Improved distance and energy aware link stability protocol for internet of medical things

Abstract

The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling seamless connectivity among medical devices for real-time monitoring and diagnosis. However, the dynamic nature and resource constraints of IoMT networks present significant challenges in ensuring energy efficiency, reliable communication, and optimal performance. For addressing the mentioned issues and challenges, we propose a comprehensive routing framework, Improved Distance and Energy Aware Link Stability (IDEALS) protocol, designed to optimise distance, Energy Consumption (EC), and Link Stability (LS). Our protocol incorporates a distance-aware routing mechanism to minimize Path Loss (PL), an Energy-Efficient (EE) communication approach to extend Network Lifetime (NLT), and LS prediction algorithm for enhancing reliability in dynamic environments. Additionally, it considers the integration of Random Waypoint Mobility (RWM) and Gauss-Markov Mobility (GMM) models for static and dynamic mobility of the nodes within the network. The IDEALS protocol uses a smart decision-making method. This helps it manage key factors well regarding the node movement and static factors. It’s great for IoMT applications because it scales and adapts easily. The protocol is evaluated with state-of-the-art (SOTA) protocols regarding different evaluation metrics. Simulations show that IDEALS performs better than other protocols. It measures how quickly nodes fail and how well data is delivered. It also performs well in big networks, busy areas, and when devices are moving around. IDEALS is a solid choice for solving challenges in IoMT networks. It helps make healthcare delivery more efficient and reliable across different situations.

Introduction

Due to the rapid advancements in sensor networks, wireless communication has become a crucial aspect of various applications; thus, sensors play a vital role in the healthcare field and form a network called IoMT. Unlike traditional systems, IoMT integrates cutting-edge technologies with sensors, wearables, and cloud-based services to form a continuously adaptive care network. It is perceived as the next significant step in the evolution of Wireless Body Area Networks (WBANs) due to its self-adaptive ability to adapt to the changing environment independently. The IoMT is an evolving healthcare ecosystem of interconnected medical devices facilitating real-time monitoring, diagnostics, and tailored treatment¹. When IoMT is appropriately integrated, actionable changes will be made remotely according to real-time monitoring of the patients, enabling the timely delivery of interventions to the patients by healthcare providers without facing the hassle of in-person visits². Accordingly, IoMT networks serve as a backbone for remote patient monitoring, chronic disease management, and the development of new smart healthcare solutions.

However, the efficient functioning of IoMT systems depends strongly on robust and adaptive communication protocols to effectively manage resource constraints like limited energy and bandwidth and adapt to highly dynamic environments in real-time³. Distance is the primary problem within IoMT networks, and it has a considerable performance impact on communication nodes. IOMT systems often consist of nodes, which can be wearable medical devices and/or sensors communicating with a central hub and/or other devices. As we shift towards real-world physical scenarios, we find that at further distances between the nodes, the signals weaken, leading to increased path loss and poor, unreliable data delivery. Moreover, greater distances between nodes lead to increased energy usage as devices need to apply more power to remain consistently connected⁴. In addition, with increasing distances between two communicating devices, the possibility of communication failure becomes high, which could result in patient monitoring interruptions or loss of important medical data. Distance-aware protocols are required to resolve and address these issues, which can optimize transmission paths by dynamically considering the distance between the nodes and selecting the energy-efficient route. This reduces PL, conserves energy, and guarantees reliable and timely data delivery even in extensive networks⁵. Another key consideration is the energy for IoMT networks, particularly since many of the medical devices in these systems are battery-operated and often deployed in environments where recharging is impractical. Medical sensors and wearables must operate continuously, sometimes for months or years, without recharging⁶. This requirement places significant pressure on the battery life of the devices, making it essential to reduce energy consumption wherever possible⁷. Efficient communication protocols are required to minimize unnecessary retransmissions (which waste energy) and optimize the use of the available energy resources⁸. The key strategies include adaptive power control, where transmission power is dynamically controlled based on the current distance between nodes and signal quality, and multi-hop routing, where data is forwarded through intermediate nodes to reduce the energy burden on individual devices⁹. Such energy-efficient mechanisms are critical to maintaining the working life of the devices and delivering continuous service in healthcare settings, even when the devices are underpowered or in geographically distant locations with limited charging facility access^10,11,12,13.

Along with energy and distance, the stability of links is also a predominant factor that enables IoMT networks to be stable and efficient¹⁴. Unlike traditional networks, where devices remain static, IoMT networks’ devices are prone to mobility or interference from the surrounding environment, leading to link instability¹⁵. Physical barriers, movement of nodes (e.g., movement of patients), and fluctuating signal levels of interference are environmental factors leading to permanent disconnections, delays, and lost packets¹⁶. Such interferences can significantly weaken the performance of health-related applications, such as real-time health monitoring or critical treatment, which require minimal latency and optimal reliability¹⁷. To solve such issues, link-stability-aware mechanisms must be constructed to monitor and predict network change and adjust communication parameters in response¹⁸. The protocol, for example, could switch between communication modes (e.g., direct or relayed communication) or adjust routing methods to accommodate stable connections, minimizing environmental impact and enhancing overall reliability^19,20,21,22. Our suggested protocol integrates LS’s key components —energy efficiency and distance awareness—into a cohesive system. IDEALS seeks to concurrently address all these factors, offering a comprehensive solution for the challenges in IoMT systems, whereas existing protocols may concentrate on just one. By emphasizing an integrated strategy, IDEALS aims to guarantee LS, minimize energy consumption, and mitigate path loss, resulting in a more dependable and effective communication network for IoMT devices. Despite these sophisticated features, the protocol still has issues, especially when handling the computational overhead necessary for real-time adaptation in extremely dynamic environments. Ensuring the protocol is scalable becomes crucial as IoMT networks grow to accommodate more devices. The network must handle more devices without slowing down or using too many resources. Solving these problems is essential so the IDEALS protocol can provide reliable communication in different healthcare settings. This includes everything from small setups to big, complicated systems. The following are the key contributions of this article, which clarify the main proposed solution for the IDEALS protocol.

• We propose a novel routing framework that optimizes distance, energy, and LS for enhanced performance in IoMT networks, and includes an energy-aware optimization model that minimizes unnecessary EC by leveraging adaptive power control, multi-hop routing, and EE node selection.

• We integrate an LS prediction algorithm that evaluates the reliability of communication links based on historical and real-time metrics, enabling proactive route adjustments to avoid unstable connections.

• We incorporate an intelligent distance-aware routing mechanism that dynamically selects the optimal path by considering the separation between nodes, reducing PL, and ensuring efficient data transmission. This is achieved through a multi-criteria decision-making approach that balances trade-offs between distance, energy, and LS to select the best routes dynamically.

• Finally, we evaluate IDEALS against existing protocols using key performance metrics, demonstrating significant improvements while maintaining consistent performance as IoMT networks scale.

The rest of the article is organized as follows: "Literature review" deals with the literature review, "Methodology" gives the core method and implementation, Sect. 4 explains and discusses the simulation results, and Sect. 5 concludes the article.

Literature review

IoMT has become pivotal in healthcare applications, enabling continuous monitoring and real-time data transmission. However, energy consumption remains a critical bottleneck due to the limited battery capacity of sensor nodes. Several approaches to improve energy efficiency in IoMT have been investigated in current research. Some researchers created a cluster-based routing system for WBANs. They suggest black widow optimisation (BWO) algorithms to improve WBANs in healthcare. Their goal was to save energy and improve the network lifetime. This is essential for medical monitoring, where reliability is the key²³. In this study, the researchers developed a new cluster-based routing system using the Elk herd optimiser. This method aims to lower energy consumption and improve WBAN performance efficiently. They focused on how to save energy during data transfer, which helps make the network more stable²⁴. Another team reviewed the technology behind the Wireless IoMT. They examined how healthcare apps fit into WBANs. They discussed challenges such as privacy, safety, and data transmission in IoMT²⁵. Proposed a new routing method that considers both energy and temperature for WBANs. They focused on avoiding situations that could lead to overheating, which could harm medical devices, especially in critical health situations²⁶.

Another study introduced the WHOOPH protocol, which uses whale optimisation to find the best spots for hub nodes in WBAN. This strategy improves energy efficiency and overall network performance²⁷. Using a Markov decision process, researchers have created an intelligent energy-focused protocol for Internet of Things-based WBANs. This seeks to maintain dependable connectivity while extending the lifespan of sensor nodes in healthcare²⁸. Another team examined how temperature and energy use could affect routing in WBANs. This is especially important for medical monitoring, where factors such as body heat and battery levels matter much²⁹. In a review, scientists classified various routing protocols for WBANs, checking their energy use, reliability, and how they scale. This laid the groundwork for tackling challenges in future WBAN developments for healthcare³⁰. MAC protocol proposed an energy-efficient and reliable communication for WBANs. It aims to maintain strong communication while using less power, which is essential for medical devices³¹. To conserve energy in WBANs, an alternative team proposed a hybrid clustering extension lifetime method. This is essential for healthcare monitoring since malfunctioning sensor nodes risk erasing data or giving inaccurate health information³². Several researchers have investigated the BWO method to improve cluster-based routing in WBANs, particularly for remote patient monitoring systems. They stressed the importance of reliable data and energy efficiency²³. Another group introduced a flexible medium access control protocol for healthcare situations. This allows reliable data transmission even when network conditions change³³. Some researchers created a new routing protocol using harmony search optimisation to improve WBAN lifespan. This helps ensure energy is used wisely while keeping communication reliable for wearable health devices³⁴. Another study explored the application of fuzzy logic in selecting forward nodes based on connection quality. By improving the choice of relay nodes, communication becomes more dependable and effective, essential for monitoring patients vital signs^26,36.

Some authors proposed a new routing protocol for Wireless Body Area Networks (WBANs) considering thermal effects. This helps keep sensors running well while also handling heat. That’s important in medical cases where heat can mess with devices³⁷. Other researchers developed an MAC protocol based on SMO for WBANs that work with IoT devices. The goal was to improve communication, especially for healthcare gadgets, by making them more energy-efficient and reliable in medical settings³⁸. A team also created an energy-saving hybrid protocol with a model to boost performance in WBANs. Their method focuses on energy use and is especially key for medical applications where devices must be dependable³⁹. Another group introduced a multi-path routing protocol to improve how data is sent and extend the network’s life. This helps avoid issues if some nodes fail, which is vital for medical monitoring⁴⁰. One study examined packet loss in ITHMAC-based smart patient monitoring systems. When they compared it to THMAC, they discovered that ITHMAC was more reliable and essential for the real-time monitoring of patients⁴¹. A reliable routing method for WBANs based on centrality measurements is also being researched. This technique ensures robust data transfer while selecting the optimal routes and minimising energy consumption. It is crucial for medical networks where dependability is crucial³⁹. The authors introduced QQAR, a routing algorithm that uses Q-learning to improve Quality of Service in IoT-enabled WBAN. This method finds optimal routes based on speed and energy use. It enhances communication for applications such as remote health monitoring^42,43. Finally, some researchers developed SEEDI, a technique that uses mobile sinks to spread data more efficiently in IoMT networks. This helps reduce energy use, which is crucial for devices with limited power. These studies work together toward making WBANs more energy-efficient, especially in healthcare⁴⁴. There has been significant research on IoMT/WBAN, which is crucial for healthcare and personal monitoring. In one study, researchers improved three-factor authentication for these networks⁴⁸. Another team looked at the setup and security of WBANs and their interaction with IoT. This is particularly important as more devices become online and require safety, especially in healthcare⁴⁹. Some others suggested a data-sharing method combining cloud and edge computing for secure information exchange in industrial IoT⁵⁰. Some researchers designed a model for dynamic access management using smart algorithms. This helps manage connected devices better⁵¹. Another project developed a tiny implantable antenna for wireless data transmission from the body to outside devices. It’s small and practical⁵². There’s also work on better resource scheduling in these networks. An adaptive algorithm was created to optimise resource use⁵³. Other studies have focused on developing a solid design for IoT-based sensors to ensure they perform well in various conditions. Some teams used federated learning to manage data transmission times with smart scheduling⁵⁴. One study showed a self-powered system using a single fibre, which is great for wearables⁵⁵. Lastly, researchers created a low-radiation wearable antenna with strong connectivity^56,57.

Methodology

The primary approach and its specifics, such as the system model, simulation parameters, and algorithms, are covered in this section. The IDEALS protocol aims to tackle the challenges of communication in IoMT networks. Efficient data transmission is key for real-time health applications. This approach improves routing by considering important factors such as node distance, energy consumption, and link quality. It uses a multi-layered strategy that adjusts to changing network conditions. This helps save energy while keeping a strong link. Collecting data from the sensor nodes, signal strength, energy levels, and distances between MIoT devices. This data helps find the optimal paths that use less energy and are stable. The protocol’s efficacy is evaluated using mathematical models and simulations, assessing parameters such as throughput, remaining energy, packet delivery, and routing overhead.

By integrating these elements, IDEALS reduces energy consumption while enhancing the reliability and efficiency of communication inside IoMT networks. We proposed IDEALS, an IoMT routing protocol that uses 20 nodes. Additionally, IDEALS are evaluated against SOTA methods to benchmark their performance in NLT, BER, PDR, delay, EE, and resource optimisations. This method enables the protocol to address the unique requirements of IoMT, to balance energy efficiency, distance optimisation, and LS for delivering advanced and reliable communication. Fig. 1 (a), and (b) illustrates architecture and method flowchart of proposed scheme. While the principle of the data forwarding scheme is given in Algorithm 1. 

Fig. 1

Overview of the IDEALS System Workflow and Methodology. A comprehensive visual representation of the overall working process of the proposed IDEALS scheme.

It begins with the system initialisation, which sets the foundation for the deployment strategy. The initialisation process incorporates essential elements such as Link Stability (LS), Energy Efficiency (EE), and key factors like distance and delay, all of which are central to the scheme’s core contributions. The Figure not only showcases the sequence of operations in the system but also provides a diagrammatic flowchart that clearly illustrates the overall methodology of the proposed system. This flowchart is a step-by-step guide to understanding how the IDEALS protocol dynamically manages network resources and adapts to changing conditions to optimise performance. The focus on Link Stability ensures that the network remains robust against disruptions, while Energy Efficiency addresses the critical challenge of conserving power in IoMT networks, thus extending the operational lifetime of sensor nodes. Moreover, including distance and delay parameters highlights the system’s attention to communication constraints, ensuring the network’s performance is optimised under varying physical and environmental conditions. These components contribute to the system’s ability to deliver reliable and efficient communication in IoMT environments, emphasising resilience, sustainability, and real-time responsiveness. The flowchart provides an organized view of the system’s operation and emphasises the interrelationship between these key elements, offering insight into how they work together to achieve the desired performance outcomes. Through this visual and methodological framework, the Figure effectively conveys the systematic approach of the IDEALS protocol in managing IoMT network requirements and challenges.

Algorithm 1

Data Forwarding in IDEALS.

System model

Network topology and distance metrics

• Distance between two nodes S_i and S_j.

$${d_{ij}}=\sqrt {{{({x_i} - {x_j})}^2}+{{({y_i} - {y_j})}^2}+{{({z_i} - {z_j})}^2}}$$

(1)

Equation (1) calculates the Euclidean distance between IDEALS nodes in the 3D deployment space, forming the foundational metric for distance-aware routing. The accuracy of distance computation is essential for evaluating EC and LS.

• PL model for transmission over distance d_ij.

$$P{L_{IDEALS}}\left( {{d_{ij}}} \right)=P{L_0}+10 \cdot \gamma \cdot lo{g_{10}}\left( {{d_{ij}}} \right)$$

(2)

PL_0​ is the path loss reference, and γ is the exponent of path loss, which is dynamically adjusted in IDEALS based on the IoMT environment. Equation (2) is used to predict the attenuation of signals and optimise LS.

• Signal-to-noise ratio (SNR).

$$~~SN{R_{IDEAL{S_{,ij}}}}=\frac{{{P_{receive{d_{,ij}}}}}}{{{N_0}}}$$

(3)

Equation (3) evaluates the signal quality for transmissions between two nodes in IDEALS, which is critical for ensuring LS. P_{received, ij} is determined by the environment and is key to stable communication.

• Received power at distance d_ij.

$${P_{received,IDEALS,ij}}={P_{transmit}} \cdot {G_t} \cdot {G_r} \cdot {\left( {\frac{\lambda }{{4\pi {d_{ij}}}}} \right)^2}$$

(4)

Equation (4) calculates the power received after accounting for antenna gains ($\:{G}_{t}\cdot\:{G}_{r}$​) and the wavelength (λ). Protocol optimises this metric to enable LS over varying distances.

Energy consumption

• Energy consumed for transmitting L bits over d_ij.

$${E_{transmit,IDEALS}}=L \cdot {E_{elec}}+L \cdot {E_{amp}} \cdot d_{{ij}}^{\alpha }$$

(5)

Equation (5) computes the energy used during data transmission, where E_elec is the energy per bit for electronic operations, and E_amp​ represents the amplifier energy. The exponent α depends on the medium.

• Energy consumed for receiving L bits.

$${E_{received,IDEALS}}=L \cdot {E_{elec}}$$

(6)

Equation (6) denotes the reception energy in IDEALS, which is simpler since it doesn’t include distance factors. Minimising unnecessary receptions is critical to conserving node energy.

• Residual energy after t^− th round.

$${E_{r,IDEALS}}\left( t \right)={E_{initial}} - \mathop \sum \limits_{{t^{\prime}=1}}^{t} \left( {{E_{transmit,IDEALS,t^{\prime}}}+{E_{receive,IDEALS,t^{\prime}}}} \right)$$

(7)

Equation (7) denotes the remaining energy in a node after each communication round, which helps IDEALS determine node eligibility for participation in the next routes.

• Energy efficiency.

$${\eta _{E,IDEALS}}=\frac{{Total~data~delivere{d_{IDEALS}}}}{{Total~energy~consume{d_{IDEALS}}}}$$

(8)

Equation (8) denotes energy efficiency, a performance parameter to quantify the IDEAL’s capability to deliver data while minimising energy usage.

Link stability

• Variation in RSSI (ΔP) between Consecutive Transmissions.

$$\Delta {P_{IDEALS}}=\mid {P_t} - {P_{t - 1}}\mid$$

(9)

Equation (9) represents the change in variations between the concurrent transmissions. In protocol, the LS is monitored by evaluating the change in RSSI over time. The higher the variations, the greater the potential instability.

• LS_ij

$$L{S_{IDEALS,ij}}=\exp \left( { - \frac{{\Delta {P_{IDEALS}}}}{{{P_{threshold}}}}} \right)$$

(10)

Equation (10) denotes that IDEALS uses an exponential decay model for LS, where a lower ΔP (less variation) results in improved LS.

• Average LS over T transmissions

$$L{S_{avg,IDEALS}}=\frac{1}{T}~\mathop \sum \limits_{{t=1}}^{t} L{S_{IDEALS,ij}}\left( t \right)$$

(11)

Equation (11) represents a cumulative measure of LS and link quality over a series of transmissions, critical for route selection in IDEALS.

• Probability of link failure (P_fail)

$${P_{fail,IDEALS}}=1 - L{S_{IDEALS,ij}}$$

(12)

Equation (12) denotes the prediction of the likelihood of a link breaking, enabling IDEALS to avoid any unstable routes or links proactively.

Routing formation

• Combined metric for routing (M_ij).

$${M_{IDEALS,ij}}={w_1} \cdot \frac{1}{{{d_{ij}}}}+{w_2} \cdot L{S_{IDEALS,ij}}+{w_3} \cdot ~\frac{{{E_{r,IDEALS}}}}{{{E_{initial}}}}$$

(13)

Equation (13) is the major routing parameter of the protocol, which combines distance, LS, and energy metrics with adjustable weights (w₁, w₂, w₃) to suit IoMT application needs.

• Normalised weights for the metrics ($\:{\varvec{w}}_{1},{\varvec{w}}_{2}$, $\:{\varvec{w}}_{3}$).

$${w_1}+{w_2}+~{w_3}=1~$$

(14)

Equation (14) represents the tuned weights based on application requirements, like prioritising energy conservation or stability in our protocol. The weighting factors $\:{w}_{1}+{w}_{2}$+ $\:{w}_{3}$ The combined routing metric M_ij balances distance, energy, and LS. These weights are static but can be adjusted based on the specific needs of the IoMT application. A sensitivity analysis was conducted, demonstrating how weight variations affect the IDEALS performance regarding energy consumption, throughput, and LS, thus ensuring the IDEALS adaptability to different conditions. As noted, the weights $\:{w}_{1}+{w}_{2}$+ $\:{w}_{3}$ in Eq. (14), the parameters are statically defined in our current implementation, based on the specific application requirements, such as prioritising energy efficiency or link stability. To address this, it’s clarified that although the weights are statically set at deployment, the IDEALS protocol is designed flexibly. Specifically, the weights can be reconfigured offline or during predefined intervals, allowing the system to adapt to changing network conditions or evolving application priorities. Based on real-time network feedback, dynamically adapting these weights at runtime could enhance performance, particularly in highly variable healthcare scenarios (e.g., mobile or failing nodes). However, dynamic adjustment introduces additional complexity, including the need for real-time monitoring and decision-making logic.

• Selection of the best route (R_best​).

$${R_{best,IDEALS}}=arg\mathop {\hbox{max} }\limits_{j} \left( {{M_{IDEALS,ij}}} \right)~$$

(15)

Equation (15) represents the best route maximising the combined metric, ensuring the protocol selects the optimal path.

Path optimisation

• Total path cost (C_p​) for a route with n hops

$${C_{p,IDEALS}}=\mathop \sum \limits_{{k=1}}^{n} \left( {{w_1} \cdot \frac{1}{{{d_k}}}+{w_2} \cdot L{S_{IDEALS,k}}+{w_3} \cdot \frac{{{E_{r,IDEALS}}\left( k \right)}}{{{E_{initial}}}}} \right)$$

(16)

Equation (16) denotes the aggregation and the cost of all hops along a route, incorporating all IDEALS parameters.

• Path reliability (R_p​)

$${R_{p,IDEALS}}=\mathop \prod \limits_{{k=1}}^{n} L{S_{IDEALS,k}}$$

(17)

Equation (17) represents the evaluation of the reliability of the entire route based on individual LS and links.

Performance metrics

• Packet delivery ratio (PDR)

$$PD{R_{IDEALS}}=\frac{{Packets~received~at~sin{k_{IDEALS}}}}{{Packets~transmitte{d_{IDEALS}}}}$$

(18)

Equation (18) represents PDR, which quantifies the IDEALS protocol’s effectiveness in reliably delivering data packets.

• End-to-end delay (EtoED)

$${D_{E2E,IDEALS}}=\mathop \sum \limits_{{k=1}}^{n} \frac{{{L_k}}}{{{B_k}}}~$$

(19)

Equation (19) calculates the total delay experienced by a packet while traversing n hops. L_k​ is the packet size at the k^-th hop, and B_k​ is the bandwidth.

• Network lifetime (T_lifetime​)

$${T_{lifetime,IDEALS}}=\mathop {\hbox{max} }\limits_{t} \left\{ {t:\forall i,{E_{r,IDEALS}}\left( t \right)>{E_{threshold}}} \right\}~~~$$

(20)

Equation (20) quantifies IDEALS’ effectiveness in extending network lifetime.

• Throughput (T)

$${T_{IDEALS}}=\frac{{Total~data~packets~delivered~b{y_{IDEALS}}}}{{Simulatio{n_{time}}}}$$

(21)

Throughput represents the rate of successful data delivery. IDEALS maximises this metric by selecting stable, energy-efficient paths and minimising retransmissions caused by link failures.

• Residual energy (E_r​)

$${E_{r,IDEALS}}\left( t \right)={E_{initial}} - \mathop \sum \limits_{{t^{\prime}=1}}^{t} \left( {{E_{transmit,IDEALS,t^{\prime}}}+{E_{receive,IDEALS,t^{\prime}}}} \right)$$

(22)

E_r​ indicates the remaining energy in a node after a specific number of rounds. IDEALS extends the network lifetime and optimises resource utilisation by considering energy efficiency in routing decisions.

• Path loss (PL).

In the context of the IoMT, path loss (in dB) is a critical factor affecting the communication between devices like medical sensors and gateways or sinks. For the IDEALS protocol, the path loss equation can be derived from the general radio propagation models commonly used in wireless communication networks. The Free-Space Path Loss (FSPL) model is often used for line-of-sight (LoS) communication, and the general path loss equation can be written as:

$$PL\left( {dB} \right)=20{\log _{10}}\left( d \right)+20{\log _{10}}\left( f \right)+20{\log _{10}}\left( {\frac{{4\pi }}{c}} \right)$$

(23)

Where PL(dB) is the path loss in dB, d is the distance between the transmitter and receiver (in meters), f is the frequency of the signal (in Hz), and c is the speed of light in a vacuum (3 × 10⁸ m/s). In an IoMT environment, especially with IDEALS, various other factors must be considered, including energy-efficient routing and localisation, as these can influence the propagation environment and affect PL. Distance (d): In IoMT, sensor nodes are placed on or near the human body or within medical devices. The PL increases logarithmically as the distance between devices (such as wearable sensors and a central monitoring system) increases. Frequency (f): Medical devices typically operate on standard communication bands, such as 2.4–5 GHz for Bluetooth or Zigbee. The PL is proportional to the frequency, and higher frequencies tend to suffer greater PL. Speed of light (c): This is a constant used to determine the propagation speed of electromagnetic waves. In IDEALS, energy efficiency is a significant factor. PL directly impacts the signal strength and energy consumption during communication. As the signal strength decreases with increased PL, devices might need to increase their transmission power, leading to higher energy usage, which IDEALS aims to optimise by selecting energy-efficient communication paths.

• Mean square error (MSE)

$$MS{E_{IDEALS}}=\frac{1}{N}\mathop \sum \limits_{{t=1}}^{N} {({P_i} - \widehat {{{P_i}}})^2}$$

(24)

MSE evaluates the accuracy of predicted performance metrics (e.g., RSSI or path loss) against actual values. IDEALS ensures that predictive models used in routing calculations are accurate, minimising MSE.

• Bit error rate (BER)

$$BE{R_{IDEALS}}=\frac{{Number~of~erroneous~bits~in{~_{IDEALS}}}}{{Total~bits~transmitted~in{~_{IDEALS}}}}$$

(25)

BER quantifies the fraction of erroneously transmitted bits due to noise or link issues. IDEALS minimises BER by selecting stable links and optimising transmission power.

• Packet error rate (PER)

$$PE{R_{IDEALS}}=1 - {(1 - BE{R_{IDEALS}})^L}$$

(26)

PER relates to BER and represents the probability of a packet being erroneous. Here, L is the packet length. IDEALS reduces PER by ensuring strong signal quality and reliable LS.

• Computational cost of routing (C_comp)

$${C_{comp,IDEALS}}=\mathop \sum \limits_{{i=1}}^{N} \left( {Complexit{y_{distance}}+Complexit{y_{stability}}+Complexit{y_{energy}}} \right)$$

(27)

C_comp measures the processing overhead for routing decisions. IDEALS ensures scalability by keeping computational complexity low, despite incorporating multiple metrics. IDEALS improves PDR and T by choosing stable and energy-efficient routes. It reduces delay and NRL by optimising route selection, which ensures control packets are used efficiently. The protocol extends the network lifetime by avoiding routes that consume excessive energy. Accurate predictions in IDEALS help make better routing decisions and minimise the need for retransmissions, as reflected in low MSE and RMSE. By maintaining high signal quality and stable links, IDEALS minimizes BER and PER, critical for transmitting sensitive medical data. Additionally, the protocol balances reducing hop counts, conserving energy, and maintaining computational efficiency.

Results

In this section, we discuss the performance of the IDEALS, which is designed for IoMT applications. The simulation results and metric analysis provided are based on the performance of five protocols, HEEMCCP, SD-WBAN, QQAR, SEEDI, and IDEALS, in terms of PDR, EtoED, Throughput, NLT, RE, PL, MSE, BER, PER, and CCR. Figure 2 shows the proposed network model for sensor node deployment in IoMT, while Table 1 shows the parameters with their values taken for simulations, along with the performance evaluation metrics that have been undertaken.

Fig. 2

Node Deployment Scenario and Coverage Area (visual representation of the deployment scenario of the nodes within the proposed network’s spatial volume and size). It effectively captures how nodes are distributed in a three-dimensional space, specifically within a 100 m x 100 m x 50 m³ volume, clearly depicting the networks physical layout. Each node is depicted with its corresponding coverage area, highlighting the range within which it can communicate and interact with other nodes in the network.

The 3D deployment of the nodes emphasises the real-world application of the network, where physical constraints such as distance, obstacles, and environmental factors influence node positioning and communication efficiency. This spatial representation helps to visualise how the network operates in a real-world scenario, where nodes are strategically placed to ensure optimal coverage and minimise communication gaps. By illustrating the coverage area for each node, the Figure emphasises the extent to which each node can serve and connect with its surrounding environment, ensuring continuous and reliable communication. The 3D arrangement of the nodes underscores the complex nature of network design in real-world IoMT applications, where factors like node density, placement, and coverage are critical to ensuring seamless data transmission and network stability. Overall, this Figure offers a detailed, spatial perspective of the network deployment strategy, providing a clear understanding of how the proposed system functions in three-dimensional space to maximise coverage and network efficiency.

Table 1 General simulation Parameters, their Values, Ranges, and Description.

Reliability evaluation

IDEALS maintains a high PDR by adjusting routes based on real-time conditions like bandwidth and signal strength. This adaptability ensures reliable delivery, particularly for urgent applications like telemedicine. Figure 3(a) compares IDEALS to other leading protocols. Furthermore, IDEALS keeps MSE low through error correction techniques like retransmission, ensuring accurate data delivery. This is particularly important for sensitive applications such as healthcare. Figure 3(g) compares the MSE of IDEALS with other protocols. IDEALS reduces BER by adjusting transmission power and using error correction techniques. This is critical in healthcare, where data accuracy is essential. Figure 3(h) shows how IDEALS compares to other methods in terms of BER. IDEALS minimizes PER by selecting optimal routes and retransmitting lost or corrupted packets. This ensures reliable data delivery, which is crucial in fields like healthcare. Figure 3(i) compares PER for IDEALS and other schemes.

Fig. 3

Performance Evaluation of IDEALS against other Protocols: PDR, EtoED, Throughput, NLT, Energy Consumption, PL, MSE, BER, PER, CCR.

Energy efficiency evaluation

IDEALS enhances NLT by using energy-efficient algorithms, balancing load, and adjusting power consumption based on node distance. It also considers node energy levels when selecting routes, preventing early failures. IDEALS significantly increase NLT compared to other protocols, which is essential in critical applications like healthcare. Figure 3(d) illustrates IDEALS superior performance in NLT. Table 2 illustrates the performance of NL in terms of alive/dead nodes. The total simulation rounds are 5000, and the total number of nodes is 20, which means these are multiplied to find each scheme’s overall operational time in terms of alive and dead nodes.

$${5000_{Rounds}} \times {20_{Nodes}}=100,{000_{tota{l_{NLT}}}}$$

(28)

This represents the number of node-simulation interactions (i.e., 20 nodes are involved across 5000 rounds). Based on this, we can generate a value representing the performance for all nodes over the simulation rounds. Now, we can calculate an aggregated value for each protocol by considering the following. We calculate an average or a combined value for each round for each FND, HND, and LND protocol. Multiply this aggregated value by the total node-simulation pairs (100,000) to get a total performance score for each protocol. The FND, HND, and LND values at each simulation round will be used to calculate the average for each protocol. We can now compute this for each protocol regarding average FND, HND, and LND over the 10 entries (simulation rounds).

$$\begin{gathered} Average~Valu{e_{\left( {per~protocol} \right)}}=\frac{{Sum~of~FND~values}}{{10}}+ \hfill \\ \frac{{Sum~of~HND~values}}{{10}}+~~\frac{{Sum~of~LND~values}}{{10}} \hfill \\ \end{gathered}$$

(29)

Finally, multiply each average value by 100,000 to get the total performance value for each protocol.

Table 2 Network lifetime (NLT) and performance analysis for all protocols (it displays the network lifetime (NLT) scenario for all schemes in terms of FND, HND, and LND. It also includes the average calculations for each scheme and their corresponding total performance scores. The performance scores are derived by calculating the average values of FND, HND, and LND for each protocol across the simulation rounds and multiplying these averages by the total number of node-simulation interactions.

The exceeding values denote that the protocols have the highest operational time. In this sense, IDEALS has the highest operational time during the entire simulation. It also indicates that the death of FND, HND, and LND has recently occurred in IDEALS, in contrast to other schemes where node death occurs more rapidly. Furthermore, IDEALS optimises energy use by selecting shorter paths, adjusting power levels, and evenly distributing energy consumption across nodes. This approach extends network life, especially in critical areas like healthcare monitoring. Figure 3(e) compares RE performance across different protocols. IDEALS optimises CCR by using simple, efficient, lightweight routing algorithms and adapting to network changes without heavy computation. This reduces the load on nodes, ensuring smooth operation without draining resources. Figure 3(j) compares the CCR of IDEALS with other schemes.

Delay and throughput evaluation

IDEALS reduces EtoED by selecting shorter, less congested routes and adjusting in real-time. Unlike fixed-path protocols, IDEALS adapts to network changes, ensuring timely telemedicine and emergency care data deliveryshown in Fig. 3(a). Figure 3(b) compares EtoED for IDEALS versus other methods. Furthermore, IDEALS optimises throughput by selecting routes with less traffic and adjusting data rates based on network conditions. Its real-time adjustments help maintain throughput even in high-demand environments, making it ideal for busy networks. Figure 3(c) compares IDEALS’ throughput performance with other protocols. IDEALS reduces PL by selecting optimal routes and adjusting transmission power based on current conditions. Its adaptability helps maintain a strong signal, even in challenging environments, ensuring reliable communication. Figure 3(f) compares PL for IDEALS and other methods, in Fig. 3 shown the Performance Evaluation of IDEALS against other Protocols. Table 3 shows the overall performance of IDEALS vs. SOTA schemes.

Table 3 Performance Summary of IDEALS vs. SOTA Schemes (here presents the performance comparison between IDEALS and SOTA schemes, highlighting the overall percentage improvements of IDEALS across each metric). The improvements are calculated based on the relative performance enhancements of IDEALS compared to the existing protocols, providing a clear view of how IDEALS outperforms other schemes in various network performance indicators.

The IDEALS protocol is implemented in MATLAB, and nodes are deployed in a 100 × 100 × 50 m³ healthcare monitoring space. We evaluated two mobility models: the GMM model for correlated movements, mimicking wearable devices on patients, and the RWM model for unpredictable mobility, simulating mobile health units. The network included 1–10 and 11–20 Sensor Nodes (SNs), with data rates of 10–50 Kbps and packet sizes of 100 bytes to 1 KB, representing real-time IoMT applications. Sampling rates varied from 1 to 10 Hz, and transmission intervals ranged from 1 to 10 s. Both static and dynamic scenarios were tested to assess performance in varying movement conditions. IDEALSS effectively enhances network lifetime, throughput, and path loss reduction from these analyses and assessments, ensuring reliable healthcare communication in diverse deployment scenarios. To evaluate the adaptability of IDEALS, we conducted simulations under varying mobility models and node densities, comparing them against HEEMCCP, SD-WBAN, QQAR, and SEEDI. The simulation incorporated different node groups (1–10 and 11–20 nodes) to evaluate performance across low and high-density deployments. We have implemented two mobility models to evaluate IDEALS under different movement conditions. GMM model is simulated for wearable devices on patients with tuning parameter α = 0.75, high correlation for smooth mobility, and node speed of 0.1–1.5 m/s (low-speed mobility reflecting real-world patient movement). The RWM model simulates unpredictable movements, such as mobile health units or roaming patients, with a node speed of 0.5–2 m/s (random speed variations) and a pause time of 0–5 s (random delays in movement). We evaluated IDEALS with different network sizes, such as Low Density (LD) of 1–10 SNs and High Density (HD) of 11–20 SNs, with a deployment area of 100 × 100 × 50 m³ (fixed for fair comparison). IDEALS were evaluated against HEEMCCP, SD-WBAN, QQAR, and SEEDI under node densities and mobility scenarios. The key performance metrics, such as LD, HD, GMM, and RWM models, are presented in Fig. 4.

Fig. 4

The communication scenario of the simulation is presented, comparing the performance of the IDEALS scheme with SOTA protocols across multiple metrics: throughput, network lifetime (NLT), packet delivery ratio (PDR), path loss (PL), end-to-end delay (EtoED), and reliability evaluation (RE). This analysis demonstrates the effectiveness of the proposed IDEALS scheme in terms of key performance indicators in IoMT applications.

The evaluation of IDEALS was conducted against new existing schemes, LAEEBA⁴⁵, Co-LAEEBA⁴⁶, EEDLABA²², and EESBSN⁴⁷ in terms of Large-Scale, Mobility, and Congested Networks, and under varying node densities and mobility scenarios. We’ve evaluated IDEALS with different network sizes, such as LD of 1–10 SNs and HD of 11–20 SNs, with a deployment area of 100 × 100 × 50 m³ (fixed for fair comparison). Figures 5, 6 and 7, and 8 present the key performance metrics.

Fig. 5

This Figure further expands the communication scenario of the simulation by including additional SOTA schemes to provide a broader evaluation of IDEALS. It highlights the performance comparison across throughput, NLT, PDR, PL, EtoED, and RE metrics, emphasising the advantages of the IDEALS protocol over existing solutions in more diverse network conditions.

Fig. 6

The simulation scenario demonstrates the performance of IDEALS and SOTA protocols in congested network conditions. The metrics include throughput, NLT, PDR, PL, EtoED, and RE, which focus on the network’s ability to handle increased load and highlight IDEALS’ superior performance in maintaining network efficiency and reliability under strain.

Fig. 7

This Figure evaluates the IDEALS scheme in mobility networks with movable nodes in IoMT. The performance of IDEALS and SOTA protocols is compared across multiple metrics, showcasing how the system handles dynamic environments where nodes are in motion, emphasising IDEALS adaptability and robustness in real-world IoMT applications.

Fig. 8

The simulation results in congested networks are presented, comparing the IDEALS scheme with SOTA protocols. Metrics such as throughput, NLT, PDR, PL, EtoED, and RE are assessed to evaluate how each protocol performs under network congestion conditions, with IDEALS demonstrating superior efficiency and lower resource consumption.

• Realism of mobility models RWM and GMM in clinical settings

We employed two widely used models to model node mobility in clinical environments: the RWM and the GMM. These models help simulate a range of mobility patterns, from random movements typical of mobile monitoring devices or non-critical patients RWM, to smoother, more correlated movements observed in guided or semi-structured environments GMM. While these models provide a controlled means to assess the performance of the IDEALS protocol under varying mobility conditions, we acknowledge that they are not direct representations of real patient or staff movement in hospitals. Our current work does not utilise empirically derived mobility traces; however, the literature has shown that GMM can approximate realistic movement behaviours better than purely random models. We consider this a first-order approximation and suggest that future work incorporate real-world mobility datasets (e.g., hospital floor plans with sensor-tracked movement logs) to validate the protocol under more realistic conditions. This would strengthen the practical applicability of IDEALS in real healthcare deployments.

Table 4 The performance comparison of the IDEALS scheme against various SOTA protocols across different evaluation metrics is shown here. It categorises performance levels into low, moderate, and high, clearly Understanding how IDEALS outperforms others in specific areas of network performance.

The simulation was executed in MATLAB using a 3D network space of 100 × 100× 50m², with 20 sensor nodes deployed. Each node transmitted data packets ranging from 100 bytes to 1 KB, with 10–50 Kbps data rates and energy capped at 10 joules per node (Figs. 9, 10). The evaluation was performed over 5000 rounds, and both GMM and RWM mobility models were used to simulate realistic and unpredictable node behavior. Figure 11 shows the scalability analysis of IDEALS with runtime and memory vs. the number of nodes deployed.

Fig. 9

The overall performance of the IDEALS scheme is illustrated and compared with SOTA methods, highlighting its strengths across all evaluation metrics. The focus is on breaking performance into low, moderate, and high categories, demonstrating IDEALS consistent superiority in key areas.

Fig. 10

The expands on the previous analysis by including additional SOTA methods for comparison. It highlights the performance of the IDEALS scheme across all evaluation metrics, emphasizing its low, moderate, and high-performance levels, showcasing the comprehensive advantages of IDEALS over other methods.

Discussion

In the realm of the IoMT, the demand for efficient, reliable, and energy-conscious routing protocols is ever-increasing due to the sensitivity of medical data and the energy constraints of sensor nodes. Our protocol focused on challenges caused by energy, LS, and routing to enhance energy consumption, ensure stable communication links, and improve data delivery reliability. The IDEALS were compared against HEEMCCP, SD-WBAN, QQAR, and SEEDI to assess their performance across key metrics critical to IoMT applications. One of the key findings from the evaluation is that IDEALS consistently outperforms existing protocols in several important areas, such as PDR, EE, EtoED, and Throughput. This improved performance comes from two main things: focusing on location services and being smart about energy use. IDEALS include specific ways to save energy. For example, it features distance-aware communication routes and smart power control. This keeps energy consumption low. As a result, devices can last longer without running out of battery. In the IoMT, this energy-saving feature is essential. Consider wearables such as health monitors or smart medical implants. These devices require a significant amount of power, and their energy efficiency directly impacts their lifespan.

On top of that, IDEALS does a great job with location services. It selects communication paths that are stable and change minimally. This is particularly crucial in IoMT, where data loss or delays can result in serious problems. When we compare IDEALS with other protocols, such as SEEDI, SD-WBAN, QQAR, and HEEMCCP, it has a higher packet PDR and uses less energy.

Another point is how well IDEALS manages energy use while maintaining high throughput. It shows that IDEALS can deliver data on time, which is essential for IoMT applications. The way it adapts energy use based on location services and smart routing helps maintain high PDR rates without compromising reliability or increasing energy consumption. IDEALS is doing incredible work ensuring devices last longer, stay reliable, and send data quickly without consuming energy. This is crucial in medical applications where real-time monitoring and timely data transmission are required for patient safety and diagnosis. Similarly, the CCR in IDEALS was found to be optimized compared to other protocols. IDEALS minimizes the load on the sensor nodes, which is important for energy-constrained devices operating in IoMT scenarios by focusing on minimal computational overhead and reducing the number of routing decisions required. The overall comparison of IDEALS with other schemes is shown in Table 4. The evaluation scenarios of all methods across all metrics are shown in Figs. 10 and 11. The evaluation of IDEALS against existing schemes, such as HEEMCCP, SD-WBAN, QQAR, SEEDI, LAEEBA, Co-LAEEBA, EEDLABA, and EESBSN, across all evaluated scenarios, including PDR, throughput, energy consumption (%), and residual energy (J), has shown outstanding performance. Specifically, IDEALS achieves optimal energy efficiency while maintaining high throughput and PDR, without significant trade-offs in performance. This demonstrates that IDEALS can effectively balance energy savings and other key metrics. With new simulations, we’ve further highlighted this aspect in emphasizing IDEALS’ robustness in varying healthcare contexts, ensuring reliable performance without compromising energy efficiency. Let’s break this down into simpler terms.

Fig. 11

The scalability of the IDEALS scheme is demonstrated by analyzing its performance with up to 20 nodes. The graph shows how runtime and memory usage scale with increasing sensor nodes, providing insight into IDEALS scalability and efficiency in larger IoMT networks.

We can use some of the metrics to evaluate how well IDEALS works. These numbers include metrics such as PL, BER, PER, and MSE. Each of these tells us something important about how well IDEALS does its job compared to other systems. By lowering these numbers, IDEALS facilitates the smoother transmission of medical data in the IoMT. This is a big deal! If these numbers are lower, there is less data loss or distortion. So, when you send information about patient care, it arrives correctly. In short, IDEALS is a strong choice for healthcare. It ensures that important medical information is sent quickly and accurately. This is crucial for doctors and hospitals that need reliable data to care for their patients.

Energy-performance trade-off, reliability, comparative advantage, relevance and limitations

One of the critical goals of IDEALS is to achieve a balance between energy efficiency and communication reliability. The protocol demonstrates superior RE retention and extended NLT across all simulation rounds. This is attributed to the dual-layer synchronization and adaptive routing mechanisms that reduce redundant transmissions and enable efficient unicast/multicast communication. However, these energy-saving strategies do not compromise delay performance, as evidenced by the low EtoED results, making IDEALS suitable for time-sensitive medical applications. IDEALS outperforms existing methods in terms of PDR, ensuring reliable data delivery even in the presence of node mobility and dynamic signal conditions. Its integration of RSSI thresholds and PL-aware routing allows it to maintain stable links, effectively mitigating packet drops and disconnections common in body-centric networks. This resilience is critical in real-time health monitoring scenarios, where continuous and accurate data delivery is paramount. Unlike CRPBA and HCEL, which suffer from higher delays due to limited path diversity, or WHOOPH and TSFIS-GWO, which exhibit energy inefficiency under dense node deployment, IDEALS incorporates a hybrid communication model with direct and multi-hop modes. It dynamically switches routes based on link quality and energy status, offering flexibility and robustness.

Furthermore, its synchronization model minimizes idle listening and overhearing, reducing protocol overhead compared to conventional MAC-based schemes. The enhanced performance of IDEALS across all evaluated metrics underlines its applicability in various real-world IoMT use cases, such as remote patient monitoring, emergency response systems, and immersive telehealth in metaverse-based healthcare systems. Its design aligns with the operational requirements of low-latency, energy-efficient, and highly reliable communication, which are critical in clinical settings. While the simulation environment validates the protocol’s efficiency, real-world deployments may present additional challenges such as unpredictable mobility patterns, interference from other wireless systems, and variations in human physiology. Future research will focus on implementing IDEALS on hardware testbeds with wearable devices and incorporating AI-based prediction models to enhance route stability and adaptivity in real-time.

Statistical validation of results

We’ve integrated statistical analysis to validate the performance improvements reported for our proposed IDEALS protocol compared to existing SOTA approaches. All evaluations were conducted via simulation, and to ensure robustness and reliability, we executed each simulation scenario 30 independent times using randomized initial conditions and varying sources. For key performance metrics, including PDR, NLT, RE, and BER, we report the mean, standard deviation, and paired t-test p-values. These statistical measures allow us to assess whether the observed performance differences between IDEALS and competing protocols are statistically significant or due to random variations. Table 5 presents the results of this statistical validation. We report the mean and standard deviation over 30 runs for each metric and method. The p-values from paired t-tests compare IDEALS against each baseline method, testing the null hypothesis that there is no significant difference in performance. Across all metrics and baselines, the p-values are well below 0.05, indicating that the performance improvements of IDEALS are statistically significant with 95% confidence. For example, in PDR, the IDEALS outperforms HEEMCCP (p ≈ 7.61e-22), SDWBAN (p ≈ 4.56e-24), QQAR (p ≈ 3.41e-28), and SEEDI (p ≈ 2.18e-26). In NLT, the IDEALS yields statistically longer node lifetimes than HEEMCCP (p ≈ 1.52e-25) and other SOTA protocols. Similar significance levels are observed in RE and BER, confirming that IDEALS conserves energy better and maintains more reliable transmission. These results underscore that IDEALS’s performance benefits are consistent across multiple runs and statistically validated.

Table 5 This table presents a statistical comparison of the IDEALS scheme against SOTA protocols across key performance metrics. The comparison is based on 30 simulation runs, providing a robust evaluation of each protocol’s performance consistency and reliability across multiple iterations.

Justification of baseline selection

The protocols included in our comparison, HEEMCCP, SD-WBAN, QQAR, SEEDI, LAEEBA, Co-LAEEBA, EEDLABA, and EESBSN, were carefully selected based on several key criteria. First, relevance to WBANs was a critical factor. All the selected protocols were specifically designed for WBANs or closely related low-power, short-range IoT environments, making them directly applicable to the context of the IDEALS protocol. Second, selecting these protocols reflects their representation of key routing strategies. For instance, energy-aware routing was emphasized by protocols such as HEEMCCP, EEDLABA, and EESBSN, all of which focus on conserving energy in resource-constrained networks. Additionally, SEEDI and QQAR, prioritizing reliable transmission and QoS, represented link-quality-based approaches. Protocols like SD-WBAN, LAEEBA, and Co-LAEEBA incorporate distance or topology-aware designs, optimizing communication efficiency based on spatial awareness. Lastly, protocols such as Co-LAEEBA and SEEDI utilized hybrid or composite metrics, which combine multiple factors, though they do not employ dynamic or adaptive weighting. Third, the selected protocols’ competitiveness in WBAN and energy-efficient IoT routing was also decisive. These protocols have been frequently cited in recent literature and are widely recognized as strong baseline approaches. For example, HEEMCCP and Co-LAEEBA are among the most benchmarked energy-efficient routing schemes in recent comparative studies.

Emphasis on IDEALS’ unique improvements IDEALS

To highlight the distinct contributions of IDEALS beyond its numerical improvements, we focus on the following key differentiators. First, unified multi-metric routing is a unique feature of IDEALS. While most baseline protocols optimize a subset of factors, IDEALS integrate distance, residual energy, and link stability into a single normalized, adaptive metric. This approach allows for a more comprehensive and efficient decision-making process. Second, context-aware adaptivity sets IDEALS apart from static-weight schemes. Unlike traditional methods that rely on fixed weights, IDEALS dynamically adjusts metric weights based on runtime network conditions, ensuring more resilient and efficient routing in various scenarios. This adaptability allows IDEALS to perform optimally across varying network topologies and conditions. Finally, IDEALS offers cross-layer awareness, a feature not in the baseline protocols. By leveraging MAC/PHY-layer information, such as signal strength and error rate, IDEALS enhances link stability estimation, improving its routing decisions. This layer-aware approach gives IDEALS a significant advantage over traditional protocols, which typically operate without incorporating such detailed information. Based on the simulation results of the IDEALS protocol for IoMT compared to other SOTA protocols, we can compute the mean and standard deviation (± std) of IDEALS’ improvements in each metric. Table 6 provides a detailed justification for the impact and interplay between each metric in healthcare IoT.

Table 6 Presents the mean (%) & ± STD summary of the improvements achieved by IDEALS compared to SOTA protocols. It highlights the variability and consistency of IDEALS’ performance across key metrics, providing insight into the robustness of its improvements over the baseline protocols.

Impact and interdependence of each metric in IoMT

The proposed IDEALS protocol introduces a synergistic routing mechanism tailored for the IoMT, where reliability, latency, energy efficiency, and resilience to mobility-induced dynamics are paramount. The following analysis details the interrelationship between key performance metrics and the rationale behind the performance superiority of IDEALS. The PDR is a critical metric for measuring data reliability, particularly in healthcare settings where data loss can directly impact patient safety. An elevated PDR signifies the successful transmission of medical data across the network. PDR inherently depends on lower BER and PER, since reduced bit or packet corruption enhances overall delivery success. PDR also benefits from increased link stability and minimized path loss, ensuring sustained and interference-resilient transmission. The IDEALS protocol leverages dynamic, energy-aware link selection and predictive stability algorithms to maintain high PDR under varying conditions. Low latency is essential for real-time healthcare applications such as remotely monitoring cardiac signals or insulin levels. End-to-End Delay reflects the time a data packet travels from source to destination. A reduced EtoED contributes to increased PDR and improved CCR, ensuring timely and accurate decision-making at the sink or medical data server. However, optimizing delay must be balanced with energy constraints, as faster transmissions can incur higher energy costs. IDEALS achieves this trade-off effectively by utilizing proactive route selection and early detection of potential link failures to prevent retransmissions and timeouts. Throughput indicates the rate at which useful medical data is transmitted across the network, which is vital for applications such as streaming biosignals or transmitting large diagnostic files. Throughput improves when the network maintains high PDR, efficient energy usage, and reduced BER. Moreover, reduced path loss enhances signal strength, contributing to throughput improvements. IDEALS enhances throughput through intelligent load balancing and adaptive routing, prioritizing bandwidth-efficient paths with high link quality. Maximizing network lifetime is crucial in IoMT, where many sensor nodes are battery-powered and not easily replaceable in clinical or remote settings. Network lifetime reflects the duration for which the majority of nodes remain operational. NLT is closely linked to residual energy levels and the overall efficiency of energy consumption strategies. Lower end-to-end delays and reduced retransmissions conserve battery life. IDEALS extends network longevity by ensuring balanced energy consumption among nodes, thereby preventing early node depletion and maintaining network connectivity. Residual energy quantifies the remaining battery power of sensor nodes. High residual energy across the network implies efficient energy utilization and longer operational periods. Higher RE supports longer network lifetime and contributes to consistent data delivery and classification accuracy. The IDEALS protocol incorporates energy-aware metric weighting and avoids overburdening specific nodes (especially central nodes), thereby maintaining uniform energy expenditure across the network. Path Loss measures the attenuation of signal power during transmission. High path loss leads to signal degradation, packet loss, and elevated BER. Lower PL correlates with enhanced PDR, improved throughput, and energy efficiency, as fewer retransmissions are needed and signal integrity is preserved. IDEALS reduces PL by dynamically selecting routes with favorable propagation conditions, especially in mobile and indoor healthcare environments where signal obstructions are common. MSE serves as a measure of prediction accuracy in modeling link stability or signal behavior. Lower MSE indicates better predictive capability and signal estimation. Inaccurate predictions (high MSE) result in misrouting, increased retransmissions, and ultimately higher BER and PER. IDEALS minimizes MSE and enhances network responsiveness to mobility and interference by integrating machine learning-style filtering for link stability estimation. The BER indicates the frequency of bit-level transmission errors, which directly impacts the integrity of medical data. High BER leads to elevated packet error rates and reduced PDR, which may trigger retransmissions and longer delays. IDEALS maintains low BER by selecting routes with high Signal-to-Noise Ratio (SNR), improving reliability even in dynamically changing environments. PER reflects the proportion of packets that are received incorrectly. High PER undermines overall network reliability and throughput. Lower PER is a direct consequence of reduced BER and effective interference management. The IDEALS protocol minimizes PER by incorporating stability-based routing and employing interference-aware link selection to avoid congested or noisy channels. CCR represents the protocol’s ability to correctly route and interpret network states, including node roles, anomalous conditions, or mobility patterns. In intelligent healthcare networks, CCR influences service quality, context-aware decision-making, and routing cost regarding workloads. Higher CCR is associated with improved security posture, lower MSE, and efficient routing decisions that avoid unnecessary data transmissions. IDEALS incorporates context-aware, decision-fusion algorithms that enhance CCR, making it well-suited for real-time, adaptive healthcare environments.

Conclusion

The IDEALS protocol is all about making the IoMT work better. It focuses on three main goals: saving energy, being aware of delays, and stabilizing connections. IDEALS brings together these ideas in a way that helps address big problems we face, like losing signal strength, running out of battery, or making sure our devices communicate properly. To make things even better, we used a multi-criteria decision-making approach. The IDEALS considers all these important factors to find the best way to keep everything running smoothly, no matter what challenges come up in the network. We’ve tested IDEALS through simulations, and the results are impressive. It showed clear improvements in key areas compared to other SOTA methods. This means it works well and can adapt to different situations. IDEALS is not limited to its focused regions; it can be used for many IoMT applications. Consider wearables that track health or technology for telemedicine - it fits right in. Even though IDEALS does a great job solving many problems, there’s still room for growth. Future work could explore how to integrate IDEALS with newer technologies. For example, machine learning could help us predict issues before they happen. And adding blockchain could make things even safer.

In short, IDEALS is set up to improve how medical devices communicate and function, but there’s always more to enhance its capabilities. This study underscores the potential of IDEALS as a transformative protocol for achieving energy-efficient and reliable communication in IoMT networks, paving the way for smarter and more resilient healthcare systems. We’ve developed a new protocol that boosts the IoMT’s efficiency. It helps save energy, reduces delays, and extends the network’s lifespan. Next, we aim to enhance this protocol’s functionality in larger setups and adapt it to IoMT configurations. This means we’ll look into using clever machine learning techniques to help with routing. We plan to add security features to keep data safe from breaches. We’ve done much testing with the IDEALS protocol and compared it to other well-known IoMT protocols. We ran tests under different network conditions to see how it stacks up. The results show that IDEALS performs better in many important areas. These include metrics such as PDR, throughput, EE, and reliability. Even though testing this in real-world settings and looking at how well it can scale up are crucial, we believe our current study gives a solid theoretical base. We’ve done thorough simulations to back up our ideas. This sets the stage for the practical use of our protocol in healthcare communication systems. With this trend, future work can include further validation and scalability analysis to fully assess the protocol’s applicability in dynamic, large-scale IoMT environments.