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To design and implement the QoS -Aware Energy Efficient Routing Mechanism for the BAN-IoT networks in Smart Health care Applications.

Babu N.V

Babu N.V

Department of Electronics and Communication Engineering, SJB Institute of TechnologyBangalore, India
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N.V, Babu
, 
Abhinav Kumar

Abhinav Kumar

Department of Nuclear and Renewable Energy, Ural Federal University Named after the First President of Russia Boris YeltsinEkaterinburg, Russia
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Kumar, Abhinav
 and 
J. Joshua Thomas

J. Joshua Thomas

UOW Malaysia KDU Penang University CollegeGeorge Town, Penang, Malaysia
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Thomas, J. Joshua
  
Feb 24, 2025
To design and implement the QoS -Aware Energy Efficient Routing Mechanism for the BAN-IoT networks in Smart Health care Applications.'s Cover Image
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Article Category: Article
Published Online: Feb 24, 2025
Page range: 55 - 78
Received: Aug 23, 2024
Accepted: Sep 30, 2024
DOI: https://doi.org/10.2478/jsiot-2024-0012
Keywords
© 2024 Babu N.V et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Chaotic Honey Badger Optimization-Based Routing Mechanism
Chaotic Honey Badger Optimization-Based Routing Mechanism

Figure 2

Distance Tuning for HBA
Distance Tuning for HBA

Figure 3:

Phase portraits of cubic nonlinear system with p1 tanh(x2 + g) function in 1st state
Phase portraits of cubic nonlinear system with p1 tanh(x2 + g) function in 1st state

Figure 4

Phase portraits of cubic nonlinear system with p1 tanh(x2 + g) function in 2nd state
Phase portraits of cubic nonlinear system with p1 tanh(x2 + g) function in 2nd state

Figure 5:

Bifurcation Diagram
Bifurcation Diagram

Figure 6:

Pseudocode for HBO Model
Pseudocode for HBO Model

Figure 6:

Delay Analysis in Optimized HBO Routing Protocols
Delay Analysis in Optimized HBO Routing Protocols

Figure 7

Average Packet Delivery Metrics for HBO Algorithms
Average Packet Delivery Metrics for HBO Algorithms

Figure 8

Analysis of Throughput in Optimization Models
Analysis of Throughput in Optimization Models

Figure 9

Analysis of Residual Energy Across Optimization Models
Analysis of Residual Energy Across Optimization Models

Figure 10:

Packet Overhead Evaluation Across Optimization Approaches
Packet Overhead Evaluation Across Optimization Approaches

Figure 11

Convergence Trends in Optimization Techniques
Convergence Trends in Optimization Techniques

Figure 12:

Convergence Analysis across Optimizations
Convergence Analysis across Optimizations

Fitness Outcomes Across Optimizers

Algorithm Best Worst Mean Median SD Variance
PSO 5.78 8.137 0.73010 0.023834 0.02565 4.542x10-6
ACO 5.34 6.532 0.56783 0.022940 0.02379 4.08x10-6
GA 4.5474 5.88 0.5225 0.020970 0.03250 5.683x10-5
AFO 4.003 4.77 0.32578 0.020029 0.045802 5.230x10-4
SEO 3.683 4.42 0.28819 0.018974 0.05532 4.676x10-4
SHO 3.545 4.1 0.22923 0.017646 0.06853 4.350x10-4
Proposed Model 2.1731 2.893 0.16273 0.011362 0.026290 3.2242x10-4

j_jsiot-2024-0012_utab_001

Step Pseudo-Code for the Proposed Scroll-based HBO Routing Algorithm
1 Yi - Initialize the feasible paths list
2 Identify the best search path
3 Initialize the chaotic honey badgers (representing the wearable nodes)
4 Evaluate the fitness function for each search path using Equation (16)
5 Yi = Update the best search path
6 Cv = Cluster of solutions reaching close to the optimal solution
7 While (N, number of iterations) do:
8 For each individual search path do:
9 Update the current path based on the principles outlined in Algorithm-2
10 End for
11 Update the values of parameters p, e, T, o, and r
12 Check for the best path identified so far
13 Recalculate the fitness function for each search path using Equation (16)
14 If Bf(i)≤Wf
15 Update the step factor C2
16 Calculate the new step size Xstep
17 Update the new chaotic position of the i-th honey badger using Equation (2)
18 Check the boundaries of the new position and calculate its fitness value
19 End If
20 End while

Fitness Metrics Across Optimization Models

Algorithm Best Worst Mean Median SD Variance
PSO 81 65.3 0.6893 0.02321 0.02929 2.734x10-6
ACO 85 66.2 0.6429 0.02292 0.03289 3.39x10-6
GA 82 62.45 0.52403 0.02190 0.04390 4.573x10-5
AFO 78 61.32 0.5689 0.02089 0.05609 4.393x10-4
SEO 78 66.89 0.6420 0.01889 0.06109 4.2503x10-4
SHO 80 72.34 0.6335 0.01799 0.06773 3.863x10-4
Proposed Model 100 97.8 0.8497 0.01432 0.02023 2.602x10-4

Friedman Test Evaluation

Algorithms P-test values
PSO 13
ACO 11.6
GA 10.2
AFO 3.1
SEO 2.5
SHO 2.2
Proposed Approach 1.052
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