Enhancement in Reliability of IEEE 802.15.4 WBAN Using Greedy Spider Monkey Algorithm

Abstract

Wireless Body Area Networks (WBANs) hold immense potential in healthcare monitoring, but ensuring reliable data transmission is crucial. While the IEEE 802.15.4 standard offers low-power operation and basic security, its Contention-Based Access (CBA) mechanism can lead to packet collisions and reduced reliability, especially in congested scenarios. This paper proposes a novel approach to enhance reliability in 802.15.4 WBANs by incorporating a Greedy Spider Monkey Optimization Algorithm (GSMA). The GSMA mimics the intelligent foraging behaviour of spider monkeys, enabling dynamic channel selection and optimal transmission scheduling. Our approach aims to minimize packet collisions by selecting channels with lower traffic based on historical data and real-time network conditions; the GSMA reduces the likelihood of collisions and data loss. Secondly, the algorithm prioritizes critical data packets based on their urgency and channel availability, ensuring the timely delivery of essential medical information. Finally, by reducing collisions and optimizing scheduling, the GSMA helps maintain efficient data flow within the WBAN. This paper presents the design and implementation of the GSMA-based approach within the 802.15.4 framework. Simulation results are presented to evaluate the effectiveness of the proposed method in improving reliability, reducing packet loss, and enhancing overall network performance in WBANs. The findings demonstrate the potential of the GSMA to address the limitations of 802.15.4 and contribute to developing more reliable and efficient WBAN solutions for healthcare applications.

Introduction

Including Guaranteed Time Slots (GTS) in Wireless Body Area Networks (WBANs) enhances the dependability and effectiveness of these networks, rendering them appropriate for a diverse array of healthcare and medical implementations. These applications encompass remote monitoring, telemedicine, and patient care. Wireless Body Area Networks (WBANs) are wireless networks specifically developed to establish connections among wearable and implantable medical devices, sensors, and other personal health monitoring equipment on or within the human body. These networks serve a multitude of purposes in the field of healthcare and monitoring. Implementing Guaranteed Time Slots (GTS) in Wireless Body Area Networks (WBANs) enhances the dependability and predictability of communication among devices while mitigating the probability of interference or collisions in data transfers. Every device within the network is allocated a distinct time slot during which it is permitted to transmit or receive data. A central coordinator, responsible for the scheduling and organization of communication between devices, commonly oversees the management of Generalized Token Systems (GTS).

Wireless Body Area Networks (WBANs) possess the capacity to fundamentally transform the healthcare landscape through the facilitation of enhanced and individualized health monitoring, the mitigation of healthcare expenditures, and the enhancement of patient outcomes. Nevertheless, utilising these technologies presents certain obstacles that must be overcome to achieve general acceptance. These hurdles encompass interoperability, data security, and regulatory compliance, necessitating careful attention and resolution. Short-range wireless communication technologies such as Bluetooth, Zigbee, or Ultra-Wideband (UWB) are commonly employed in Wireless Body Area Networks (WBANs) to establish connections between different devices. These technologies facilitate data transfer across limited distances, reducing interference and power consumption. Incorporating sensors into these devices enables the measurement of several physiological characteristics, including heart rate, body temperature, blood pressure, glucose levels, and other metrics. The aforementioned sensors offer instantaneous data for health monitoring purposes. Data collection and aggregation involves gathering data from sensors, which is subsequently combined and analyzed, typically on a wearable device. The aggregated data is then communicated to a central hub or a medical facility for additional examination and analysis. The preservation of security and privacy is of utmost importance in Wireless Body Area Networks (WBANs) due to the delicate nature of health data. These procedures are crucial for safeguarding patient information and maintaining the authenticity and reliability of the data.

IEEE 802.15.4 is a widely used standard for wireless body area networks (WBANs) and low-rate wireless personal area networks (LR-WPANs). IEEE 802.15.4 is a set of specifications ideal for WBAN applications since they deal with low-power, low-data-rate wireless communication. Media access control (MAC) and physical (PHY) layers are defined by IEEE 802.15.4 for low-rate wireless communication. The MAC layer offers channel access, addressing, and frame management methods, whereas the PHY layer establishes modulation and data rate. IEEE 802.15.4’s emphasis on low power consumption is one of its main benefits. Because wearable and implantable devices frequently run on batteries and must conserve energy to extend their lifespan, this is crucial for WBAN applications. IEEE 802.15.4 is intended for applications requiring comparatively modest data rates. Transmitting physiological and health-related data, which doesn’t require high data rates, is the main focus of WBANs. This is consistent with the standard’s low-data-rate capabilities. WBANs are suitable for the short-range communication that the standard provides. It reduces interference and aids in preserving the confidentiality and security of data transferred inside a local area network. These frequency ranges enable deployment flexibility and compliance with local spectrum laws. Mesh networking is supported by IEEE 802.15.4, which makes WBAN communication more reliable and scalable. Because every device in a mesh network can relay data to other devices, the network’s dependability is increased. IEEE $802.15.4^{\prime }$s 2.4 GHz version is compatible with Bluetooth and Wi-Fi, among other wireless standards, making it appropriate for use in settings where several wireless technologies are present.

Based on how spider monkeys in the rainforest forage for food, the Spider Monkey Optimization Method (SMO) is an optimization method inspired by nature. It belongs to the larger class of swarm intelligence and evolutionary algorithms and is a meta-heuristic algorithm used to solve complicated optimization issues. SMO explores the solution space by utilizing a population of candidate solutions. A spider monkey is a common term used to describe each potential option. Within the framework of the algorithm, spider monkeys exchange information regarding where good solutions are located. This resembles how spider monkeys interact with one another in the wild. SMO solves issues when a goal function has to be either maximized or minimized. The algorithm aims to determine which combination of variables or parameters minimizes or maximizes this objective function. There is an iterative operation in the algorithm. Spider monkeys search the solution space, communicate with one another, and adjust their placements according to the quality of solutions discovered in each iteration. As most optimisation algorithms do, SMO incorporates termination criteria to specify when to end the search. A convergence threshold, a maximum number of iterations, or other variables.

Data privacy in Wireless Body Area Networks (WBANs) is paramount due to the sensitive and personal nature of the health-related data these networks typically handle. WBANs monitor physiological parameters, collect health-related information, and facilitate communication between wearable devices and medical infrastructure.

Goal of the Article

Our article have the following goals to make more reliable in the WBAN:

1. Design a novel optimized algorithm for enhancing reliability in the WBAN with IEEE 802.15.4.

2. Enhancing packet delivery rate and minimizing the number of packet losses.

3. optimizing the nearest neighbour finding using the spider monkey algorithm for the fast WBAN node finding for personal devices.

4. Designing simulation with OmNeT++ and optimizing the results using Python.

Further structure of the paper is as follows: literature review is described in the Sect. 2. Problem definition and solution formation is given in the Sect. 3. In the Sect. 4, experimenatl setup and results of the proposed system is exxplainhned. In the Sect. 5, comparision with the proposed system and state-of-the-art system is given. Auditiing and error failure state is descriubed in Sect. 6. Conclusion and futiure scope is given in Sect. 7.

Literature Review

In Hemavathi and Latha [4], aims to further improve the network’s quality of service (QoS) by identifying a set of optimal paths for packet routing and assessing each path’s dependability using a variety of metrics (packet loss rate, residual energy, end-to-end delay, link stability, and hop count). stage introduces the Hybrid Fuzzy Levy Flight Particle Swarm Optimization Algorithm (HFLPSO). In Gonzalez et al. [7], displays the findings of this kind of comparison on networks that are physically deployed utilizing the FIT-IoTLab’s resources. A complete implementation of a MQTT-based IIoT protocol stack in Contiki-NG is part of the assessment. It consists of integrating OpenDSME, the only DSME implementation that is made accessible to the public, into Contiki-NG’s software stack. In Hajam and Sofi [3], proposed greedy task scheduling SMO (gTSSMO) and semi-greedy task scheduling SMO (sgTS-SMO) for efficient task scheduling in fog computing environment. The main aim is minimize delay and energy consumption while considering the constraints of deadline and violation time. Mkongwa et al. [8], proposed performance improvement for coexisting WBANs through transmission prioritization and adaptive channel access mechanisms. We categorize user data into classes and implement transmission prioritization scheme that considers data category, device synchronization, dynamic clear channel assessment (DCCA), backoff range adaptation, and packet retransmission trials in stationary and mobile networks. Haggag et al. [2], applied IPv6 to IoT networks to avoid the scalability bottleneck of the IPv4 subnet. IPv6 accommodates a large number of connected devices and solves issues resulting from the heterogeneous nature and access methods of IoT devices. However, IPv6 is a large protocol that does not suit itself well in the IoT world. Qiu et al. [10], presented a reliable and efficient IPv6 packet broadcast protocol for IEEE 802.15.4-based WPSNs, named 6WPSN. First, the operations of the 6WPSN are given, in which linear network coding is applied to enhance the reliability of weak links in WPSNs. Then the performance analysis model is established that combines with radio frequency (RF) energy supply and linear network coding.

Soni et al. [12], proposed a novel and effective means of GTS allocation for WBANs, called Dynamic Distribution of GTS Scheme (DDGS) protocol in which the contending nodes are granted GTS slots on a priority basis purely decided by the real-time nature of the traffic in the sensor field. Through extensive simulations, we show that our proposed DDGS protocol significantly outperforms the static allocation of the limited and available contention free slots in respect of bandwidth utilization, network throughput and end-to-end latency without consuming significant energy of sensor nodes. Muthuvel et al. [9] presented, issues related to security in WBAN in separate networks as well as in multiple networks. For WBAN working in a separate network, the IEEE 802.15.6 standard is considered. For WBANs working in multiple networks, especially heterogeneous networks, the security issues are considered. Yaghoubi et al. [13], examined body sensor network design and functionalities, communication technologies, WBAN, and security issues. The work presented in this paper looks into a serious security-level issue with WBAN. Finally, it outlines a number of methods for boosting security and lowering energy usage. Zhu et al. [14] presents the CSMA-CA mechanism was modeled using a novel Markov chain, which produced the expected number of backoff periods, expected number of backoffs when a node intended to send a packet, and expected number of failures in acquiring the channel. CCE was the consequence of these statistics. An optimization problem was developed that, in relation to the three crucial criteria listed above, maximized the CCE. In [5], the suggested approach, the cluster head transmits a beacon frame with data on the allotted channels and time slots. The new node can now identify its channel and timeslot thanks to this. According to a performance investigation, when compared to the well-known IEEE 802.15.4 MAC protocols that are frequently used in the literature to provide quality of service (QoS) to smart-grid applications, the suggested approach can achieve low end-to-end delays and low collision rates. Kumar et al. [6], The proposed model comprised of five sensor nodes along with a coordinator node. Sensor nodes include electrocardiography (ECG), Blood Oxygen Saturation (SpO2), Blood Pressure, Body Temperature and Body motion. A number of protocols are being proposed on the different layers of WBAN. This paper provides guidance for simulation of various scenarios and characteristics of Wireless Body Area Networks (WBANs) and also furnishes an assessment of quality metrics using Castalia 3.2 Simulator with OMNET++ framework. Roy et al. [11] presented modeled as a Markov Decision Process. There is a need to adapt to the changing ambient conditions through exploration and exploitation [1].

Proposed System

In this section the WBAN with the reliability issues are presented with possible novel solution with spider monkey optimization algorithm. This part is further devied in the four parts as follows:

• Design a novel GSMO optimized algorithm for enhancing reliability in the WBAN with IEEE 802.15.4.

• Enhancing packet delivery rate and minimizing the number of packet loss.

• optimizing the nearest neigbour finidning using spider monekey algorithm for the fast WBAN node finding for personal devices.

• Designing simulation with OmNeT++ and optimizing of the results using Python.

• Auditing of the proposed system againt different failures and error states.

Design of WBAN

The first step of the WBAN formation includes the list of the sensors to be deployed in the body of the patient. It is based on the requirements and recommendation s of the doctor. Proposed design of the WBAN is shown in the Fig. 1. All the notations used in this article is given in the Table 1. Th sensors $S{n}_i$ is deployed in the body of the patient $P_x$ at location $G{p}_y$. Since the sensors have the low power and low memory capacity to compute large data, the mobile devices are used to be intermediate between the cloud and the body sensors. The architecture in the figure 1 shows the reliable connection between the sensors, mobile devices and the cloud to collect and compute data for the patient. Generated report is saved and shared with the patient, caretaker and doctors.

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Fig. 1

Overall design of the proposed system

Table 1. List of notations in this article

S.n	Notation	Details
1	$S_n$	Sensors
2	$P_x$	Patient
3	$G_p$	Geo-location
4	PDA	Personal digital assistance
5	$Rat{e}_p$	Packet delivery Rate
6	SP	Search space
7	FF()	Fitness function
8	CD	Cloud storage

Design of Novel GSMO Optimized Algorithm

According to spider monkey algorithm, the detection of the nodes are inspired by the social behavior of spider monkeys. The algorithm have the following terms:

1. Fission: The population divides into smaller sub-groups, allowing for focused exploration.

2. Leader Learning: Each sub-group identifies the monkey with the best fitness (the leader).

3. Local Movement: Monkeys update their positions based on the leader’s location and their own exploration tendencies.

4. Fusion: Sub-groups occasionally merge, sharing information and potentially leading to better solutions.

5. Global Leader Update: The overall best monkey’s position is periodically updated, guiding the entire population towards promising areas.

The pseudo code of the algorithm is shown in the Algorithm 1

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Algorithm 1

The pseudocode for GSMO algorithm

In our case we are going to to deploy the sensor nodes as monkey in the body of the patient and going to find the best signal strength node in order to be leader of the group. The total nodes deployed in the body are forming a group. So strong signal nodes transmits the signal from all other deployed nodes. This leader node sends signal to the PDA devices and the PDA devices send to the cloud. For reliability between the deployed nodes and cloud the GSMO algorithm tries to select best devices each time of memory synchronization. So GSMO algorithm with our proposed system is explains in the Algorithm 2.

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Algorithm 2

GSMO with proposed solution

Formulation of the node strength measure:

1

$$\begin{array}{c}\hfill S_{N_i}=\sum _{i=0}^n{N}_i(BeaconTransferAccuracy/Second)\end{array}$$

Enhancing Packet Delivery Rate and Minimizing Packet Loss

Before applying our solution the packet delivery is formulated as:

2

$$\begin{array}{c}\hfill Rat{e}_P=Numberofpacketriived/packetsend\end{array}$$After applying the proposed solution packet delivery rate is increased. We can see the results in the Sect. 2

Enhancing the GSMO Algorithm with Energy Optimization

To integrate energy efficiency into GSMO, the fitness function is modified as follows:

3

$$\begin{array}{cc}& Fitness=\alpha \times Reliability+\beta \times \hfill \\ \hfill & \left(1,-,\frac{Energy\_Consumption}{Max\_Energy\_Consumption}\right),\hfill \end{array}$$where:

• Reliability: Measured as metrics like packet delivery ratio or bit error rate (BER).

• Energy_Consumption: Energy used by nodes in a specific cycle.

• Max_Energy_Consumption: Maximum allowable energy for a node.

• $\alpha ,\beta$: Weight parameters to balance the trade-off between reliability and energy efficiency.

Energy Optimization in GSMO

Dynamic node prioritization is implemented to extend the operational lifespan of Wireless Body Area Networks (WBANs). Nodes with higher residual energy are prioritized for data transmission tasks, while nodes below a predefined energy threshold are deprioritized. This strategy ensures prolonged network functionality by conserving energy in critical nodes.

Energy-aware channel selection is another enhancement, where channels are selected based on minimal interference to reduce retransmissions, thereby conserving energy. Additionally, adaptive allocation of channels is employed, considering node energy levels to optimize communication efficiency.

Energy constraints are integrated into the optimization process. Nodes with energy below a specific threshold contribute less to the solution space, while iteration parameters are adjusted dynamically to ensure energy-efficient operations. This approach balances the algorithm’s performance with the energy limitations of WBAN nodes.

Finally, simulation adjustments are recommended to evaluate the enhanced GSMO. Metrics such as average energy consumption, network lifetime (measured as the time until the first node dies), and packet delivery ratio under low-energy conditions should be incorporated. These metrics comprehensively understand the algorithm’s effectiveness in real-world scenarios. This proposed algorithm is shown in Algorithm 3.

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Algorithm 3

Enhanced GSMO with Energy Optimization

Security Analysis of Proposed Algorithm

Potential Security Vulnerabilities in WBANs

Wireless Body Area Networks (WBANs) are vulnerable to several security threats due to their reliance on wireless communication and the sensitivity of the data they handle. Key vulnerabilities include:

Potential Security Vulnerabilities in WBANs

Wireless Body Area Networks (WBANs) are vulnerable to several security threats due to their reliance on wireless communication and the sensitivity of the data they handle. Key vulnerabilities include eavesdropping, where data transmitted between sensors and the central node can be intercepted by malicious actors, which is particularly concerning for sensitive health data. Another critical issue is data tampering, where attackers can alter transmitted data, potentially leading to incorrect diagnoses or medical responses. Unauthorized access poses a significant risk as weak authentication mechanisms may allow intruders to compromise the network, undermining privacy and data integrity. Denial of Service (DoS) attacks, which flood the network with malicious traffic, can cause delays or failures in critical data transmission. Furthermore, node compromise, resulting from physical tampering or hacking, may expose encryption keys and other sensitive information. Lastly, resource exhaustion caused by repeated malicious requests can deplete the battery life of sensors, thereby disrupting WBAN operations.

Integrating GSMA with Security Protocols

The Greedy Spider Monkey Algorithm (GSMA), known for its optimization capabilities, can be effectively integrated with security protocols to enhance data privacy and integrity in WBANs. GSMA can optimize encryption key generation and distribution dynamically, ensuring strong and adaptive cryptographic mechanisms without significant computational overhead. Additionally, it can perform multi-objective optimization to balance energy efficiency and robust security measures, preventing sensor power exhaustion due to intensive security operations. GSMA can also optimize intrusion detection by identifying optimal thresholds for anomaly detection, thereby reducing false positives while preserving network integrity.

In terms of authentication, GSMA can optimize lightweight protocols by selecting energy-efficient and secure mechanisms, making them suitable for WBAN nodes. Moreover, it can improve data aggregation security by optimizing techniques that minimize the exposure of raw data during transmission while ensuring integrity. Lastly, GSMA can enhance dynamic channel allocation by adaptively selecting secure and interference-free channels, reducing the risk of eavesdropping and jamming attacks.

Detailed Comparison of GSMA with Leading Algorithms

Dynamic node prioritization is implemented to extend the operational lifespan of Wireless Body Area Networks (WBANs). Nodes with higher residual energy are prioritized for data transmission tasks, while nodes below a predefined energy threshold are deprioritized. This strategy ensures prolonged network functionality by conserving energy in critical nodes.

Energy-aware channel selection is another enhancement, where channels are selected based on minimal interference to reduce retransmissions, thereby conserving energy. Additionally, adaptive allocation of channels is employed, considering node energy levels to optimize communication efficiency.

Energy constraints are integrated into the optimization process. Nodes with energy below a specific threshold contribute less to the solution space, while iteration parameters are adjusted dynamically to ensure energy-efficient operations. This approach balances the algorithm’s performance with the energy limitations of WBAN nodes.

Finally, simulation adjustments are recommended to evaluate the enhanced GSMO. Metrics such as average energy consumption, network lifetime (measured as the time until the first node dies), and packet delivery ratio under low-energy conditions should be incorporated. These metrics comprehensively understand the algorithm’s effectiveness in real-world scenarios. These comparisons are given in the Table 2.

Table 2. Comparison of GSMA with leading algorithms

Feature	GSMA	ACO	PSO	Adaptive mechanisms
Optimization goal	Reliability	Path optimization	Global search	Reliability/energy
Energy efficiency	Limited consideration	Low	Moderate	Moderate to high
Computational overhead	Low	High	Moderate	Moderate to high
Real-time adaptability	High	Moderate	Low to moderate	High
Scalability	Moderate	Moderate	High	Moderate

Experimental Setup and Results

The system requirement of the expermental setup is given in the Table 3. OmNet++ network environment setup is given in the Table 4. From this table we can see that the area of the placement of the sensors are less. So the GSMO applied on the OmNet++ model and we find the leader foe the data sending to the PDA and cloud server. Checking the beacons before applying our proposed model and recording the event log along with data rate and error rate. After applying the model again event log is recorded with the other parameters. The result part is discussed in subsection.

Table 3. System remarks for experimental setup

S.n	System remarks	Property
1	Processor	intel i7 12th Gen with 4GB Nvidia Graphics, 16GB RAM
2	OS	Windows 11 64-bit
3	Simulator	OmNet++ Version 6.0.3
4	Protocol	IEEE 802.15.4
5	Analysis	Python 3.12

Table 4. OmNet++ Network Parameter Setup for GSMAO

Simulation parameters	Parameter values
Simulation time	55 s
Sensor node filed size	5$·$5 $m^2$
Transaction output power	-5dBm
Application packet rate	10 to 80 packets/second
Network topology	Star Topology
Synchronization mode	Beacon enabled
Carrier frequency	2.4 GHz
Carrier sense sensitivity	85 dBm
Transmission range	10 m
Number of sensor nodes	5 end devices
Frame size	127 bytes (default)
Transmission rate	250 Kbps

Experimental Results

Experimental results are recorded from the simulation of the proposed solution consist the data rate, beacons transmitted and received, bit error rate and latency. Since the position of sensors in WAN are fixed in body but the person can move, so in our proposed system the sensors are moving along together.

Data Rate

From the experiment we found the data rate for the fit node is 117 b/s. The histogram data rate for the the same node is 116 b/s. In the Fig. 2, we can see the scalar results of packet count for various simulation parameters in the experiment. Since after applying the GSMO algorithm the selection of fit node is done as leader node, so the packet rate of the node is increased.

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Fig. 2

Plotting of scalar parameters in the data rate

Bit Error Rate

The bit error rate average is 122 and the histogram bit error rate is 121. Plot for the bit error count is shown in the Fig. 3.

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Fig. 3

Plotting of scalar parameters in the BER

Latency

The Latency of the nodes in the experiment is 1432. Packet received is plotted in Fig. 4, state count is plotted in the Fig. 5 and packet statistics are plotted in the Fig. 6.

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Fig. 4

Packet receiving

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Fig. 5

State count

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Fig. 6

Latency of the nodes

The simulation parameters, including topology, transmission power, and packet rates, were carefully chosen to reflect real-world Wireless Body Area Network (WBAN) scenarios and constraints. The star topology was selected as it mirrors the typical WBAN structure, where sensors communicate with a central hub, such as a personal device or gateway. This design supports efficient communication with minimal latency, which is critical for medical applications like real-time monitoring and emergency alerts. Furthermore, the simplicity of the star topology reduces energy consumption and computational overhead, making it highly suitable for resource-constrained environments. The transmission power was set to -5 dBm to balance energy efficiency with communication reliability. This low power level ensures minimal interference with other devices operating in the 2.4 GHz band while maintaining sufficient range for intra-body communications, which is vital for the energy-constrained nature of WBAN nodes. Packet rates ranging from 10 to 80 packets per second were incorporated to represent the diverse data demands of WBAN applications, from low-frequency health metrics like body temperature to high-frequency signals such as ECG. This range ensures the simulation evaluates the algorithm’s performance and adaptability under varying data loads.

These parameters ensure energy efficiency, reliability, and scalability, essential for real-world WBAN applications. Low transmission power and a simple topology help conserve energy and extend the operational lifespan of WBAN nodes, addressing the challenge of frequent battery replacements in medical devices. The chosen topology and dynamic packet rates support the reliable transmission of critical health data with minimal delays, ensuring the system’s robustness in scenarios such as continuous patient monitoring and emergency response systems. Furthermore, simulating realistic environmental conditions, such as interference in the 2.4 GHz band, validates the system’s adaptability across various WBAN use cases, including multi-patient monitoring in hospitals and home healthcare setups. This meticulous selection of parameters ensures that the proposed GSMA-based WBAN system is practical and effective in addressing real-world healthcare challenges.

The proposed Greedy Spider Monkey Optimization (GSMO) algorithm can be enhanced to meet the specific recommendations for Wireless Body Area Network (WBAN) optimization. First, the population size and iteration limit can be dynamically adjusted based on the number of active WBAN nodes and their communication frequency. This ensures energy conservation for networks with lower node counts by pruning unfit nodes during iterations. Furthermore, the fitness function can be updated to explicitly incorporate WBAN-specific metrics such as energy consumption, latency, and packet error rate. This modification would optimize the trade-off between energy efficiency and reliability, ensuring improved healthcare data delivery. Real-time channel and slot allocation can be improved by introducing prioritization mechanisms for critical medical data over general health monitoring data, ensuring the reliable delivery of life-critical information.

Integrating energy efficiency into GSMO can be further enhanced by incorporating penalties for high-energy-consuming nodes and rewarding energy-efficient transmission pathways. A weighted scoring system in the fitness function would address this need, extending the network’s operational lifespan. Adapting the algorithm for mesh network topologies would reduce dependency on a single central node and increase network reliability. This can be achieved by extending GSMO’s "Fusion" and "Leader Update" phases to include multi-hop communication scenarios.

Finally, the simulation setup can include real-world WBAN conditions such as body movement, varying interference levels, and heterogeneous data traffic. Simulations should evaluate parameters such as packet size, collision rates, retransmission metrics, and their impact on energy savings and reliability. These enhancements collectively ensure that GSMO is robust, energy-efficient, and well-suited for dynamic WBAN environments.

Comparisons

In this section, we compare our propose method with the previously developed methods. We also provided the accuracy of the proposed system in the terms of reliability and latency.

Network Life Time

Network life time of nodes are compared with the Hemvathi and Latha [4]. We can see from Fig. 7, network life time of the nodes of the proposed system is better than the network life time of Hemvathi and B. Latha. So our system is more reliable in terms of network life time as compare to the previously developed system.

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Fig. 7

Comparison of network life time

Energy Consumption

Energy consumption of nodes are compared with the Hemvathi and B. Latha [4]. We can see from Fig. 8, energy consumption of the nodes of the proposed system is less than from Hemvathi and B. Latha. So our system is more efficient in terms of energy consumption as compare to the previously developed system.

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Fig. 8

Comparison of energy consumption

Packet Delivery Ratio (PDR)

Packet delivery ration of the of network are compared with the Hemvathi and B. Latha [4]. We can see from Fig. 9, this ratio of the network of the proposed system is better from Hemvathi and B. Latha. So our system is more efficient in terms of packet delivery as compare to the previously developed system.

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Fig. 9

Comparison of packet delivery ratio

Auditing and Difficulties

Auditing of the proposed system consist the evaluation of the system against the real time hardware scenario. In the real time hardware implementation with the sensors and IoT devices, many factors like physical channel, wireless network, cloud speed etc can effect the energy consumption, authenticity, packet delivery ratio and the network efficiency. So, we need to perform real time network audit for said system in the future.

Limitation of the proposed system are, number of nodes are less to be deployed in the patient body. if the number of nodes are more, then we can get more results with the dataset generation by the proposed method.

Conclusion and Future Scopes

In the above proposed system, we evaluated the reliability of the WBAN using GSMA. This paper addresses the challenge of reliable data transmission in Wireless Body Area Networks (WBANs) that utilize the IEEE 802.15.4 standard. A Greedy Spider Monkey Optimization Algorithm (GSMA) is proposed to overcome limitations caused by the Contention-Based Access mechanism. The GSMA incorporates dynamic channel selection and prioritizes critical data packets to minimize collisions, reduce packet loss, and improve overall network performance. Simulation results are presented to validate the effectiveness of the GSMA in enhancing reliability and efficiency for WBANs in healthcare applications. This approach paves the way for more dependable WBAN solutions for critical medical monitoring.

The proposed research using the Greedy Spider Monkey Optimization Algorithm (GSMA) to enhance reliability in IEEE 802.15.4 WBANs shows promise. Here are some potential future works to build upon this foundation:

1. Real-world experimentation: The current study utilizes simulations. Validating the GSMA’s effectiveness in real-world WBAN deployments with actual medical sensors and varying data types would strengthen the research.

2. Multi-objective optimization: The GSMA prioritizes two objectives: minimizing collisions and prioritizing critical data. Future work could explore incorporating additional objectives like energy consumption optimization for longer sensor life.

3. Hybrid approaches: The GSMA can be further enhanced by combining it with other techniques for reliable data transmission in WBANs. This could involve integration with machine learning algorithms for dynamic channel allocation or error correction protocols for improved data integrity.

4. Security integration: The abstract acknowledges the importance of security but doesn’t explore how GSMA interacts with security mechanisms. Future research could investigate incorporating security measures within the GSMA framework to address data privacy and confidentiality concerns in WBANs.

5. Scalability evaluation: The current study might focus on a limited number of sensor nodes. Evaluating the GSMA’s performance with a larger and more scalable WBAN setup would be beneficial for practical applications.

6. Hardware implementation: Explore implementing the GSMA on low-power embedded systems typically used in WBAN sensor nodes. This would assess its feasibility for real-world resource-constrained WBAN devices.

7. Cross-WBAN interference: Investigate the impact of interference from nearby WBANs on the GSMA’s performance. Develop mechanisms within GSMA to mitigate such interference and ensure reliable data transmission.

Author Contributions

Umashankar Pandey: Conceptualization, Draft of Manuscript, Experiments and results analysis Saroj Kumar Chandra: Supervision, Investigates the findings Narendra Kumar Dewangan: Conceptualization, Draft of Manuscript, Experiments and results analysis All authors discussed the results and contributed to the final manuscript.

Funding

There is no funding available for this work.

Availability of data and materials

Data sharing not applicable to this article, but can be made available on request.

Declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics approval

Not Applicable.

Consent for publication

All authors give consent for the publication of identifiable details, which can include photograph(s) and/or videos and/or case history and/or details within the text (Material) to be published in this journal.

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