COTiR: Molecular Communication Model for Synthetic Exosome-Based Tissue Regeneration.

Mesenchymal stem cell (MSC)-derived exosomes are recognized as an unparalleled therapy for tissue damage rendered by COVID-19 infection and subsequent hyper-inflammatory immune response. However, the natural targeting mechanism of exosomes is challenging to detect the damaged tissue over long diffusion distances efficiently. The coordinated movement of exosomes is desired for successful identification of target sites. In this work, we propose a molecular communication model, CoTiR, with a bio-inspired directional migration strategy (DMS) for guided propagation of exosomes to target the damaged tissues. The model includes directional propagation, reception, and regeneration of tissue. The proposed model has the potential to be used in designing efficient communication systems in the nanodomain. We compare the proposed model to the basic random propagation model and show the efficacy of our model regarding the detection of multiple targets and the detection time required. Simulation results indicate that the proposed model requires a shorter period of time for a similar number of exosomes to detect the targets compared to the basic random propagation model. Furthermore, the results reveal a 99.96% decrease in the collagen concentration in the absence of inflammatory cytokine molecules compared to the collagen concentration in the presence of inflammatory cytokine molecules.

Estimating Time Window to Administer Anti-Cytokine Therapeutic Drugs to COVID-19 Patients.

The severe COVID-19 infection often leads to "Cytokine Release Syndrome (CRS)", which is a serious adverse medical condition causing multiple organ failures. Anti-cytokine therapy has shown promising results for the treatment of the CRS. As part of the anti-cytokine therapy, the immuno-suppressants or anti-inflammatory drugs are infused to block the release of cytokine molecules. However, determining the time window to infuse the required dose of drugs is challenging due to the complex processes involving the release of inflammatory markers, such as IL-6 and C-reactive protein (CRP) molecules. In this work, we develop a molecular communication channel to model the transmission, propagation, and reception of cytokine molecules. The proposed analytical model can be used as a framework to estimate the time window to administer anti-cytokine drugs to get successful outcomes. Simulation results show that at a 50 s-1 release rate of IL-6 molecules, the cytokine storm is triggered at ~ 10 hours, and consequently, the CRP molecules reach the severe level of 97 mg/L at ~ 20 hours. Further, the results reveal that with one-half of the release rate of IL-6 molecules, the time to observe the severe level of 97 mg/L CRP molecules increases by 50%.

i-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals.

In this article, we propose a smart bedsheet-i-Sheet-for remotely monitoring the health of COVID-19 patients. Typically, real-time health monitoring is very crucial for COVID-19 patients to prevent their health from deteriorating. Conventional healthcare monitoring systems are manual and require patient input to start monitoring health. However, it is difficult for the patients to give input in critical conditions as well as at night. For instance, if the oxygen saturation level decreases during sleep, then it is difficult to monitor. Furthermore, there is a need for a system that monitors post-COVID effects as various vitals get affected, and there are chances of their failure even after the recovery. i-Sheet exploits these features and provides the health monitoring of COVID-19 patients based on their pressure on the bedsheet. It works in three phases: 1) sensing the pressure exerted by the patient on the bedsheet; 2) categorizing the data into groups (comfortable and uncomfortable) based on the fluctuations in the data; and 3) alerting the caregiver about the condition of the patient. Experimental results demonstrate the effectiveness of i-Sheet in monitoring the health of the patient. i-Sheet effectively categorizes the condition of the patient with an accuracy of 99.3% and utilizes 17.5 W of the power. Furthermore, the delay involved in monitoring the health of patients using i-Sheet is 2 s which is very diminutive and is acceptable.

SemBox: Semantic Interoperability in a Box for Wearable e-Health Devices.

In this work, we propose SemBox - Semantic interoperability in a Box, to enable wireless on-the-go communication between heterogeneous wearable health monitoring devices. It can connect wirelessly to the health monitoring devices and receive their data packets. It uses a Mamdani-based fuzzy inference system with data pre-processing to classify the received data packet into one of the classes of the vital parameters. It enables semantic interoperability by labelling and annotating the data packets based on the extracted packet information. We implement SemBox using three different health monitoring wearables, with different keywords used for each vital parameter representation in the data packet. SemBox shows a maximum classification accuracy of 85.71%, with a maximum PDR of 1 at the SemBox with varying device parameters. Overall, SemBox is a potential plug-and-play solution to achieve semantic interoperability and collaboration between heterogeneous health monitoring wearable devices, irrespective of their commercial and proprietary specifications. It is customizable for applications that use multiple heterogeneous devices for collaborative monitoring and decision support. SemBox enables interoperability among health monitoring devices, introduces flexibility and ease the inter-device dynamics in the domain of biomedical research.

Persistent Service Provisioning Framework for IoMT Based Emergency Mobile Healthcare Units.

The resource constrained nature of IoT devices set about task offloading over the Internet for robust processing. However, this increases the Turnaround Time (TAT) of the IoT services. High TATs may cause catastrophe in time-sensitive environments such as chemical and steel industries, vehicular networks, healthcare, and others. Moreover, the unreliable Internet in rural parts of underdeveloped and developing countries is unsuitable for time-critical IoT systems. In this work, we propose a framework for continuous delivery of IoT services to address the issue of high latency/TAT with poor/no-internet coverage. The proposed framework guarantees service delivery in such areas. To demonstrate the proposed framework, we implemented an IoT-based mobile patient monitoring system. It predicts the patient's criticality using actual sensor data. When the sensed parameters exceed the pre-set threshold in the rule-base, it initiates data transfer to the fog or cloud server. If fog or the cloud is unreachable, it performs onboard predictions. Thus, the framework ensures essential service delivery to the user at all times. Our test-bed-based evaluation demonstrates edge CPU and RAM load reduction of 16% and 26%, respectively, in the ML model's test phase. Also, the results confirm continuous service delivery, reduced latency, power and computing resource consumption.

VIVID: In Vivo End-to-End Molecular Communication Model for COVID-19.

As an alternative to ongoing efforts for vaccine development, scientists are exploring novel approaches to provide innovative therapeutics, such as nanoparticle- and stem cell-based treatments. Thus, understanding the transmission and propagation dynamics of coronavirus inside the respiratory system has attracted researchers' attention. In this work, we model the transmission and propagation of coronavirus inside the respiratory tract, starting from the nasal area to alveoli using molecular communication theory. We performed experiments using COMSOL, a finite-element multiphysics simulation software, and Python-based simulations to analyze the end-to-end communication model in terms of path loss, delay, and gain. The analytical results show the correlation between the channel characteristics and pathophysiological properties of coronavirus. For the initial 50% of the maximum production rate of virus particles, the path loss increases more than 16 times than the remaining 50%. The delayed response of the immune system and increase in the absorption of virus particles inside the respiratory tract delay the arrival of virus particles at the alveoli. Furthermore, the results reveal that the virus load is more in case of asthmatic patients as compared to the normal subjects.

Long-Term Alleviation of Parkinsonian Resting Tremor Using Wireless Optogenetic Nanonetworks.

Resting tremor is one of the major symptoms of Parkinson's disease, which causes havoc in motor functions of the body, has its genesis in communication impairments in the subthalamic nucleus of the basal ganglia. The modern sophisticated surgical treatments, including electrical deep brain stimulation do not yield satisfactory results due to their inability to provide long-term cure and minimize side effects, such as discomfort and increased infection rates. In this work, we propose a novel system based on the emerging communication technology of wireless optogenetic networks of neural dusts to provide a long-term solution for the alleviation of resting tremor. Interfaced with neural dusts, each of the subthalamic nucleus neurons can be controlled and stimulated by the ultrasonic waves which are transmitted from a single/multiple subdural transducer(s) that are placed in the dura mater of the brain. Moreover, in order to address the challenging tasks of charging and addressing each of the neural dusts, we propose a protocol, named as Single Time Instant addressing Protocol, which outperforms the state-of-the-art parallel charging protocol. The basic idea of our protocol is that it selects most frequently occurring spike patterns in a single time instant and assigns the pattern with an ultrasonic frequency. With the improved efficiency of Single Time Instant addressing Protocol validated with empirical datasets, the proposed system is expected to revolutionize the way of treatment of parkinsonian resting tremor.

NetADD: Network Flow-Based Distributed Topology Control on Addressing Asymmetric Data Delivery in Nanonetworks.

Architecting nanonetwork-based coronary heart disease monitoring requires a set of nanodevice-embedded drug-eluting stents (nanoDESs) inserted inside the affected sites of coronary arteries of the heart to cooperatively collect medical information therein and transmit the information via the nano-macro (NM) interface, which is inserted into the intercostal space of the rib cage. These nanonetworks, which operate in the terahertz band (0.1-10 THz), face increased complexity in delivering the data of underlying nanonetworks to the NM, due to the limited energy content of nanoDESs. In this paper, we propose a distributed topology control algorithm based on the solution of the well-known network flow problem for addressing asymmetric data delivery. The generated topology is dynamic in the sense that it changes according to the energy levels of the nanoDESs. The proposed algorithm helps establish the topology and balance the load on nanoDESs. The proposed approach changes the topology if there arises a need to balance the energy content of the nanoDESs. We study the problem of asymmetric data delivery in various types of network topologies as well. The proposed solution is shown via extensive simulation to yield improved performance over the existing topology control solutions with respect to data delivery ratio, energy consumption, delay, and the events of shutdown.

Game Theoretic Analysis of Cooperative Message Forwarding in Opportunistic Mobile Networks.

In cooperative communication, a set of players forming a coalition ensures communal behavior among themselves by helping one another in message forwarding. Opportunistic mobile networks (OMNs) require multihop communications for transferring messages from the source to the destination nodes. However, noncooperative nodes only forward their own messages to others, and drop others' messages upon receiving them. So, the message delivery overhead increases in OMN. For minimizing the overhead and maximizing the delivery rate, we propose two coalition-based cooperative schemes: 1) simple coalition formation (SCF) and 2) overlapping coalition formation (OCF) game. In SCF, we consider the presence of a central information center, whereas OCF is a fully distributed scheme. In SCF, coalitions are disjoint, whereas in OCF, a node may be the member of multiple coalitions at the same time. All nodes in a coalition help each other cooperatively by forwarding group messages to the intermediate or destination nodes. The goal of the nodes is to achieve high success rate in delivering messages. The proposed SCF scheme is cohesive, in which disjoint coalitions always combine to form grand coalition. In OCF, a node reaches a stable grand coalition when all the nodes of the OMN are members of overlapping coalition of the node. No node gains by deviating from the grand coalition in SCF and OCF. Simulation results based on synthetic mobility model and real-life traces show that the message delivery ratio of OMNs increase by up to 67%, as compared to the noncooperative scenario. Moreover, the message overhead ratio using the proposed coalition-based schemes reduces by up to about (1/3)rd of that of the noncooperative communication scheme.

"Catch the Pendulum": The Problem of Asymmetric Data Delivery in Electromagnetic Nanonetworks.

The network of novel nano-material based nanodevices, known as nanoscale communication networks or nanonetworks has ushered a new communication paradigm in the terahertz band (0.1-10 THz). In this work, first we envisage an architecture of nanonetworks-based Coronary Heart Disease (CHD) monitoring, consisting of nano-macro interface (NM) and nanodevice-embedded Drug Eluting Stents (DESs), termed as nanoDESs. Next, we study the problem of asymmetric data delivery in such nanonetworks-based systems and propose a simple distance-aware power allocation algorithm, named catch-the-pendulum, which optimizes the energy consumption of nanoDESs for communicating data from the underlying nanonetworks to radio frequency (RF) based macro-scale communication networks. The algorithm exploits the periodic change in mean distance between a nanoDES, inserted inside the affected coronary artery, and the NM, fitted in the intercostal space of the rib cage of a patient suffering from a CHD. Extensive simulations confirm superior performance of the proposed algorithm with respect to energy consumption, packet delivery, and shutdown phase.

From Micro to Nano: The Evolution of Wireless Sensor-Based Health Care.

Over the past decade, embedded systems and microelectromechanical systems have evolved in a radical way, redefining our standard of living and enhancing the quality of life. Health care, among various other fields, has benefited vastly from this technological development. The concept of using sensors for health care purposes originated in the late 1980s when sensors were developed to measure certain physiological parameters associated with the human body. In traditional sensor nodes, the signal sources are mostly different environmental phenomena (such as temperature, vibration, and luminosity) or man-made events (such as intrusion and mobile target tracking), whereas in case of the physiological sensors, the signal source is living human tissue. These sensor nodes, as their primary sensing element, have a diaphragm that converts pressure into displacement. This displacement, in turn, is subsequently transformed into an electrical signal. The concept of wireless physiological sensor nodes, however, gained popularity in the mid-2000s, with the sensed data from the nodes transmitted to the hub via a wireless medium. The network formed by this heterogeneous set of wireless body sensor nodes is termed a wireless body-area network (WBAN). Each WBAN is essentially a composition of multiple wireless body sensor nodes and a single hub. The hub is primarily responsible for acquisition of the raw sensed data from all the component sensor nodes and first-level aggregation of the data before transmitting the aggregated data for further analysis to a remote data acquisition center. Here, we outline the evolution of WBANs in the context of modern health care and its convergence with nanotechnology.

MIRACLE: Mobility Prediction Inside a Coverage Hole Using Stochastic Learning Weak Estimator.

In target tracking applications of wireless sensor networks (WSNs), one of the important but overlooked issues is the estimation of mobility behavior of a target inside a coverage hole. The existing approaches are restricted to networks with effective coverage by wireless sensors. Additionally, those works implicitly considered that a target does not change its mobility pattern inside the entire tracking region. In this paper, we address the above lacunae by designing a stochastic learning weak estimation-based scheme, namely mobility prediction inside a coverage hole (MIRACLE). The objectives of MIRACLE are two fold. First, one should be able to correctly predict the mobility pattern of a target inside a coverage hole with low computational overhead. Second, if a target changes its mobility pattern inside the coverage hole, the proposed estimator should give some estimation about all possible transitions among the mobility models. We use the trajectory extrapolation and fusion techniques for exploring all possible transitions among the mobility models. We validate the results with simulated traces of mobile targets generated using network simulator NS-2. Simulation results show that MIRACLE estimates the mobility patterns inside coverage hole with an accuracy of more than 60% in WSNs.

Optimization of starch isolation from taro using combination of enzymes and comparison of properties of starches isolated by enzymatic and conventional methods.

The optimization of enzymatic starch isolation process from taro tubers using cellulase and xylanase was carried out. The functional properties of starch isolated by optimized enzymatic process were compared with starch isolated by conventional method without the use of enzymes. A central composite rotatable design (CCRD) with four numerical factors was employed to design the experiments. The numerical factors were cellulase concentration (0-100 U/100 g tuber), xylanase concentration (0-100 U/100 g tuber), temperature of incubation (30-50 °C) and incubation time (1-5 h). Statistical analysis showed that the main effects of all the factors were significant on starch yield and effect of cellulase was more significant compared to xylanase. The effectiveness of xylanase in increasing the yield of starch from taro tubers confirmed that xylan is an important component of the cell walls of taro tubers. The optimized condition with maximum starch yield (17.22 %) was obtained when cellulase and xylanase concentration were 299.86 and 300 U/100 g tuber, temperature was 35 °C and incubation time was 2 h. The swelling of the starch granules increased whereas solubility decreased for enzymatic method. The clarity of the starch paste isolated by enzymatic method was found to be better compared to the clarity of starch paste isolated by conventional method. The pasting temperature of the starch paste was slightly higher and viscosity was lower for the starch isolated by enzymatic method. Freeze-thaw stability of the starch paste was also found to be better for the enzymatically isolated starch.

Collision bottleneck throughput in bacterial conjugation-based nanonetworks.

Bacterial conjugation-based nanonetwork has been recently proposed as a novel molecular communication paradigm, in which the bacteria act as carriers. This is the foundational work proposing the phenomenon of collision which occurs in the form of multi-conjugation of multiple carrier bacteria at the side of receiver nanodevice. We show the effect of this conjugation-based collision on the maximum achievable throughput of the network, using a simple graph-theoretic approach, namely, Maximum Weight Bipartite Matching. One of the several interesting results that emerges concerns the maximum achievable throughput, which is bounded by Θ(n/p) in case of homogeneous nodes, where n and p refer to the total number of nodes, and the vertical layers in the network, respectively.

Priority-based time-slot allocation in wireless body area networks during medical emergency situations: an evolutionary game-theoretic perspective.

In critical medical emergency situations, wireless body area network (WBAN) equipped health monitoring systems treat data packets with critical information regarding patients' health in the same way as data packets bearing regular healthcare information. This snag results in a higher average waiting time for the local data processing units (LDPUs) transmitting data packets of higher importance. In this paper, we formulate an algorithm for Priority-based Allocation of Time Slots (PATS) that considers a fitness parameter characterizing the criticality of health data that a packet carries, energy consumption rate for a transmitting LDPU, and other crucial LDPU properties. Based on this fitness parameter, we design the constant model hawk-dove game that ensures prioritizing the LDPUs based on crucial properties. In comparison with the existing works on priority-based wireless transmission, we measure and take into consideration the urgency, seriousness, and criticality associated with an LDPU and, thus, allocate transmission time slots proportionately. We show that the number of transmitting LDPUs in medical emergency situations can be reduced by 25.97%, in comparison with the existing time-division-based techniques.

Green wireless body area nanonetworks: energy management and the game of survival.

In this paper, we envisage the architecture of Green Wireless Body Area Nanonetwork (GBAN) as a collection of nanodevices, in which each device is capable of communicating in both the molecular and wireless electromagnetic communication modes. The term green refers to the fact that the nanodevices in such a network can harvest energy from their surrounding environment, so that no nanodevice gets old solely due to the reasons attributed to energy depletion. However, the residual energy of a nanodevice can deplete substantially with the lapse of time, if the rate of energy consumption is not comparable with the rate of energy harvesting. It is observed that the rate of energy harvesting is nonlinear and sporadic in nature. So, the management of energy of the nanodevices is fundamentally important. We specifically address this problem in a ubiquitous healthcare monitoring scenario and formulate it as a cooperative Nash Bargaining game. The optimal strategy obtained from the Nash equilibrium solution provides improved network performance in terms of throughput and delay.

Information warfare-worthy jamming attack detection mechanism for wireless sensor networks using a fuzzy inference system.

The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes.

Random early detection for congestion avoidance in wired networks: a discretized pursuit learning-automata-like solution.

In this paper, we present a learning-automata-like The reason why the mechanism is not a pure LA, but rather why it yet mimics one, will be clarified in the body of this paper. (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as LAL Random Early Detection (LALRED), is founded on the principles of the operations of existing RED congestion-avoidance mechanisms, augmented with a LAL philosophy. The primary objective of LALRED is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue. We attempt to achieve this by stationing a LAL algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. At every time instant, the LAL scheme, in turn, chooses the action that possesses the maximal ratio between the number of times the chosen action is rewarded and the number of times that it has been chosen. In LALRED, we simultaneously increase the likelihood of the scheme converging to the action, which minimizes the number of packet drops at the gateway. Our approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue-loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of LALRED over the traditional RED methods which were chosen as the benchmarks for performance comparison purposes.

Dynamic algorithms for the shortest path routing problem: learning automata-based solutions.

This paper presents the first Learning Automaton-based solution to the dynamic single source shortest path problem. It involves finding the shortest path in a single-source stochastic graph topology where there are continuous probabilistic updates in the edge-weights. The algorithm is significantly more efficient than the existing solutions, and can be used to find the "statistical" shortest path tree in the "average" graph topology. It converges to this solution irrespective of whether there are new changes in edge-weights taking place or not. In such random settings, the proposed learning automata solution converges to the set of shortest paths. On the other hand, the existing algorithms will fail to exhibit such a behavior, and would recalculate the affected shortest paths after each weight-change. The important contribution of the proposed algorithm is that all the edges in a stochastic graph are not probed, and even if they are, they are not all probed equally often. Indeed, the algorithm attempts to almost always probe only those edges that will be included in the shortest path graph, while probing the other edges minimally. This increases the performance of the proposed algorithm. All the algorithms were tested in environments where edge-weights change stochastically, and where the graph topologies undergo multiple simultaneous edge-weight updates. Its superiority in terms of the average number of processed nodes, scanned edges and the time per update operation, when compared with the existing algorithms, was experimentally established. The algorithm can be applicable in domains ranging from ground transportation to aerospace, from civilian applications to military, from spatial database applications to telecommunications networking.