, portability, and portability of patient health information) also present privacy risks. Organizations need to share person-specific health data without disclosing the privacy of their subjects.

Current mechanisms for effective management and protection of health records in Senegal have proven insufficient. In this paper, we propose a system that addresses the issue of sharing health data between hospitals in a trustless environment based on the Consortium Blockchain to improve the quality of care and the efficiency of the health system in Senegal. After a brief introduction, we present some characteristics of Blockchain as well as the different types and securing of Blockchain using cryptographic algorithms. Then an overview of related work is conducted. Finally, we presented the preliminaries of sharing health records with the Blockchain in Senegal. Our work ends with the description of the functioning of our medical record management mechanism with the Blockchain in Senegal and its implementation.

Human tissue mimicking phantoms allow development of realistic emulations platforms which are essential for design of several biomedical monitoring and diagnosis systems. This first aim of this paper is to present a novel and durable fat tissue phantom for lower microwave frequency ranges 2.5–10 GHz. The phantom is developed from the liquid propylene glycol (pure) which we found to have similar dielectric properties as the fat tissue and hence, it is suitable to be used as liquid fat phantom. Development steps of solid fat phantoms with different trials are presented to provide insight how each ingredient affect on the dielelctric properties of the mixture. Additionally, phantom’s stability over time in terms of dielectric and physical properties are evaluated. The second main aim of this paper is to present a novel approach to verify the feasibility and reliability of phantoms in practical scenarios with tissue layer model simulations. In the simulations, the antenna reflection coefficients are calculated with tissue layer models in which the dielectric properties of the fat tissue layer is varied between the proposed prolyne glycol -based fat phantoms as well as real human fat tissue values. Our goal is to show how small differences in the dielectric properties of the phantoms affect on a practical scenario which is based on antenna impedance measurements. The dielectric properties of the proposed fat phantom have very good correspondence with real fat tissue especially in the range of 5 GHz-10 GHz. Also, at lower ultrawide band (3.1–5 GHz), the difference in dielectric properties is minor. The layer model simulations show that the differences in dielectric properties do not have significant effect when modelling the practical scenarios in the frequency ranges targeted for medical applications. Hence the proposed liquid and solid fat phantoms are suitable to be used in the emulation platforms of biomedical applications.

Inter-symbol interference (ISI) decreases the performance of diffusion based molecular communication (MC) significantly. Especially, considering the molecular degradation during the propagation, the ISI mitigation becomes more tricky as the received molecules vary greatly. To tackle this problem, in this paper, we propose an optimal detection method based on maximum likelihood detection by minimizing the error probability. To characterize the proposed detection method, the optimal detection threshold and bit error rate (BER) is derived. Simulation results verified the effectiveness of the proposed ISI mitigation method in the considered MC system with molecular degradation.

For a long time, people have carried out various studies on molecular communication and nano information network in order to realize biomedical applications inside human body. However, how to realize the communication between these applications and the outside body has become a new problem. In general, different components in the blood have different absorption rates of the different light. Based on this, we propose a new through-body communication method. The nanomachine in the blood vessel transmits signal by releasing certain substances which can influence blood oxygen saturation. The change of blood oxygen saturation can be detected by a outside body device measuring the attenuation of different light through blood. The framework of the entire communication system is proposed and mathematically modeled. Its error performance is discussed and evaluated. This research will contribute to the realization of the connection of communication systems inside and outside the human body.

Molecular communication (MC) is a significant technology in the field of nanobiology, which uses molecules as message carriers to transmit information. Diffusion channel model is the most commonly channel model base on Brownian motion in molecular communication. In single-input single-output (SISO) molecular communication model, inter-symbol interference (ISI) exists due to the long tail effect. In this study, inspired by the D-MoSK modulation scheme, where different types molecules used for encoding, A new simple modulation is proposed, which can not only reduce the ISI interference effectively, but also improve the transmission rate to a certain extent. Numerical results show that compared with the current modulation scheme, the proposed scheme makes the system achieve better BER performance and the transmission rate is also improved.

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.

People with visual impairment have increased difficulty in performing activities of daily living, such as walking without bumping into obstacles. Many assistive technologies are used to help with ambulation as one walks forward, such as a white walking cane or a service dog. These have proven to be of tremendous help, but the cane may miss suspended objects not touching the ground, and service dogs are not available to all who need them. Further assistive technologies continue to be developed and tested. In nature, those without visual acuity tend to obtain much information from their environment through the other senses, such as hearing or tactile touch. This study is exploring the mapping of obstacle detection to tactile vibration motors on the skin. Ultrasonic sensors were used to detect obstacles in the forward direction where the user would be walking, and calculate the distance. The distance was mapped to a vibration pattern, with the pattern being more intense for closer obstacles. A prototype was developed and had several tests run. Obstacle detection and distance were useful up to 3 m. The functional field of view was 10° to 30° from centerline, but became more narrow as the distance increased and for harder to detect obstacles. The distance was mapped to 3 different vibration patterns, and human subjects were able to distinguish the patterns in a consistent manner. The prototype shows promise, but more testing and development would be required toward widespread application.

In recent years the rapid technological development in printable electronics technology has made it possible to develop sensors and circuits for wearable medical devices using flexible substrate. In this paper a novel implementation of flexible capacitive sensors to detect and quantify human excreta inside adult diaper is explored. The flexible capacitive sensors are implemented in co-planar geometry by using conductive strips, paint and conductive fabric which are easily available in the market. The developed sensors are used to detect and quantify the fluid and concentration of electrolytes in-vitro (adult diaper and glass jar). An impedance analyzer is used to perform measurements from the sensor and collect necessary specifications in developing suitable printable sensors for a diaper.

The Internet of Things (IoT) has emerged as a subject of intense interest among the research and industrial community as it has significantly impacted human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart healthcare, smart devices, smart city, and smart grid. The security of IoT devices has become a serious concern, especially in the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. In addition, in IoT networks where the connected devices are vulnerable to attacks such as attacks that affect the resource constraints of healthcare devices, e.g., energy consumption attacks. Therefore, this paper defines the impact of Distributed Denial of Service (DDoS) and Fake Access Points (F-APs) attacks on WiFi smart healthcare devices and investigates in detail how these attacks can be deployed toward victim devices and Access Points (APs). Our work focuses on IoT devices’ connectivity and energy consumption when under attack. The main key findings of this paper are as follows: (i) the minimum and maximum attack rate of DDoS attacks that cause service disruptions on the victim side, and (ii) the minimum-the higher effect of energy-consumption Distributed Denial of Service (EC-DDoS) and F-APs attacks on the energy consumption of the smart healthcare devices. Our study reveals the communication protocols, attack rates, payload sizes, and victim devices’ ports state as the vital factors in determining the energy consumption of victim devices. These findings facilitate a thorough understanding of IoT devices’ potential vulnerabilities within a smart healthcare environment and pave solid foundations for future studies on defense solutions.

The objective of a Flexible Manufacturing System (FMS) is to respond faster to changes in products and demands with minimum changeover cost. However, layout changes in FMS are not automatic and required human intervention. Therefore, when requirements for layout changes are frequent, such as in a dynamic production environment, like mass personalisation production environments, layout reconfiguration becomes expensive and unrealistic. In this paper, we relax this core assumption of static FMS layout and introduce a decentralised approach to the design and coordination of manufacturing systems’ entities, whereby both products and production machines are mobile and autonomous. We apply three different optimisation methods, of which two are ensembles of computational and heuristics optimisation approaches based on Gradient Descent and Ant Colony Optimisation (ACO), to optimise mobile machines locations under non-deterministic manufacturing conditions as obtainable in a mass personalisation context. These approaches enable mobile production machines to coordinate and autonomously adjust their location and layout in real-time to minimise the cost of material flow between production machines. The proposed approach offers a promising outlook on the design and coordination of manufacturing systems under unpredictable manufacturing conditions.

This paper proposes a nanorobots fish swarm algorithm (NFSA) for tumor targeting. The alterations in the tumor microenvironment caused by tumor growth produce the biological gradient field (BGF), which is regulated by the adjacent tortuous and dense capillary network. NFSA is used to measure tumor-targeting efficiency in comparison to the benchmarks of Brute-force and the conventional gradient descent algorithm. Our goal is to increase the efficiency of targeting tumors in the early stages by using existing swarm intelligence algorithms to manipulate nanorobot swarms (NS) through magnetic fields. The extracorporeal observation system sensed the motion of NS under the influence of a BGF and then estimated the gradient of BGF. The invasive percolation algorithm models the vascular network to evaluate the performance of searching strategies. We also apply the exponential evolution step mechanism to boost the tumor-targeting efficiency of NFSA. The results show that NFSA has higher overall tumor targeting efficiency and a fast convergence property than previous algorithms. We hope that the NS in a multi-agent system could pave the way for challenges in tumor targeting.