AU2021105613A4 - A system and method of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations - Google Patents
A system and method of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations Download PDFInfo
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Abstract
The present invention generally relates to a system and a method
of uniform distributed wireless sensor nodes in medical tools for
edge/cloud-based computations. The method comprises employing QRD
approach for identifying a defective node from a plurality of sensor nodes
configured with medical devices for transferring information to nearest
neighbor nodes; transferring information from the nearest neighbor nodes
to a main node for activating a triggering alarm configured for alerting
surgeons or operators when the sensor node approach towards a
threshold level; and promoting precautionary steps to avoid any severe or
fatal operations in case of unavailability of experts during urgent
operations.
71
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Description
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The present invention relates to a system and method of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations.
Implementing the soft computational paradigm for smart healthcare and other IoMT applications is very difficult as well as necessary. The necessity of adopting soft computing in the medical profession includes regular to pandemic crisis care (as in COVID-19), which is required in the event of an urgent demand for clinical services based on geographical location and resource availability. Existing soft computational techniques, in general, rely on classical algorithms with restricted computer resources. Although cloud-based computations are being employed for long-term diagnosis, implementing mobile edge computing for IoMT remains difficult. Existing medical facilities are not only inefficient in terms of resource management, but they also have high deployment costs.
In the fields of Internet of Things (IoT), Industrial IoT (IoT), and various smart applications, the introduction of advanced machine learning techniques, neural networking with fuzzy logic, and other artificial intelligence models has evolved as a breakthrough technology. Despite this, the Internet of Medical Things (IoMT) is emerging as smart health and other medical-related IoT solution. The Internet of Things (IoMT) consists of a variety of human-to-machine and machine-to-human solutions for remote monitoring and diagnosis, as well as medical recommendations and procedures wherein blood pressure, heart rate, oxygen level, body temperature, and other indicators can be monitored remotely while the doctor consults with the patient. However, IoMT will give an efficient solution if an epidemic or pandemic (COVID-19) occurs by monitoring, calculating, and processing data at the edge or in the cloud. Because data collection for pre and post-diagnosis of exposed patients is a major concern. Therefore IoMT necessitates an intelligent edge solution with computing capabilities to address the situation if any problems arise during cloud connectivity. In some circumstances, transactional data is saved in edge-based or cloud-based online storage for fast diagnosis. In these types of catastrophic pandemic (COVID-19) scenarios, traditional smart healthcare systems lack ubiquitous computing when doing remote therapy and treatment.
In hospitals, quarantine hubs, and other therapeutic services, AI based models and architectures in IoMT will stand as a promising solution with efficient and precise computing capabilities of ubiquitous networking. There is a lot of study on computing in smart healthcare systems, but more work is needed to investigate the computation capabilities of ubiquitous networks i.e. pre-symptoms observations and finite tests with restricted medical facilities which remain a major issue for researchers and the medical industry, especially in these pandemic scenarios with an alarming proportion of COVID-19 positive individuals. In Australia, distant health care systems can be updated in places like Torres Strait Island, New Hampshire, and others, especially during emergency situations. The specialists require some time to begin their work, and the time it takes them to arrive can be significant. Psychological discomfort, chronic disease, and multimorbidity have also been observed in several parts of southern Australia. So, by utilizing edge-based remote monitoring and health-care processes, specialists may counsel, monitor, and administer efficient Medicare without having to travel to these remote locations.
In the view of the forgoing discussion, it is clearly portrayed that there is a need to have a system and method of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations.
The present disclosure relates to a system and method of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations. More particularly the proposed model help during the rapid diagnosis process of patients. The proposed work is supported by the novel QRD algorithm that will help in immediately identifying the dead nodes and active nodes participating in edge or cloud-based communication. The work focuses on improving the cooperative networking strategy with minimal error and energy efficient.
In an embodiment, a method of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations. The method comprises employing QRD approach for identifying a defective node from a plurality of sensor nodes configured with medical devices for transferring information to nearest neighbor nodes. The method further comprises transferring information from the nearest neighbor nodes to a main node for activating a triggering alarm configured for alerting surgeons or operators when the sensor node approach towards a threshold level. The method further comprises promoting precautionary steps to avoid any severe or fatal operations in case of unavailability of experts during urgent operations.
In an another embodiment, the sensor nodes are defective nodes configured with the medical devices, wherein the medical devices used with the patient bodies and the nodes that are nearest to the important organs, and neurons are the defective nodes.
In an another embodiment, the QRD approach focuses on identifying the defective nodes.
In an another embodiment, robots are promoted for spontaneous response and are able to perform critical surgeries due to direct connection with the sensors.
In an another embodiment, connecting directly with all the sensors to the main server are encouraged through interaction with nearest neighbors which helps the information to move faster thereby results in fast edge computations.
In an another embodiment, a system of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations is disclosed. The system includes a plurality of sensor nodes equipped with QRD approach configured with medical devices for transferring information to nearest neighbor nodes. The system further includes a main node for receiving information from the nearest neighbor nodes for activating a triggering alarm, wherein the triggering alarm is configured for alerting surgeons or operators when the sensor node approach towards a threshold level thereby promoting precautionary steps to avoid any severe or fatal operations in case of unavailability of experts during urgent operations.
In an another embodiment, the wireless sensor nodes are uniformly distribution in the medical tools and appliances.
In an another embodiment, edge and cloud-based computations are performed herein for promoting rapid diagnosis process of patients.
In an another embodiment, a complete analysis of the patient's health status based on historical data, present health progress from different test reports, doctor's prediction, and similar data collected is evaluated herein to provide detailed progress of the treatment process by suggesting medicines, diet plans, and similar related recommendations.
In an another embodiment, a central processing unit configured with sensor nodes and main node for triggering the alarm for alerting surgeons or operators.
An object of the present disclosure is to provide a system and method of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations.
Another object of the present disclosure is to perform edge or cloud-based computations.
Yet another object of the present disclosure is to provide necessary authorization, security with clinical validations.
Yet another object of the present disclosure is to immediately identifying the dead nodes and active nodes participating in edge or cloud-based communication.
Yet another object of the present disclosure is to enhances the cooperative networking strategy with minimal error and energy efficient.
To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates a flow chart of a method of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations in accordance with an embodiment of the present disclosure; Figure 2 illustrates a block diagram of a system of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations in accordance with an embodiment of the present disclosure; Figure 3 illustrates the proposed framework for Health 4.0 in accordance with an embodiment of the present disclosure; Figure 4 illustrates a three-layer FC based e-Health architecture in accordance with an embodiment of the present disclosure; Figure 5 illustrates an architecture related to the smart e-health gateway model in accordance with an embodiment of the present disclosure; Figure 6 illustrates a Mobile Edge Cloud MEC in the healthcare domain in accordance with an embodiment of the present disclosure; Figure 7 illustrates a broad research objective in accordance with an embodiment of the present disclosure; Figure 8 illustrates a proposed model for edge and cloud enabled devices in accordance with an embodiment of the present disclosure;
Figure 9 illustrates a block diagram for the proposed algorithm in accordance with an embodiment of the present disclosure; Figure 10 illustrates a node distribution in a geographical area with defective nodes marked as red in accordance with an embodiment of the present disclosure; Figure 11 illustrates an algorithms identifying the defective nodes in accordance with an embodiment of the present disclosure; Figure 12 illustrates a mean Square Error performance of all the nodes combined in accordance with an embodiment of the present disclosure; Figure 13 illustrates table 1 depicts computation complexity at node k in accordance with an embodiment of the present disclosure; Figure 14 illustrates an excess Mean Square Error performance of all the nodes combined in accordance with an embodiment of the present disclosure; Figure 15 illustrates a mean Square Deviation performance of all the nodes combined in accordance with an embodiment of the present disclosure;
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other
R components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
Referring to Figure 1, a flow chart of a method of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations is illustrated in accordance with an embodiment of the present disclosure. At step 102, the method 100 includes employing QRD approach for identifying a defective node 208 from a plurality of sensor nodes 202 configured with medical devices for transferring information to nearest neighbor nodes 204.
At step 104, the method 100 includes transferring information from the nearest neighbor nodes 204 to a main node 206 for activating a triggering alarm 210 configured for alerting surgeons or operators when the sensor node 202 approach towards a threshold level.
At step 106, the method 100 includes promoting precautionary steps to avoid any severe or fatal operations in case of unavailability of experts during urgent operations.
In an embodiment, the sensor nodes 202 are defective nodes 208 configured with the medical devices, wherein the medical devices used
q with the patient bodies and the nodes that are nearest to the important organs, and neurons are the defective nodes 208.
In an embodiment, the QRD approach focuses on identifying the defective nodes 208.
In an embodiment, robots are promoted for spontaneous response and are able to perform critical surgeries due to direct connection with the sensors.
In an embodiment, connecting directly with all the sensors to the main server are encouraged through interaction with nearest neighbors which helps the information to move faster thereby results in fast edge computations.
Figure 2 illustrates a block diagram of a system of uniform distributed wireless sensor nodes 202 in medical tools for edge/cloud based computations in accordance with an embodiment of the present disclosure. The system 200 includes a plurality of sensor nodes 202 equipped with QRD approach configured with medical devices for transferring information to nearest neighbor nodes 204.
In an embodiment, a main node 206 for receiving information from the nearest neighbor nodes 204 for activating a triggering alarm 210, wherein the triggering alarm 210 is configured for alerting surgeons or operators when the sensor node 202 approach towards a threshold level thereby promoting precautionary steps to avoid any severe or fatal operations in case of unavailability of experts during urgent operations.
In an embodiment, the wireless sensor nodes 202 are uniformly distribution in the medical tools and appliances.
1i
In an embodiment, edge and cloud-based computations are performed herein for promoting rapid diagnosis process of patients.
In an embodiment, a complete analysis of the patient's health status based on historical data, present health progress from different test reports, doctor's prediction, and similar data collected is evaluated herein to provide detailed progress of the treatment process by suggesting medicines, diet plans, and similar related recommendations.
In an embodiment, a central processing unit configured with sensor nodes 202 and main node 206 for triggering the alarm for alerting surgeons or operators.
This project deals with one of the important aspects of humanity i.e. health-care. Many researchers have put effort into their innovative ideas in developing the Internet of medical things (IoMTs) for health 4.0. To handle these IoMTs, some algorithms and models have developed. In this section, some of these related works have been discussed.
The IoMT based on two sub networks intra namely WBAN and beyond WBAN (Wireless body area network). The objective was to minimize system costs. They have proposed some models that helped the media units for a cost-effective in the 5G health care system but with high computational issues. The ETS-DNN (Effective training scheme deep neural network), model have proposed which generates the diagnosis report using the help of edge computing which can provide to different health care centers and professionals. It was claimed that this model will help especially in the remote area and the places where advanced diagnosis centers are not available which can use the edge computations in medical devices for emergency response. Similarly, Fig.3 shows the general framework to describe the basic IoMT structure.
A patented work where a wearable devices that can able to send the data to the cloud continuously and securely providing the status of health. A high-level encryption and the keys are distributed and restricted to concerned medical professionals only. An architecture based on IoMT devices and the mathematical model designed was proposed and provides energy efficient compatibility with the devices. A recommender system was formulated where the system recommends patients timely medicine and food. Then machine learning concepts was used to develop the models to work on brain images and signals. Taking the IoMT devices for data acquisition and slicing them based on the severity in 5G communication may help in finding the critical cases in real-time. A 3 layer fog computing based e-Health architecture model was proposed as shown in Fig. 4. An architecture was proposed that looks after the maintenance of the IoMT device to prevent any mishappenings during critical periods. Fig.5 shows an architecture related to the smart e-health gateway model.
The implementation of IoMT devices for monitoring the heart by sending data to the cloud and warns the healthcare professionals in case of emergency using machine learning algorithms. Different architectures and mathematical models was proposed using the help of IoMT wearable devices to monitor the health in real-time by interfacing the cloud with the devices. The cloud contains the high-end machine learning tools responsible for acquiring real-time data and processes it continuously so that it can estimate timely if there is any chance of health issues. There are some review papers also which have a good amount of information in the area of our project. These research articles focused on collecting information in modeling, architecture aspects. Also, how the cloud-based, fog based and mobile edge-based computing methods have been categorized for easy analysis of the work done in these fields. A perspective was shown on how mobile edge-based cloud computing can be helpful for the healthcare domain which can be depicted through Fig. 6 which explains the basic mobile edge cloud for healthcare domain. As shown, the edge cloud is used for cooperative communication, computing and migration (wherever required) from one cloud to another depending on the ease of delivering the clinical service with respect to the data obtained from the edge clusters.
The proposed work can be used in mobile-edge and cloud-based tools where, the number of sensors are placed are uniformly distributed. The objectives of the proposed work are: • Developing a high-speed diagnosis model that can provide quick prediction like an estimated list of disease categories • To develop a fast diagnosis model which can categorize the patient into severity category The applications of the proposed work can be considered as: • A model that can be able to provide a complete analysis of the patient's health status based on historical data, present health progress from different test reports, doctor's prediction, and similar data collected. This will help to provide detailed progress of the treatment process by suggesting medicines, diet plans, and similar related recommendations. It can also able to provide different treatment procedures that can be adopted by the doctors based on the financial condition of the patient like alternatives of surgeries or slow recovery methods etc.
At any ' time instant, all the vectors of every node in the network
are rearranged to form a global matrixI 1 21 *.. XN4and measured
data vector Y= '2'N . The global input regressor and measured data of the network are now related by a linear model given as y, = X,w+v (1) where is noise rearranged in global form and is expressed as vi=[v[(i),v2 (i),...,vN()]T . The input regressor and the noise are assumed to be spatially and temporarily independent and identically distributed (i.i.d.) with zero mean.
ALGORITHM: QR based Diffusion algorithm for node k Start with C=I(assumed here to take only weight diffusion), Wk,-i =0 for all kand for each time i > 0 and for each node repeat
For k =1:Nincremental update wk-1 _1
For alllek
Collect X1 ,y 1 R,(i -1), u,(i -1)
ej [(y,)-(xi ).T kJ1
cgIohaI,i =diag(e:Nj)
N Rk (i - 1)=Y c1,kR (i - 1) 1-1
N uk(i -1)= ckU(i-1)
end
[_kj) uki)]*- F ,fA.Rk(i -1) -k V-.uk(i -1)
Calculation of BHT using Hkon Rk and k(0
[ Rk Wuk(i)]<- THk(kW[Rk W"k W)
T k = (R k i4uk, )
end
alkj 'k; k k,i lT j - -22 k
kj k4k,i c, = a*,, optional. Spatial update for every node k repeat N
1-1
Check if
w 1 -w1 2 .Then update
* The proposed novel QRD algorithm focuses on identifying the defective nodes. These nodes the sensors that are put on the medical devices. When the devices are used with the patient bodies then the nodes that are nearest to the important organs, neurons etc. then they will be called as defective nodes. These nodes will transfer the information to the nearest neighbour nodes and eventually it will transfer the information to the main node that is connected with the triggering alarm informing the surgeons or operators that they are approaching towards the threshold level. In this way appropriate precautionary steps can be taken in order to avoid any severe or fatal operations. This can be very much helpful when the experts are not available and the operations are carried out on urgency basis. Also, in the remote places where reaching the specialists is not possible but remotely, they can operate or can help to operate the other young doctors or nurses to operate critical and emergency cases.
The algorithm developed shows that the steady state is achieved very fast with minimized error which is compared with the traditional algorithm. Also, during robotic operations the robots can take spontaneous response and can perform critical surgeries as it is connected with all the sensors directly. Since, connecting directly with all the sensors to the main server is not possible, the interaction with the nearest neighbours helps the information to move faster and hence results in fast edge computations.
For simulation, a total of 16 nodes are assumed in a square pattern of 4 nodes at each side. From the 16nodes 3 nodes are assumed to be deviating from normal weight (Shown in red circular mark) in Fig 10. For implementation of QRD method, the block size and the forgetting factor is taken. For clustering the weight combining parameter is taken with step size . The data combining parameter is assumed identity matrix. A data of lengthlOOO is taken for simulation and is simulated for 20 independent experiments. Noise variance of is present at all the nodes.
Fig. 10 demonstrates the arrangement of 16 wireless sensor nodes in a simulated geographical area. The nodes are represented using circles act as input geographical simulation condition to the algorithm. In the proposed work, geographical area is considered as the local clinical trial area and the sensors are placed equidistant in the medical tools. In the figure green color marked circles reflect the normal nodes and the red circle nodes reflect the defective nodes or the nodes that deviate from primary optimal weight. The nodes which may fail to operate or communicate between another node are assumed to be as defective nodes. Further, the energy consumption of these nodes is assumed to be 0.
In Fig. 11, the defective/deviating nodes are identified by both of the algorithm presented in the paper. The cyan mark square symbol represents the identification by classical RLS method and the red marked rhombus symbol represents the identification by QRD-based method. Both the methods have successfully identified the defective nodes. The defectives nodes are then assumed to be a non-participant for the mobile edge or cloud-based computations. This will save the energy required during a continuous communication between node-node or node-cloud.
16F
Fig. 12, 14, and 15 shows the performance results in terms of MSE, EMSE, MSD of both the algorithms. In case of MSE Fig.5, the performance for proposed QRD-method shows better result compared to the classical RLS method which is used during the real-time computational operation in the nodes. But in case EMSE and MSD as depicted in Fig. 12 and Fig. 14, it can be observed that the classical RLS result out performs the proposed QR method of implementation, which signifies the effectiveness towards the accuracy of the model. Further, it can be concluded that the error performance during this edge and cloud-based computations results in more reliable and energy efficient manner.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
1R
Claims (10)
1. A method of uniform distributed wireless sensor nodes in medic al tools for edge/cloud-based computations, the method comprises:
employing QRD approach for identifying a defective node from a plurality of sen sor nodes configured with medical devices for transferring information to nearest neighbor nodes; transferring information from the nearest neighbor nodes to a main node for activating a trig gering alarm con figured for alerting surgeons o r ope rators wh en the sen sor node approach tow ards a threshold level; and promoting precautionary s teps to avoid an y severe or fata I operations in case of unavailability of experts during urgent operations.
2. The method as claimed in claim 1, w herein the sensor nodes are defective nodes configured with the medical devices, wherein the medical devices used w ith the patie nt bodie s an d t he nodes t hat are ne arest to the important organs, and neurons are the defective nodes.
3. The method as cla imed in claim 1, w herein the QRD approa ch focuses on identifying the defective nodes.
4. The method as claimed in claim 1, wherein robots are promoted for spontaneous response a nd are a ble to perform critical surgeries due to direct connection with the sensors.
5. The method as claimed in claim 4, wherein connecting directly with all t he se nsors to the main serve r a re encou raged th rough inte raction with nearest ne ighbors which helps th e information to m ove faster thereby results in fast edge computations.
1q
6. A system of uniform distributed wireless sensor nodes in medical tools for edge/cloud-based computations, the system comprises:
a plurality of sensor nodes equipped with QRD approach configured with medi cal devices f or transferring in formation to nearest nei ghbor nodes;and a main node for receiving information from the nearest neighbor nodes f or activating a tr iggering al arm, wh erein the tr iggering alarm is configured for alerting surgeons or ope rators w hen the sensor node approach tow ards a thres hold leve I th ereby promot ing precautionary steps to avoid any severe or fatal operations in case of unavailability of experts during urgent operations.
7. The s ystem as clai med in cl aim 6, wherein the wire less sensor nodes are uniformly distribution in the medical tools and appliances.
8. The system as claim ed in claim 6, wh erein edge and c loud-based computations are performed herein for promoting rapid diagnosis process of patients.
9. The system as cla imed in claim 6, wherein a complete analysis of the patien t's health statu s based on historical d ata, present health progress from different test reports, doctor's prediction, and similar data collected is evaluated herein to provide detailed progress of the treatment process by su ggesting medic ines, diet plans , and simila r r elated recommendations.
10. The system as claimed in claim 6, comprises a central processing unit configured with sensor nodes and main node for triggering the alarm for alerting surgeons or operators.
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