Internet of Things (IoT) has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices. The e-healthcare application solely depends on the IoT and cloud computing environment, has provided several characteristics and applications. Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing, which led to quick exhaustion of energy. In this view, this paper introduces a new energy efficient cluster enabled clinical decision support system (EEC-CDSS) for embedded IoT environment. The presented EEC-CDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process. The EEC-CDSS model incorporates particle swarm optimization with levy distribution (PSO-L) based clustering technique, which clusters the set of IoT devices and reduces the amount of data transmission. In addition, the IoT devices forward the data to the cloud where the actual classification procedure is performed. For classification process, variational autoencoder (VAE) is used to determine the existence of disease or not. In order to investigate the proficient results analysis of the EEC-CDSS model, a wide range of simulations was carried out on heart disease and diabetes dataset. The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.
Recently, population increase in old age has resulted in serious chronic and health problems globally which result in enhanced medical expenses for common people [
Cloud and IoT methods enable prominent development and service interpretation by applying cloud service based schemes [
The combination of Cloud and IoT is involved in making effective medical domains for monitoring the clinic centers and patients. Cloud IoT update the healthcare application by creating a collaboration between different units. In order to make simple works for patients suffering from acute disease, the paradigms of Ambient Assisted Living (AAL) is deployed [
Under the application of Cloud IoT in medical sector in the combination of Information and Communication Technology (ICT) model which has been interrelated applications, sensors material, and backend users coordinates and operate jointly as smart method in monitoring and recording the patient details. There are 3 main elements namely: smart wearables and implantable sensor nodes for data accumulation, data communication that can be applied for actual and trusted communication of sensed details to the medical datacenter, and cloud data archival for computation, analysis, and visualization. The on-demand service mechanism of cloud offers error free accessibility to physicians under different pool of data from dissimilar resources with Electronic Medical Record (EMR), prescription, and laboratory report.
This paper devises a novel energy efficient cluster based clinical decision support system (EEC-CDSS) for embedded IoT environment. The objective of the EEC-CDSS model is to effectively communicate the medical data from IoT devices and accomplish accurate diagnostic process. The EEC-CDSS model includes particle swarm optimization with levy distribution (PSO-L) based clustering technique, which groups the set of IoT devices and decreases the quantity of data transmission. Moreover, the IoT devices propagate the information to the cloud-server where the actual diagnostic procedure is performed using variational autoencoder (VAE). For examining the proficient performance of the EEC-CDSS model, an extensive set of experimentations were performed on the benchmark heart disease and diabetes dataset.
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Basically, PSO is referred as a population-based optimization method. The random solution is stimulated with population and searching for better solutions. The applicable solutions gained from each round are called a particle, which contains the details derived from the coordinate points which are related to gain best solution using optimal particles. The fitness function (FF) of the particles is executed and considered as best solution. Therefore, the FF value of current optimal particle is called p_best. Moreover, it is suitable to optimize better population metrics gained by different particles from neighbors and location is meant to be l_best.
If the population is applied as the topological neighbor by special particles, afterward the best value is decided from created population and exact optimum values are considered as best solution called g_best. The velocity is estimated by random models which are comprised of randomly generated values for velocity by means of p_best and l_best coordinates. For huge volume of produced solutions, tremendous value is selected to resolve the issue [
Levy flight is defined as a Markov procedure. Also, the Levy flight is considered to be a common random walk approach, that implies a type of non-Gaussian stochastic computation and relevant to the Levy stable distribution. The consistent improvement follows the Levy steady distribution. It is classified by minimum steps but it is represented in large steps, thus the moving entities are not repeated in searching the similar place, modifying the system behavior. Even though the motion direction is random, the motion step size is considered as exponential rate distribution. The integration of PSO and Levy flight models extends the search range of the technique and enhance the diversity of population, and prevents from trapping of local optimum.
Recently, Levy flight is employed extensively in optimization field, and results indicate that Levy flight has accomplished better outcomes [
Here,
where
where
Here, IoT device telecasts the data to adjacent IoT devices. When it gains the data, it is enclosed with node identifier, location which is the distance from
Step-1: Conversion of PSO-L space in which PSO-L particle is limited with 2D such as particle position as well as velocity.
Step-2: The newly developed FF for EEC-CDSS model is employed to optimize extreme distance as well as energy of cluster members (CM) and CH. The FF value is determined by the use of
where
Step-3: Generation of new particles from the fundamental solutions. Deployment of new particles from previous solution is a method of emulating novel particles.
Step-3.1: Estimation of novel velocity: the current particle’s velocities can be determined by modifying the position. It can be expressed as:
where
Step-3.2: Estimation of novel position for particle is expressed as:
Consequently, the new particle is developed.
Step-4: Evaluation of FF measures for new particles.
FF values of novel particles are processed using FF in Step 2 accompanied by velocity as well as novel position.
Step-5: The FF score of existing particle and new particle is compared with best one in upcoming round:
If fresh FV > traditional FV
Pick up the novel particle;
else
previous particles are transmitted to upcoming round.
Step-6: For every round, the optimum solutions have been chosen as l_best solution. The particle having maximum FF value in current round is selected as l_best solution.
Step-7: The l_best solution of particle that is maximum than alternate solution considered as g_best solution. Thus, the final one is decoded under the application of clusters.
The cluster is generated with the application of PSO-L and telecasts the cluster data for IoT devices which are enclosed with applied data. The IoT device is applicable to save the message and proceed for CH selection.
Followed by, IoT device preserves the cluster list. It is composed of currently clustered, velocity, location, and energy. Then, it is employed for CH election process.
Step 1. The members exist closer to the current node are suitable for communicating with each other for CH election.
Step 2. Estimate FF:
where
Step 3. Generation of new particles from basic set of solutions.
Step 3.1. Estimation of new velocity as represented as follows:
where
Step 3.2. Estimation of novel position by fresh velocity:
New position
Finally, new particle has been developed.
Step 4. Evaluate FF score of new particle.
The FF values of new particles are processed using FF scores as depicted in Step 2 with new velocity’s position.
Step 5. FF of former and novel particle are relevant and normalized solution is selected for computing the future round:
When new FF
Elect new particle;
else
former particle is promoted to subsequent round.
Step 6. Under all round, the considerable solutions are elected as l_best solutions.
Step 7. For each round, solitary l_best solution has been examined and the particle with maximum l_best solutions are referred as g_best solution.
Once the IoT CHs transmit the data to the cloud server, data preprocessing takes place to transform the data into a compatible format. Besides, VAE based classification process is performed to assign the class labels of the applied medical data. In general, Autoencoder (AE) is defined as a Neural Network (NN) model which is comprised of operating logic for training the input vector to redevelop the output vector under unsupervised method. Basically, the structure is developed by encoding and decoding units. The individual layer of AE is composed of encoding and decoding units as depicted in
Under the application of affine mapping that results in nonlinearity and conversion of input vector
VAE is a directed probability based graphical method that is accomplished by the application of Artificial Neural Network (ANN) to the subsequent end. Here, latent parameter
Here,
The attributes of approximate posterior
VAE training is accomplished under the application of Backpropagation (BP) scheme. Then, the latter portion of
The re-parameterization task makes sure that
The stochastic latent parameters generate the variables of actual input variable distribution is applied for RP computation. Then, the probability of data is generated from specific latent parameters considered from approximate posterior distribution.
Afterward, RP processed in VAE is modified from RE measured in AE. Firstly, if the latent variable is referred as deterministic mapping in AE, it is assumed to be stochastic variables in VAE. Also, difference in the latent space is considered from the sampling process since the VAE applies probabilistic encoder for modeling the distribution of latent variables rather than using latent variables. Next, reconstructions are stochastic parameters in VAE. RP considers the variations among actual input and reconstruction, where the variability of reconstruction is assumed in variance attributes. Hence, defined sensitivity for reconstruction with variable difference is empowered under the application of this feature, which has not been accessible in AE because of the deterministic behavior. Next, probability metrics correspond the reconstructions in VAE. Mostly, in AE dependent disease classification, detection scores are emanated by RE. Additionally, the estimation of appropriate objective threshold for RE is considered to be complicated process. Besides, since the probability distribution of each parameter activates the independent computation of single variability and calculation of RP does not require weighing of RE in heterogeneous data. As a result, it is finalized that the estimation of threshold values of RP is activated effectively with maximum objective when compared with coherent RE.
The performance of the EEC-CDSS technique has been validated against the UCI data repository and medical data collected by the IoT devices. In this study, the experimental results are tested against heart disease and diabetes dataset [
Sensitivity (%) | |||||
---|---|---|---|---|---|
Instance count | KNN | NB | SVM | DT | EEC-CDSS |
2000 | 92.28 | 87.49 | 82.48 | 92.95 | 95.87 |
4000 | 88.08 | 84.19 | 81.68 | 91.95 | 96.11 |
6000 | 92.88 | 85.99 | 83.18 | 93.25 | 97.63 |
8000 | 92.08 | 88.19 | 81.68 | 96.55 | 98.72 |
10000 | 93.28 | 88.69 | 83.48 | 95.65 | 98.19 |
Specificity (%) | |||||
Instance count | KNN | NB | SVM | DT | EEC-CDSS |
2000 | 83.88 | 82.99 | 79.48 | 92.25 | 94.52 |
4000 | 85.78 | 83.19 | 81.38 | 90.85 | 94.51 |
6000 | 86.98 | 86.49 | 82.68 | 92.05 | 95.55 |
8000 | 87.98 | 81.69 | 77.68 | 88.25 | 93.59 |
10000 | 88.98 | 85.99 | 83.58 | 90.05 | 94.73 |
Accuracy (%) | |||||
Instance count | KNN | NB | SVM | DT | EEC-CDSS |
2000 | 89.08 | 76.39 | 72.68 | 91.25 | 95.17 |
4000 | 90.98 | 78.19 | 76.98 | 92.05 | 94.98 |
6000 | 87.28 | 77.39 | 74.88 | 90.05 | 96.23 |
8000 | 86.08 | 79.69 | 77.68 | 92.85 | 95.57 |
10000 | 88.98 | 81.99 | 80.88 | 92.45 | 95.42 |
A sensitivity analysis of the EEC-CDSS model with existing techniques denoted that the NB and SVM models have showcased poor performance whereas the KNN and DT models have exhibited moderately closer sensitivity value. But the presented EEC-CDSS model has accomplished a maximum sensitivity under all the instances. For instance, in the presence of 2000 instances, the EEC-CDSS model has demonstrated effective sensitivity of 95.87% whereas the KNN, NB, SVM, and DT models have led to a reduced sensitivity of 92.28%, 87.49%, 82.48%, and 92.95% respectively. On the other hand, on the existence of 10000 instances, the EEC-CDSS model has depicted effective sensitivity of 98.19% whereas the KNN, NB, SVM, and DT models have led to a minimum sensitivity of 93.28%, 88.69%, 83.48%, and 95.65% respectively.
Specificity analysis of the EEC-CDSS method with existing techniques denoted that the NB and SVM models have showcased inferior performance whereas the KNN and DT models have exhibited moderately closer specificity value. However, the projected EEC-CDSS model has accomplished a high specificity under all the instances. For instance, in the presence of 2000 instances, the EEC-CDSS model has illustrated effective specificity of 94.52% whereas the KNN, NB, SVM, and DT models have led to a minimal specificity of 83.88%, 82.99%, 79.48%, and 92.25% respectively. Followed by, on the presence of 10000 instances, the EEC-CDSS model has projected effective specificity of 94.73% whereas the KNN, NB, SVM, and DT models have led to a reduced specificity of 88.98%, 85.99%, 83.58%, and 90.05% correspondingly.
Accuracy analysis of the EEC-CDSS model with previous techniques denoted that the NB and SVM models have showcased poor performance while the KNN and DT models have exhibited moderately closer accuracy value. But the developed EEC-CDSS model has accomplished a maximum accuracy under all the instances. For example, on the existence of 2000 instances, the EEC-CDSS model has depicted effective accuracy of 95.17% whereas the KNN, NB, SVM, and DT models have led to limited accuracy of 89.08%, 76.39%, 72.68%, and 91.25% respectively. On the other hand, in the presence of 10000 instances, the EEC-CDSS model has demonstrated effective accuracy of 95.42% and the KNN, NB, SVM, and DT models have led to minimal accuracy of 88.98%, 81.99%, 80.88%, and 92.45% respectively.
Sensitivity (%) | ||||||
---|---|---|---|---|---|---|
Instance count | kNN | NB | SVM | DT | FNC | EEC-CDSS |
2000 | 92.00 | 87.50 | 83.00 | 93.00 | 94.50 | 97.67 |
4000 | 88.00 | 86.00 | 82.50 | 92.00 | 93.50 | 98.32 |
6000 | 92.80 | 88.00 | 83.80 | 93.00 | 94.50 | 99.12 |
8000 | 93.50 | 88.00 | 83.00 | 97.00 | 98.00 | 99.43 |
10000 | 94.20 | 90.00 | 83.40 | 96.00 | 97.00 | 99.09 |
Specificity (%) | ||||||
Instance count | kNN | NB | SVM | DT | FNC | EEC-CDSS |
2000 | 84.00 | 83.00 | 82.00 | 92.50 | 94.00 | 96.18 |
4000 | 90.00 | 83.00 | 83.00 | 91.00 | 94.20 | 96.89 |
6000 | 87.00 | 86.00 | 83.00 | 93.00 | 94.10 | 95.67 |
8000 | 87.50 | 85.00 | 80.00 | 88.00 | 90.00 | 92.79 |
10000 | 90.00 | 87.00 | 84.00 | 90.50 | 92.00 | 95.82 |
Accuracy (%) | ||||||
Instance count | kNN | NB | SVM | DT | FNC | EEC-CDSS |
2000 | 89.00 | 77.00 | 74.00 | 92.00 | 93.00 | 96.72 |
4000 | 91.00 | 81.00 | 76.00 | 94.00 | 94.00 | 96.94 |
6000 | 87.00 | 76.00 | 75.00 | 90.00 | 91.00 | 95.89 |
8000 | 88.00 | 82.00 | 78.00 | 93.50 | 94.50 | 95.36 |
10000 | 90.00 | 83.00 | 80.00 | 92.50 | 94.00 | 96.54 |
A sensitivity analysis of the EEC-CDSS model with traditional techniques denoted that the NB and SVM models have showcased poor performance and the KNN and DT models have exhibited moderately closer sensitivity value. Additionally, the FNC model has outperformed higher to compare previous methods. Thus, the presented EEC-CDSS model has accomplished a maximum sensitivity under all the instances. For sample, on the presence of 2000 instances, the EEC-CDSS model has depicted effective sensitivity of 97.67% whereas the KNN, NB, SVM, DT, and FNC models have resulted in reduced sensitivity of 92%, 87.50%, 83%, 93%, and 94.50% respectively. Next, on the presence of 10000 instances, the EEC-CDSS model has illustrated productive sensitivity of 99.09% whereas the KNN, NB, SVM, DT, and FNC models have led to a minimum sensitivity of 94.20%, 90%, 83.40%, 96%, and 97% respectively. Specificity analysis of the EEC-CDSS model with existing techniques denoted that the NB and SVM models have shown poor performance whereas the KNN and DT models have exhibited moderately closer specificity value. Besides, the FNC model has outperformed higher to compare general methods. But the presented EEC-CDSS model has accomplished a maximum specificity under all the instances. For instance, in the presence of 2000 instances, the EEC-CDSS model has depicted efficient specificity of 96.18% whereas the KNN, NB, SVM, DT, and FNC models have led to lower specificity of 84%, 83%, 82%, 92.50%, and 94% respectively. On the other hand, on the application of 10000 instances, the EEC-CDSS model has demonstrated effective specificity of 95.82% while the KNN, NB, SVM, DT, and FNC models have led to a compact specificity of 90%, 87%, 84%, 90.50%, and 92% respectively. Accuracy analysis of the EEC-CDSS model with former techniques implied that the NB and SVM methods have showcased poor performance whereas the KNN and DT models have exhibited considerable accuracy value. Additionally, the FNC model has outperformed higher to compare previous methods. But the projected EEC-CDSS model has accomplished a maximum accuracy under all the instances. For example, in the presence of 2000 instances, the EEC-CDSS model has demonstrated effectual accuracy of 96.72% while the KNN, NB, SVM, DT, and FNC methods have resulted in minimum accuracy of 89%, 77%, 74%, 92%, and 93% respectively. Followed by, on the existence of 10000 instances, the EEC-CDSS model has demonstrated effective accuracy of 96.54% and the KNN, NB, SVM, DT, and FNC models have led to a lower accuracy of 90%, 83%, 80%, 92.50%, and 94% respectively.
No. of IoT devices | EEC-CDSS | CSO | GWO | PSO |
---|---|---|---|---|
50 | 44 | 62 | 65 | 69 |
100 | 51 | 64 | 72 | 76 |
150 | 56 | 69 | 74 | 78 |
200 | 62 | 73 | 79 | 83 |
250 | 64 | 79 | 84 | 86 |
Along with that, under the presence of 150 IoT sensors, the EEC-CDSS method has resulted in a reduced TEC of 56% while the CSO, GWO, and PSO algorithms have required an increased TEC of 69%, 74%, and 78% respectively. On continuing with, under the presence of 200 IoT sensors, the EEC-CDSS scheme has resulted in the least TEC of 62% whereas the CSO, GWO, and PSO approaches have required an increased TEC of 73%, 79%, and 83% respectively. Simultaneously, under the existence of 250 IoT sensors, the EEC-CDSS method has resulted in the least TEC of 64% whereas the CSO, GWO, and PSO algorithms have required an improved TEC of 79%, 84%, and 86% respectively. By looking into the above-mentioned tables and figures, it is observed that the EEC-CDSS model has resulted in an improved energy efficient performance with maximum classification performance. Therefore, the EEC-CDSS model can be employed as an effective tool for disease diagnosis in the embedded IoT environment without sacrificing energy.
This paper has presented a novel EEC-CDSS model for embedded IoT environments. The objective of the EEC-CDSS model is to effectively communicate the medical data from IoT devices and accomplish accurate diagnostic process. Primarily, the IoT devices placed on the human body observes the medical data. At the same time, the PSO-L-L algorithm gets executed to determine the optimal cluster heads (CHs) and perform cluster construction. Next to that, the CHs transmit the collected medical information to the cloud server where the actual diagnosis process begins. Subsequently, the VAE model is applied to determine the appropriate class label of the applied medical data. Finally, in case of abnormality, an alarm will be raised to alert the patient, hospital environment, and caretaker. For examining the proficient performance of the EEC-CDSS model, an extensive set of experimentations were performed on the benchmark heart disease and diabetes dataset. The obtained values stressed out the betterment of the EEC-CDSS model interms of energy efficiency and classification accuracy. In future, the performance of the EEC-CDSS model can be improvised using outlier detection and feature selection methodologies.