An intelligent data communication in IoT-based healthcare application using a novel optimized routing protocol

Abstract

Nowadays, Internet of Things (IoT) technology has compressed various aspects of day-by-day life, but it has a profound effect on the healthcare sector. Because the IoT role can play a significant part in the aids of IoT-based healthcare applications. Here, many healthcare data packets communicate through the IoT devices such as actuators, sensors, transceivers, etc. Here, vulnerabilities, attacks, and threats overflow for data on the IoT. Hence, addressing IoT-linked security and communication problems needs a robust communication solution. Therefore, this paper aims to develop a novel communication protocol named Whale-based Low-Power and Lossy Network Routing Protocol (RPL) routing protocol. Here, the RPL routing protocol enables IoT network communication with a suitable objective function. The proposed optimization fitness function initially selects the cluster head and chooses the communication path with the help of the RPL routing protocol. Moreover, the developed routing protocol is executed in the MATLAB platform, and simulation results are validated with conventional models. From the comparison, the proposed framework has attained a higher security range and packet delivery ratio.

Keywords

packet delivery ratio Internet of Things communication protocol routing protocol optimization fitness function

1 Introduction

In current years, the Internet of Things (IoT)-based healthcare industry has rapidly grown its significant revenue to contribute to employment.¹ IoT is the essential innovation that transfers information and services through numerous smart-based applications such as smart cities, smart homes, etc.² Moreover, IoT has more valuable medical care functions. Better healthcare function is the most challenging factor in increasing the overall population.³ In such cases, the Internet of Medical Things (IoMT), a subset of IoT, has contributed to a more widespread and improved healthcare system.⁴ Then, the device-to-device communication system can incorporate the IoMT technology to offer the finest interoperability system.

Furthermore, healthcare IoT (IoT) technology can be adapted as a medical gadget to create more effective analysis through the physician.⁵ According to the healthcare support framework, different middleware platforms are used to access the Global System Mobile module.⁶ Also, IoT-based healthcare structures can integrate various clinical gadgets with several IoT devices (Figure 1). These are incorporated with a healthcare gateway to manage the collected data.⁷

Figure 1.

Application-oriented healthcare IoT (HIoT).

Consequently, IoT networks provide a vast amount of heterogeneous communication between the devices to process the correspondent performance through the protocol.⁸ Due to the emerging application of IoT protocol, management has arisen based on current environmental conditions.⁹ Moreover, the multipath routing protocol has more efficiency in routing all communication channels and also improves the IoT network performance between source and destination.¹⁰ IoT devices can generate the specified data; it is more required to transmit this information from source to destination.¹¹ This performance is essential to address the protocol routing latency of the appropriate service quality and maintenance.¹² Modern technology can efficiently utilize a single way to back up the data for consistent durations.¹³ Here, internet protocol (I.P.)¹⁴ is employed to access the gateway for managing the healthcare data streaming between the sources to destination nodes.

Moreover, the modern message queuing protocol¹⁵ is an IoT-based protocol to deliver information through new technologies across space and time. Additionally, it allows I.P. systems to enable the communication and sharing of both legacy and new applications. Consequently, the service discovery protocol¹⁶ is one of the most potent and efficient protocols for managing the extensive scalability of resource-oriented applications.

In the past, several routing protocols have been developed such as deep-learning model,¹⁷ machine learning with hyena optimizer methodology,¹⁸ clustering-based Markov protocol,¹⁹ co-operative with self-scheduling protocol,²⁰ etc. These are the existing techniques proposed for different objectives and problems. But, those that are not applicable still have many drawbacks, such as poor communication and energy consumption. Therefore, this paper aims to develop a new optimization-based routing protocol to mitigate the issues.

The critical impact of the suggested methodology is given below:

Firstly, the IoT environment is established with numerous nodes utilizing the Low-Power and Lossy Network Routing Protocol (RPL) protocol.

Subsequently, a novel routing protocol, Whale-based RPL (WbRPL), is developed to determine transmission paths securely and efficiently. This protocol incorporates a fitness function derived from Whale optimization (W.O.) into the RPL routing framework.

The developed routing protocol identifies cluster nodes within the IoT network, enhancing its organizational structure and efficiency.

WbRPL ensures optimized data communication between source and destination nodes, leveraging the capabilities of the MATLAB platform for implementation.

Finally, performance is evaluated in terms of packet delivery ratio (PDR), throughput ratio, energy consumption, end-to-end delay, and security features.

This paper is structured as follows: Section 2 delves into related work concerning localization in wireless sensor networks (WSNs). Section 3 outlines the problem and system model. The methodology development process is elaborated in Section 4. Section 5 presents the obtained results, and Section 6 concludes with a discussion of the proposed work.

2 Related works

To improve the healthcare system in IoT, El Zouka and Mustafa²¹ have developed fuzzy-based neural networks. The proposed method has two main components: trust environment and authenticated physiological environment. This system can provide a secure and reliable transmission system. Moreover, the interference system is adapted as fuzzy logic to get the best medical advice from the corresponding physicians.

IoT technology is one of the most critical areas of modern technology, and it includes business, healthcare management, education, etc. In this context, healthcare management is implemented within an IoT framework, referred to as an IoT-based healthcare system. Moreover, e-health, e-medicine, and body area networks are further classified based on the intelligent health system. Here, Qureshi et al.²² have developed an energy-efficient protocol to avoid energy management problems. Also, the multihop protocol is used to forward the data from the source node to the coordinator node.

Security performance is the primary concern regarding vulnerability to attack (VoA) during the data transmission process. Previously developed methodologies only deal with intrusions between the sensor nodes. Therefore, Deebak and Fadi²³ have proposed an optimized link state protocol to mitigate the VoA. Here, the set of monitoring nodes is equipped with an IoT environment to build an efficient security framework. Moreover, this monitoring routing protocol helps to prevent adversaries in generative adversarial networks.

Kumar et al.²⁴ have introduced a constrained-based application protocol to protect the data transfer channel. Based on the protocol functions, security, and energy management performance can be controlled and improved. Moreover, datagram transport layer features improve data integrity, authentication, and confidentiality measures. Moreover, the developed protocol can utilize 30.86% less energy than the telemetry transport protocol.

Arul et al.²⁵ developed an optimized data distribution to protect data access and control. Moreover, this proposed model effectively solves security and privacy requirements. Here, the blockchain module can be developed through the healthcare application to strongly authenticate healthcare providers. Consequently, this model attains a 92% prediction ratio, 95% accuracy, and 1.5% response time compared to other models.

Sun and Sun²⁶ have introduced a clustering-based hierarchical approach for establishing a healthcare patient monitoring system. Under this framework, a merged center is designated to select a leader for each cluster, who assumes responsibility for managing and coordinating activities within the cluster. These responsibilities include gathering information, organizing data, and facilitating communication with leading platforms. The objective of this organizational structure is to maintain a consistent load on the system and optimize the efficiency of support equipment. Our research shows that our approach not only decreases the energy consumption necessary to sustain the network under different configurations but also notably prolongs its operational lifespan. Specifically, the quality of delivery (QoD) achieved by our proposed method was measured at 4.9%, outperforming the QoD values of 7.45% for Distributed Energy-Efficient Clustering and 9.82% for Low-Energy Adaptive Clustering Hierarchy.

WSNs are integral in various sectors, such as military, healthcare, and defense, where efficient data transmission heavily relies on energy utilization primarily from batteries. However, traditional approaches often overlook critical factors during energy-aware routing processes. Addressing this gap, Ram and Ilavarasan²⁷ have introduced a novel multi-objective energy-efficient routing scheme named multi-objective May-Badger (M2B) optimized routing. This protocol prioritizes energy efficiency by considering node-to-node distance, distance from the base station, traffic rate, and node delay. The proposed method achieves a significant reduction in energy consumption, with only 0.09 mJ consumed by 100 nodes in the network. Additionally, it achieves a more excellent PDR of 96% for 1000 nodes, effectively extending the network's lifespan.

In recent years, the rapid expansion of the IoT has brought about a revolution in the healthcare industry, leading to the emergence of healthcare big data. Consequently, there is a pressing need to safeguard data from potential attacks to ensure secure transmission across networks. Goswami et al.²⁸ primarily explore healthcare data security within the IoT framework, utilizing the Artificial Shuffle Shepherd Integrated Jellyfish Optimization Digital Homomorphism Elgamal Algorithm (ASS-JFO-DHEA) model. This model integrates the innovative hybrid ASS-JFO algorithm with the DHEA encryption technique to enhance data security. The proposed approach demonstrates the most robust peak signal-to-noise ratio values, reaching 74 dB, underscoring its effectiveness in securing healthcare data within IoT environments (Table 1).

Table 1.
Research gap.

Sl. No. Author name Technique Cons

1 El Zouka and Mustafa²¹ Fuzzy-based neural networks Sharing the healthcare data without any external distance It provides various duplicate services in each network layer

2 Qureshi et al.²² Energy-efficient protocol This technique is a secure and scalable authentication model Vulnerable to malicious securities

3 Deebak and Fadi²³ Optimized link state protocol The finest privacy performance of crucial information Reduce flexibility and scalability

4 Kumar et al.²⁴ Constrained-based application protocol Third-party action cannot disturb the entire performance Lack of data privacy and confidentiality

5 Arul et al.²⁵ Secure data dissemination methodology Higher data integrity and improving network security Lack of scalability and key management

6 Sun and Sun²⁶ Clustering-based hierarchical approach Operational lifespan Reduced energy consumption

7 Ram and Ilavarasan²⁷ M2B Achieves a higher packet delivery ratio Latency

8 Goswami et al.²⁸ ASS-JFO-DHEA Enhance data security Enhance the credibility

Sl. No.	Author name	Technique		Cons
1	El Zouka and Mustafa²¹	Fuzzy-based neural networks	Sharing the healthcare data without any external distance	It provides various duplicate services in each network layer
2	Qureshi et al.²²	Energy-efficient protocol	This technique is a secure and scalable authentication model	Vulnerable to malicious securities
3	Deebak and Fadi²³	Optimized link state protocol	The finest privacy performance of crucial information	Reduce flexibility and scalability
4	Kumar et al.²⁴	Constrained-based application protocol	Third-party action cannot disturb the entire performance	Lack of data privacy and confidentiality
5	Arul et al.²⁵	Secure data dissemination methodology	Higher data integrity and improving network security	Lack of scalability and key management
6	Sun and Sun²⁶	Clustering-based hierarchical approach	Operational lifespan	Reduced energy consumption
7	Ram and Ilavarasan²⁷	M2B	Achieves a higher packet delivery ratio	Latency
8	Goswami et al.²⁸	ASS-JFO-DHEA	Enhance data security	Enhance the credibility

M2B: multi-objective May-Badger; ASS-JFO-DHEA: Artificial Shuffle Shepherd Integrated Jellyfish Optimization Digital Homomorphism Elgamal Algorithm.

3 System model and problem statement

In general, numerous healthcare applications are incorporated with the internet for IoT applications. Here, numerous IoT devices are associated with global information frameworks for their frequent development.²⁹ In Figure 2, the foundational system model for IoT-based healthcare applications through the network layer. The healthcare data are gathered and sent through the correspondent medical user. Also, the gateway functionalities handle the performance, such as data collection, analysis, and temporary storage.

Figure 2.

Basic system model.

For example, the IoT application can gather diverse information transferred to the correspondent data access user. This healthcare application requires higher security to protect the data from unwanted users. Consequently, IoT healthcare applications can transfer the information to other gadgets through any routing protocol. From that, the protocol may provide improper data transmission during the route selection. Therefore, dynamic routing topology is a tough challenge and is vulnerable as a security model.

4 Proposed methodology

In this present research, a novel, efficient WbRPL routing protocol is developed to improve communication in IoT-based healthcare applications. Initially, the IoT environment is created using numerous nodes with RPL protocol. Here, the developed WbRPL routing protocol can track the efficient transmission path for secure communication. In this W.O.,³⁰ the performance function is integrated into the RPL routing protocol.

4.1 W.O. algorithm (WOA) for RPL

The hunting strategies of humpback whales serve as an inspiration for the WOA,³¹ which is used to optimize routing decisions in IoT networks. This section explains the fundamentals of WOA and how it may be used to improve dynamic routing and network performance by integrating it with the RPL. WOA is divided into three primary stages: finding food, encircling the target, and swimming in a spiral motion to eat. These phases are designed to maximize routing metrics in RPL by imitating the predatory behavior of humpback whales.

Finding food

In this phase, whales search for optimal solutions by exploring their environment. Routing optimization for RPL involves identifying the best paths for data transmission. The algorithm updates the position of a whale (representing a potential routing path) based on its distance from a randomly chosen whale (another routing path). The position update is given in equation (1):

\begin{matrix} Optimal\_Pat h_{new} (t + 1) = Random\_Path (t) - A \cdot D_{search} \end{matrix}

(1)

\begin{matrix} D_{Research} = C \cdot {Random}_{Path (t)} - {Current}_{Path (t)} \end{matrix}

(2)

\begin{matrix} A = 2 a \cdot r - a \end{matrix}

(3)

\begin{matrix} a = 2 - 2 \cdot \frac{T}{T_{max}} \end{matrix}

(4)

Here ○

Random_Path(t) is the location of a whale that was chosen at random (routing path).

○

Current_Path(t) is the current routing path of the whale.

○

C is a coefficient vector ranging from 0 to 2.

○

The random number r ranges from 0 to 1.

○

a is the control parameter

○

t is the current iteration.

○

$T_{max}$ is the maximum number of iterations that can be made.

It is clear that as the number of iterations t increases, coefficient A's value decreases linearly from 2 to 0.

Encircling the target

In this phase, whales refine their search around the best solution identified so far. For RPL, this means optimizing routing paths closer to the best path discovered. The best path update is calculated using equation (5):

\begin{matrix} Pat h_{new} (t + 1) = Pat h_{best} (t) - A \cdot D_{encircle} \end{matrix}

(5)

\begin{matrix} D_{encircle} = C \cdot Pat h_{best} (t) - Path (t) \end{matrix}

(6)

Here

○

$Pat h_{best} (t)$ is the best-performing routing path identified.

○

$D_{encircle}$ is the distance between the current routing path and the best path.

Swimming in a spiral motion

In this phase, whales move in a spiral pattern to approach the best solution. For RPL, this represents exploring alternative routes while converging on the optimal path. The optimal path is found using equation (7):

\begin{matrix} Optima l_{Path_{new}} (t + 1) = Bes t_{Path (t)} + D_{spiral} \cdot e^{b l} . \cos (2 π l) \end{matrix}

(7)

\begin{matrix} D_{spiral} = Best\_Path (t) - Current\_Path (t) \end{matrix}

(8)

Here,

○

$D_{spiral}$ is the distance that separates the optimal path from the present routing path.

○

A constant coefficient called b determines how the logarithmic spiral is shaped.

○

The random number l ranges from 0 to 1.

WOA is integrated with RPL to enhance routing efficiency by optimizing path selection. The algorithm dynamically adjusts routing paths based on network conditions, improving performance metrics such as latency and energy consumption. By incorporating WOA, RPL can better address challenges related to network topology changes and data transmission reliability. Subsequently, the developed routing protocol locates the cluster node in the created IoT environment. Here, the MATLAB platform is used to make the WbRPL routing protocol, which offers the best data connection between the source and the destination. Additionally, Figure 3 shows the suggested architecture in block diagram form.

Figure 3.

Proposed methodology.

Figure 4.

Flowchart of the proposed method.

4.2 Process of proposed methodology

In an IoT environment, most nodes are sensors equipped with diverse functionalities, and they serve various applications, including laptops, personal computers, and mobile phones. In this study, IoT devices are randomly organized, and an optimization algorithm is integrated with the RPL routing protocol to identify optimal nodes for data communication and transmission along the selected path. Furthermore, the proposed protocol differs from the existing one in that it incorporates multiple functions to achieve specific tasks.

At this point, the optimization fitness function is used to guide the cluster node selection process. Here, the RPL routing protocol is integrated with W.O. to choose the node as the cluster node. Moreover, the communication process occurs among the IoT nodes and base station, also referred to as the cluster node. Routing performance is more important to broadcast healthcare data securely. Here, the created IoT sensor nodes are communicated directly with the help of cluster nodes. Selecting the optimal path data transmission can decrease the transmission time from cluster node to base station. During the setup phase, a cluster node is selected based on a fitness function that considers energy efficiency and lifespan. This selected node can create the routing performance in the fitness function.

In this model, the transmission path and cluster node are selected in the created IoT environment based on the fitness function. Initially, create the IoT network in the IoT environment that contains various nodes, such as unknown nodes and known nodes, using equation (9):

G (i)_{IoT} (i = 1, 2, 3, \dots)

(9)

In the optimization strategy, the group of whales is considered analogous to several IoT nodes present in the environment during the corresponding search phase. Accordingly, the IoT node with the lowest distance and maximum energy is termed a cluster node to transfer the information from the sink to the base station in the finest way. Here, the fitness value is attained with less delay and minimum energy so, the fitness function is calculated using equations (10), (11), and (12):

\begin{aligned} F_{f} & = \frac{1}{2} [j_{i} + (1 - l)] \end{aligned}

(10)

\begin{aligned} E_{c} & = \frac{1}{p \times q} \sum_{n = 1}^{p} \sum_{m}^{q} J_{p q} \end{aligned}

(11)

\begin{aligned} L_{IoT} & = \frac{1}{p \times q \times λ} \sum_{n = 1}^{p} \sum_{m}^{q} D_{p q} \end{aligned}

(12)

where

F_{f}

E_{c}

L_{IoT}

, and

λ

denote the fitness function, energy consumption, and delay performance, respectively. After finding the fitness square, energy, and delay find the energy model of the entire network. The network has an initial energy

j_{i}^{'}

allocation, assuming IoT nodes are not rechargeable based on their capabilities. Therefore, the data are transmitted kth time from the first node to the node that has lost energy at the receiver end. So, the RPL routing protocol is used as an energy dissipation module. Moreover, each data packet has a size denoted as

U_{d}

that is summarized by the distance and nature of the node. Therefore, the energy dissipation of each node while transmitting

U_{d}

data, as given by equation (13), is

J_{dis} (x^{k}, x^{l}) = \sqrt{\frac{J_{t}}{J_{a}}}

(13)

where

J_{t}

is the energy required to transmit the data that means transmitted energy,

J_{a}

is the aggregated energy of the source node. Moreover, in the IoT environment, the healthcare data transmitted from every IoT node to base stations is done with the help of the RPL routing mechanism. However, the energy is dissipated by the receiver end after getting the data, so the cluster node gets updated based on the following equation (14):

J_{e} (C_{n}) = J_{d} \times U_{d}

(14)

where

J_{e}

is the updated energy level after the transmission process, energy

J_{d}

is denoted as dissipation energy, transmitted data size. Then, whales circling behavior is tuned as distance finding each IoT node from the IoT environment. Here, each whale is denoted as a search agent but the proposed method search agent is tuned as an identification element of each parameter such as lifetime, and throughput. After finishing the energy setup, update the position of each node using equation (15):

\vec{d} = [{\vec{c}}^{'} . {\vec{x}}^{'} * (t^{'}) - {\vec{x}}^{'} (t)]

(15)

where

{\vec{c}}^{'} = 2 * {\vec{r}}^{'}

, where

{\vec{r}}^{'}

denotes a random node among the 0 to –;

{\vec{x}}^{'} * (t^{'})

is the optimal position of each node and

{\vec{x}}^{'} (t^{'})

is the current position of each iteration. Then, the updated position of the IoT node can be determined using equation (16):

{\vec{x}}^{'} (t^{'} + 1) = {\vec{x}}^{'} * (t^{'}) - \vec{a} . \vec{d}

(16)

where

\vec{a} = 2 \cdot a^{'} \cdot {\vec{r}}^{'} - a^{'}

, where

\vec{a}

can be used to linearly decrease the position of each node while sending the data. This will help to communicate the best with every node in the IoT environment (Figure 4).

Figure 5.

Cluster formation.

Ethical considerations are pivotal in the design and implementation of location-based recommendation systems (LBRSs).

Privacy: LBRSs often collect and process user location data to provide personalized recommendations. It is essential to prioritize user privacy and ensure that location information is handled securely and meets data protection regulations.

Transparency: LBRSs should be transparent about how recommendations are generated and the factors influencing the recommendation process. Users should understand why specific recommendations are made to foster trust in the system.

Fairness: LBRSs should strive to provide fair and unbiased recommendations to all users. Developers should be mindful of potential biases in the recommendation algorithms and take steps to mitigate them to ensure equal treatment for all users.

Diversity: LBRSs should promote diversity in recommendations to offer users a wide range of options. Ensuring diversity in recommendations can help prevent filter bubbles and expose users to new and varied content.

User empowerment: LBRSs should empower users to control their recommendations and provide options to adjust preferences, filters, and privacy settings. Giving users control over their recommendations can enhance user trust and satisfaction.

Security: LBRSs should prioritize protecting user data and system infrastructure. By implementing robust security measures, they can prevent data breaches and unauthorized access to sensitive information.

LBRSs should prioritize the protection of user data and system infrastructure. Implementing robust security measures can help prevent data breaches and unauthorized access to sensitive information.

Accountability: LBRS developers should be accountable for the recommendations generated by their systems. Establishing mechanisms for accountability and oversight can help address any ethical concerns that may arise during system operation.

5 Result and discussion

The routing protocol is implemented using the MATLAB platform. Initially, 200 nodes are deployed to transmit healthcare data within the IoT environment. Cluster formation is depicted in Figure 5. Consequently, Figure 6 shows the data transmission process in IoT infrastructure, and Figure 7 shows the communication path from the sink to the base station.

Figure 6.

Data transmission over the Internet of Things (IoT) network.

Figure 7.

Communication path.

Figure 8.

Simulation outcome of energy consumption.

5.1 Performance estimation and comparison

The proposed work parameters are compared with existing protocols such as the hybrid bird swarm and W.O. (HBSaW) strategy,³¹ real-time-based secure route analysis (RbSRA) model,³² clustering multihop dynamic routing protocol (CbMRP),³³ reliable data transmission with energy-efficient routing protocol (RDT-ERP),³⁴ and application-based routing protocol (AbRP).³⁵

5.1.1 Energy consumption

The total energy required by all IoT nodes to transmit healthcare data via the selected node in the proposed routing protocol is determined. The energy consumption of the proposed methodology can calculated using equation (9):

E^{'} (T) = E^{'} (T^{'}) - (E_{t}^{'}, - E_{r}^{'})

(17)

where the total consumed energy of each node is represented as

E^{'} (T)

, then the received and transmitted energy is denoted as

E_{t}^{'}, E_{r}^{'}

, respectively. Let us assume that each IoT node initially consumes 4 mJ of energy. The proposed protocol has utilized less energy to broadcast the data packets from the source node to the destination node (Figure 8).

Several existing protocols for secure energy networks include HBSaW, RbSRA, CbMRP, reliable data transmission with energy-efficient routing protocol (RDT-ERP), and AbRP. Compared to conventional models the developed WbRPL has utilized low energy of 0.002369, while HBSaW, RbSRA, CbMRP, RDT-ERP, and AbRP all have consumed energy 9.48, 8.452, 0.18, 12.8 and 18, respectively.

Figure 9 shows the energy consumption of the varying number of nodes and illustrates the energy consumption outcomes of the suggested WbRPL routing protocol compared to conventional techniques.

Figure 9.

Comparison of energy consumption.

Figure 10.

Simulation outcome of packet delivery ratio.

5.1.2 Packet delivery ratio (PDR)

It is distinct as the ratio of entire healthcare data broadcasted to the total number of received healthcare data with all IoT nodes.

Packet delivery ratio = \frac{D_{r}^{'} (n)}{D_{t}^{'} (n)} \times 100 %

(18)

where

D_{r}^{'} (n)

and

D_{t}^{'} (n)

represent the total number of healthcare data packets established by the destination node and the total amount of healthcare data packets transmitted by the source node.

Analysis of the importance of the projected routing protocol has been validated with existing protocols such as HBSaW, RbSRA, CbMRP, RDT-ERP, and AbRP. From this comparison, the planned protocol has a higher PDR and randomness in IoT sensor nodes.

Here, the proposed WbRPL protocol has exhibited an improved PDR, while the HBSaW, RbSRA, CbMRP, RDT-ERP, and AbRP protocols have achieved a greater PDR of 89%, 93%, 97%, and 91% and 78%, respectively. Figure 10 shows the PDR of the varying nodes. The PDR of the suggested WbRPL routing protocol with conventional techniques is illustrated in Figure 11.

Figure 11.

Comparison of packet delivery ratio.

5.1.3 End-to-end delay

End-to-end delay is the total time taken to transmit data packets from the source node to the destination nodes. On the other hand, the total delay occurs during communication between the receiver and all IoT nodes. Moreover, the delay depends on the transmission distance between the source and destination nodes.

Here, the proposed WbRPL strategy has a lower delay—while the HBSaW, RbSRA, CbMRP, RDT-ERP, and AbRP methods exhibited delays of 0.00372, 0.008, 0.025, 0.0620, and 0.091 ms, respectively. This performance demonstrates that the developed protocol is the finest solution for IoT in end-to-end delay without data drops.

Figure 12 shows the end-to-end delay of the changing number of nodes. Figure 13 illustrates the end-to-end delay of the proposed WbRPL routing protocol with conventional techniques.

Figure 12.

Simulation outcome of end-to-end delay.

Figure 13.

Comparison of end-to-end delay.

5.1.4 Throughput ratio

The network throughput ratio is the most significant consideration when verifying the effectiveness of the data packets near the destination. The energy level of each IoT network node is secured and regarded as the best route to decrease the network overhead.

Figure 14 shows the throughput of the varying number of nodes. Figure 15 presents the throughput ratio of the developed routing protocols and existing protocols such as HBSaW, RbSRA, CbMRP, RDT-ERP, and AbRP methods. Furthermore, throughput ratios are significantly improved compared to conventional techniques.

Figure 14.

Simulation outcome of throughput.

Figure 15.

Comparison of throughput.

The outcomes indicated that the number of healthcare data packets received at HBSaW, RbSRA, CbMRP, RDT-ERP, and AbRP is nearly 32.71 kbps, 20.97 kbps, 25.85 kbps, 33.242 kbps, and 45.9234 kbps, respectively. Many IoT applications prioritize dependability, focusing on throughput and packet transfer ratios.

5.1.5 Security

The security assessment of the developed WbRPL protocol demonstrates a heightened capability to protect healthcare data from various attacks and third parties. By working, the optimization fitness module has tracked the attacks throughout the entire network. Here, the man-in-a-middle attack can be detected and prevented with suitable parameters; it includes three elements: source node, destination node, and attacker. Initially, the source node that is selected cluster head transmits the data to the destination entity, which is the gateway. In this scenario, the attacker tries to disrupt communication between the two elements. The security of the proposed model's graphical representation is illustrated in Figure 16.

Figure 16.

Simulation outcome of security.

Figure 17 shows the performance comparison of the developed WbRPL routing protocol and existing models based on the delay results. From this comparison, the HBSaW model has attained a lower security measure (69.9%) than other models. Subsequently, the RDT-ERP model has shown optimum security measures (92%) over the HBSaW model with a slightly lower security measure (89%).

Figure 17.

Comparison performance of security.

Also, the AbRP model has achieved an average security measure (88%), which is marginally lower than the results offered by RbSRA (85%) and slightly under those of the CbMRP model (89%). However, the proposed WbRPL routing protocol exceeded all the previous replicas with a higher security measure on the selected IoT nodes.

The overall assessment of the proposed routing procedure has been validated with existing methods, which show that the developed model has greater security and poorer energy consumption, PDR, end-to-end delay, and throughput (Tables 2 and 3).

Table 2.

Comparison results of proposed with conventional models.

Techniques and	Packet transferring	Security	Throughput	End-to-end	Energy
performance	ratio (%)	(%)	(kbps)	delay (ms)	consumption (J)
HBSaW	89	69.9	32.71	0.00372 ms	9.48
RbSRA	93	85	20.97	0.008	8.452
CbMRP	97%	89	56.85	0.025	0.18
RDT-ERP	91	92	73.242	0.0620	12.8
AbRP	78	88	65.9234	0.091	18
Proposed	99.30132	98	34.82394	0.0030038	0.002369

HBSaW: hybrid bird swarm and whale optimization; RbSRA: real-time-based secure route analysis; CbMRP: clustering multihop dynamic routing protocol; RDT-ERP: reliable data transmission with energy-efficient routing protocol; AbRP: application-based routing protocol.

Table 3.

Overall results of the proposed WbRPL routing protocol.

Sl.	No. of	Throughput	End-to-end delay	PDR	Energy	Secure
No.	nodes	ratio (kbps)	performance (ms)	(%)	consumption (mJ)	range (%)
1	200	54.613359	0.038787	99.611407	0.00224	97
2	400	43.852436	0.034913	99.567023	0.002341	97
3	600	36.136227	0.033486	99.441713	0.002358	98
4	800	24.797466	0.022669	99.182265	0.002378	99
5	900	14.720195	0.020334	98.704171	0.002526	99

WbRPL: Whale-based Low-Power and Lossy Network Routing Protocol; PDR: packet delivery ratio.

5.1.6. Comparison of existing methods in terms of complexity

Figure 18 shows the comparison of existing methods in terms of complexity. Table 4 provides a comparative analysis of the complexity of existing methods across different numbers of nodes, ranging from 20 to 200. Each technique, including root mean square load balancing (RM-LB), optimized mean square load balancing (OMS-LB), software-defined networking with artificial intelligence (SDN-AI), and WbRPL(Pro), is evaluated based on its complexity score for each node count. Complexity scores generally increase with the number of nodes, indicating potentially greater computational demands as the network grows. For instance, while RM-LB starts with a complexity score of 0 for 20, it gradually increases to 45 for 200. Similarly, other methods show varying complexity trends as the number of nodes increases. Such comparisons provide insights into the computational efficiency and resource requirements of different routing methods across different network sizes, aiding in choosing appropriate techniques based on specific application requirements and scalability concerns.

Figure 18.

Comparison of existing method in terms of complexity.

5.1.7. Comparison of existing methods in terms of bandwidth consumption

Table 5 presents the performance evaluation of different systems or algorithms with varying numbers of nodes. The columns include metrics such as RM-LB, OMS-LB, SDN-AI, and WbRPL likely denoting weighted average bandwidth consumption per protocol. Lower values in RM-LB and OMS-LB columns suggest better load balancing, while higher values in SDN-AI and WbRPL typically indicate improved performance. For instance, as the number of nodes increases, the load balancing efficiency tends to enhance, while bandwidth consumption and possibly networking performance metrics fluctuate. These metrics provide insights into the effectiveness and efficiency of the systems or algorithms in handling biological parameter-tuning tasks. Figure 19 shows the comparison of the existing method regarding bandwidth consumption.

5.2. Discussion

A primary limitation of the proposed routing protocol is its dependence on the varying capabilities of current IoT devices, including processing power, memory, and energy resources. Devices with limited capabilities may need help to fully utilize the protocol's features, resulting in suboptimal performance in some cases. Healthcare environments are highly dynamic, with devices frequently added or removed from the network. While the protocol includes adaptive mechanisms, sudden and substantial changes in network topology can still pose challenges, potentially affecting the stability and reliability of data transmission. While the protocol is engineered for scalability, deploying it extensively in densely populated areas or extensive healthcare facilities could potentially encounter performance bottlenecks.

Further optimization and testing are necessary to ensure consistent performance across all scales. Incorporating robust security measures, such as encryption and authentication, introduces computational overheads. While these are crucial for protecting patient data, they may also lead to increased energy consumption and latency, particularly in devices with limited processing power. The principles and mechanisms underlying the proposed routing protocol apply beyond healthcare applications. With appropriate modifications, the protocol can be extended to various other IoT scenarios, offering similar benefits in terms of efficiency, reliability, and security. In innovative city applications, IoT devices monitor and manage urban infrastructure, including traffic lights, public transportation, and environmental sensors.

Table 4.
Comparison of existing method in terms of complexity.

No. of nodes RM-LB OMS-LB SDN-AI WbRPL(Pro)

20 62 58 55 0

40 80 62 60 55

60 85 80 75 60

80 100 90 85 75

100 120 110 100 85

120 140 130 110 100

140 160 140 100 95

160 180 150 120 110

180 200 170 110 115

200 220 180 140 120

No. of nodes	RM-LB	OMS-LB	SDN-AI	WbRPL(Pro)
20	62	58	55	0
40	80	62	60	55
60	85	80	75	60
80	100	90	85	75
100	120	110	100	85
120	140	130	110	100
140	160	140	100	95
160	180	150	120	110
180	200	170	110	115
200	220	180	140	120

RM-LB: root mean square load balancing; OMS-LB: optimized mean square load balancing; SDN-AI: software-defined networking with artificial intelligence; WbRPL: Whale-based Low-Power and Lossy Network Routing Protocol.

Table 5.

System performance evaluation: Load balancing and bandwidth metrics.

No. of nodes	RM-LB	OMS-LB	SDN-AI	WbRPL(Pro)
20	0	28	35	40
40	28	32	38	45
60	29	35	38	45
80	30	38	42	50
100	35	40	50	55
120	35	45	55	60
140	38	50	60	65
160	38	55	65	70
180	40	58	68	75
200	45	60	72	80

Figure 19.

Comparison of existing method in terms of bandwidth consumption.

The routing protocol can be adapted to handle the high data volume and diverse network conditions typical of urban environments, ensuring efficient and reliable communication. In industrial settings, IoT devices monitor and control manufacturing processes, machinery, and supply chains. The protocol's emphasis on low latency and high reliability makes it well-suited for time-sensitive industrial applications, where delays or data loss can have significant operational impacts. Precision agriculture relies on IoT devices to monitor soil conditions, weather patterns, and crop health. The protocol can be customized for agricultural environments to address challenges such as comprehensive area coverage and intermittent connectivity, ensuring timely and accurate data collection for informed decision-making. In smart homes, IoT devices control lighting, heating, security systems, and household appliances. The protocol can enhance the efficiency and reliability of home automation networks, providing seamless integration and communication among various devices. IoT devices deployed for environmental monitoring track parameters such as air quality, water quality, and wildlife activity. The protocol's energy-efficient and scalable design can support large-scale deployments in remote or harsh environments, enabling comprehensive and continuous monitoring. Collaborating with IoT device manufacturers to develop hardware optimized for the routing protocol can enhance performance and efficiency, particularly in devices with constrained resources. Improving the protocol's adaptability to dynamic network conditions through advanced algorithms and machine-learning techniques can ensure excellent stability and reliability in diverse environments.

6 Conclusion

This research paper proposes an intelligent WbRPL algorithm for an optimal routing protocol in IoT-based healthcare applications. To enhance the security and communication of IoT networks, the developed routing protocol makes the secure and efficient decision about the IoT nodes to utilize the cluster node using the proposed optimization fitness function. Moreover, this fitness can be considered the initial energy level of each IoT node. The efficiency of the developed routing protocol was then compared to conventional protocols. The performance of different protocols is evaluated based on energy consumption, security range, and throughput. Then, the implementation outcomes demonstrate that the proposed protocol performs higher throughput and consumes less energy within a short duration. Finally, the proposed research presents an intelligent routing protocol on the fundamental objectives of W.O. It can select the best cluster node with minimum energy consumption in IoT networks.

Footnotes

Ethical considerations

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to participate

All the authors involved have agreed to participate in this submitted article.

Consent to publish

All the authors involved in this manuscript give full consent for publication of this submitted article.

Authors contributions

All authors have equal contributions to this work.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

Data sharing does not apply to this article.

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No. of nodes	RM-LB	OMS-LB	SDN-AI	WbRPL(Pro)
20	62	58	55	0
40	80	62	60	55
60	85	80	75	60
80	100	90	85	75
100	120	110	100	85
120	140	130	110	100
140	160	140	100	95
160	180	150	120	110
180	200	170	110	115
200	220	180	140	120

No. of nodes	RM-LB	OMS-LB	SDN-AI	WbRPL(Pro)
20	62	58	55	0
40	80	62	60	55
60	85	80	75	60
80	100	90	85	75
100	120	110	100	85
120	140	130	110	100
140	160	140	100	95
160	180	150	120	110
180	200	170	110	115
200	220	180	140	120

An intelligent data communication in IoT-based healthcare application using a novel optimized routing protocol

Abstract

Keywords

1 Introduction

4.1 W.O. algorithm (WOA) for RPL

5.1.1 Energy consumption

5.2. Discussion

Table 4. Comparison of existing method in terms of complexity. No. of nodes RM-LB OMS-LB SDN-AI WbRPL(Pro) 20 62 58 55 0 40 80 62 60 55 60 85 80 75 60 80 100 90 85 75 100 120 110 100 85 120 140 130 110 100 140 160 140 100 95 160 180 150 120 110 180 200 170 110 115 200 220 180 140 120

Footnotes

Ethical considerations

Consent to participate

Consent to publish

Authors contributions

Funding

Declaration of conflicting interests

Data availability statement

References

Table 4.
Comparison of existing method in terms of complexity.

No. of nodes RM-LB OMS-LB SDN-AI WbRPL(Pro)

20 62 58 55 0

40 80 62 60 55

60 85 80 75 60

80 100 90 85 75

100 120 110 100 85

120 140 130 110 100

140 160 140 100 95

160 180 150 120 110

180 200 170 110 115

200 220 180 140 120

No. of nodes	RM-LB	OMS-LB	SDN-AI	WbRPL(Pro)
20	62	58	55	0
40	80	62	60	55
60	85	80	75	60
80	100	90	85	75
100	120	110	100	85
120	140	130	110	100
140	160	140	100	95
160	180	150	120	110
180	200	170	110	115
200	220	180	140	120