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Article

IEEE 802.15.6 and LoRaWAN for WBAN in Healthcare: A Comparative Study on Communication Efficiency and Energy Optimization

by
Soleen Jaladet Al-Sofi
1,2,*,
Salih Mustafa S. Atroshey
2 and
Ismail Amin Ali
1,*
1
Department of Electrical and Computer Engineering, College of Engineering, University of Duhok, Duhok 42001, Iraq
2
Department of Biomedical Engineering, College of Engineering, University of Duhok, Duhok 42001, Iraq
*
Authors to whom correspondence should be addressed.
Computers 2024, 13(12), 313; https://doi.org/10.3390/computers13120313
Submission received: 16 October 2024 / Revised: 18 November 2024 / Accepted: 21 November 2024 / Published: 26 November 2024
Browse Figures
Figure 1
<p>Bandwidth-range characteristics of different wireless technologies.</p> "> Figure 2
<p>Three-tier architecture for WBAN.</p> "> Figure 3
<p>Network topology of the IEEE 802.15.6 network.</p> "> Figure 4
<p>IEEE 802.15.6 flowchart using CSMA/CA.</p> "> Figure 5
<p>Network topology of LoRaWAN.</p> "> Figure 6
<p>Flowchart of the LoRaWAN transmitter module.</p> "> Figure 7
<p>Average throughput changes across the number of nodes.</p> "> Figure 8
<p>Average arrival rate changes across the number of nodes.</p> "> Figure 9
<p>Average delay changes across the number of nodes.</p> "> Figure 10
<p>Average energy consumption changes across the number of nodes.</p> "> Figure 11
<p>Average packet delivery ratio changes across the number of nodes.</p> "> Figure 12
<p>Average network lifetime changes across the number of nodes.</p> ">
Versions Notes

Abstract

:
Wireless body area networks (WBANs), which continually gather and transmit patient health data in real time, are essential for improving healthcare administration. Patient outcomes can be improved by sending these data to medical professionals for prompt review and treatment. For the effective deployment of WBANs, communication solutions are necessary to maximize critical performance parameters, such as low power consumption, minimal delay, and acceptable data rates, while guaranteeing dependable transmission. Two prominent technologies in this field are LoRaWAN, which is renowned for its long-range capabilities and energy efficiency, and IEEE 802.15.6, which was created especially for short-range medical applications with high data throughput. This study provides a comparative evaluation of these two technologies to determine their suitability for diverse WBAN healthcare scenarios. By using the NS3, a simulation was performed to calculate six key performance metrics: throughput, arrival rate, delay, energy consumption, packet delivery ratio (PDR), and network lifetime. The study analyzed each technology’s performance under varying node counts. At a density of 50 nodes, IEEE 802.15.6 demonstrated superior throughput, with 45 kbps, compared to LoRaWAN, and a higher PDR of 30%. Additionally, IEEE 802.15.6 showed a higher arrival rate, of 0.33%, than LoRaWAN. On the other hand, LoRaWAN showed notable strengths in energy consumption, with only 42 J, compared to IEEE 802.15.6, and significantly lower delay, with a delay of 7 s. Additionally, LoRaWAN offered an extended network lifetime, of 18 h, compared to IEEE 802.15.6.

1. Introduction

The proliferation of miniature sensors and the growing use of wireless networks are fostering the development of network applications that can be used to provide a wide range of services for the human body. A wireless sensor network (WSN) that combines various smart devices, including sensors, actuators, and nodes, is called a wireless body area network, or WBAN. In this context, the uses of WBAN include a wide range of application fields, such as the medical field, the military field, sports, and multimedia. The researchers were motivated to explore the potential of WBANs for healthcare applications, especially in real-time health monitoring and management of chronic conditions. WBANs, which use miniature sensors for continuous health data collection, are becoming increasingly important in healthcare, as they enable real-time health tracking and timely responses in critical situations, including pandemics such as COVID-19. The rapid expansion of Internet of Things (IoT) technologies further emphasizes the need for effective, energy-efficient, and reliable wireless communication systems for healthcare purposes [1].
IoT networks work to prepare the sensors and devices for collecting and analyzing data in real time for making decisions and managing resources in transportation, environment, and healthcare [2]. WBANs play an important part by using wearable or implantable sensors to gather data on health, which significantly benefits public and personal health management [3]. The use of body sensor networks allows for observing health in real time and enhancing the response to emergency and health-focused environments. Energy is limited in sensor nodes, which makes energy consumption an important factor in the network lifetime. Sensors, such as motion sensors, are linked to patients to observe vital signs and movement for enabling an immediate response and effective patient management [4]. The variety of sensors specifically developed for IoT applications is growing quickly, thereby enhancing the effectiveness of using WBAN in several domains. Moreover, the valuable information provided by WBANs from individuals operating in the industry cannot be acquired without wireless connectivity. The increase in WBAN usage will inevitably lead to a corresponding increase in network problems. It is imperative to resolve these difficulties in order to optimize the benefits of the future WBAN. Given that WBANs produce and transmit personal biometric data, adequate network security measures are crucial [5]. Additional significant concerns include the power management of WBANs, the creation of new sensors with minimal power consumption, and the optimization of network parameters to achieve optimal power management [6].
The main obstacles of WBANs that technologies must overcome are the following: long-range connectivity, low delay, dependable data transfer, and energy efficiency. These problems are critical in remote patient care and real-time health monitoring, because sensors must run over extended periods of time on a small amount of energy.
The WBANs’ effectiveness significantly depends on their communication protocols for tasks to ensure reliable, secure, and efficient data transmission. Various protocols offer unique features for specific needs. IEEE 802.15.6 [7] is prominent in energy efficiency, data security, and high-rate communications, which is ideal for sensitive healthcare applications. Conversely, LoRaWANs [8] offer long-range coverage and low power consumption capabilities, making them suitable for observing patients in remote areas and scenarios that require connectivity to sensors at long distances. Bluetooth [9], ZigBee [10], and Z-Wave [11] technologies are often involved in connecting network devices. However, these technologies may face challenges in energy consumption, cost, and complexity, especially in large-distance areas with many devices because of complex multi-hop routing in hard conditions. Low-power wide-area network (LPWAN) technologies offer a solution, enabling network applications to operate over large areas and long distances [12]. Key LPWAN technologies, such as LoRaWAN, SigFox, Weightless, WAVIoT, and Wi-Fi HaLow, compete in the expanding IoT market, expected to reach USD 65 billion by 2025, constituting 20% of the LPWAN market [13]. Figure 1 compares LPWAN with other wireless technologies in bandwidth and range.
In recent years, new networking technologies have increased, exciting research interest and a growing need for real-world evaluation of their pros and cons. The communication architecture of WBAN, essential in this context, is depicted in Figure 2 and comprises three tiers: Tier-1 (inside WBAN), involving node-to-coordinator communication, Tier-2 (also inside WBAN), covering coordinator-to-access points’ communication, and Tier-3 (outside WBAN), linking Tier-2 to external networks [14]. WBANs employ nodes positioned all throughout the body, with the body central unit (BCU) serving as the hub for communication between the external networks and the sensors. Sensors record physiological data, such as temperature, blood pressure, glucose levels, electrocardiogram (ECG), electromyography (EMG), and electroencephalograph (EEG). For examination and analysis, the BCU transmits these signals to a hospital’s server [15]. Historically, WBANs utilized standards such as IEEE 802.11 (WiFi), IEEE 802.15.1 (Bluetooth), and IEEE 802.15.4 (Zigbee), but these proved insufficient for WBAN requirements [16]. To address this, the IEEE 802.15 Task Group 6 developed a new standard, IEEE 802.15.6. While IEEE 802.15.4 is tailored for low-rate, low-power, cost-effective communication within wireless personal area networks (WPANs), IEEE 802.15.6 is the first global standard specifically for WBANs. It focuses on low-power, short-range communication, placing nodes on or within the human body for real-time monitoring and physiological data collection to assess health conditions.
The deployment of these networks requires addressing challenges associated with the trade-off between power consumption, data rates, and network performance. IEEE 802.15.6 and LoRaWAN technologies have unique benefits and constraints. Despite increased power consumption, IEEE 802.15.6 offers relatively high data rates and low latency, which are crucial for real-time monitoring. Conversely, LoRaWAN offers greater range and reduced battery consumption, which is advantageous for health management in the long term but may affect data transmission speed and latency.
Hyper MACs are designed to optimize the efficiency of data transmission in wireless networks, particularly in resource-constrained environments, such as WBANs. They accomplish this by minimizing collisions, cutting down on idle listening, and dynamically modifying transmission schedules, all of which are essential for striking a balance between energy consumption and network performance. The hyper MAC improves the network’s adaptability and scalability in WBAN applications, where low power consumption, low latency, and dependable data delivery are crucial for real-time health monitoring. WBANs can accomplish greater data throughput and longer network lifetimes by utilizing hyper MAC, which qualifies them for a range of healthcare applications.
Prior research on WBANs has examined a number of topics, including network performance and data transmission, but it frequently concentrates on discrete elements, such as throughput, power consumption, or network range. It can be difficult to strike a balance between data transmission efficiency, energy consumption, and range, particularly in healthcare contexts, where extended operating lives and high data rates are crucial. Existing technologies, such as Bluetooth, ZigBee, and IEEE 802.15.4, have demonstrated drawbacks, such as inadequate coverage and power inefficiency. Although newer protocols, such as IEEE 802.15.6 and LoRaWAN, provide intriguing possibilities, further research is needed to determine how well they apply to various healthcare settings, such as real-time monitoring vs. long-term health management.
The objective of this study is to apply these technologies that contain hyper MACs and evaluate them by using performance characteristics in order to choose the most suitable technology for different healthcare situations, facilitating efficient implementation and recommending the most effective technology for different healthcare scenarios, such as real-time monitoring and long-term health monitoring. A thorough evaluation of critical performance metrics is carried out under different node densities, including throughput, delay, power consumption, packet delivery ratio, and network lifetime. With the help of this analysis, it will be possible to thoroughly assess the advantages and disadvantages of each technology and make well-informed recommendations about which one to use in various healthcare situations. Additionally, it will address critical issues related to real-time monitoring and the management of chronic health conditions. These observations are essential for increasing WBAN adoption and enhancing the provision of healthcare services.
This study contributes to the field by providing a comparison of IEEE 802.15.6 and LoRaWAN protocols for WBANs in healthcare. It evaluates these technologies using network performance metrics. The research fills gaps in previous studies by considering healthcare scenarios and the trade-offs between the IEEE 802.15.6 and LoRaWAN technologies in terms of power efficiency, data rates, and network performance. This paper’s findings can guide the selection of the most suitable protocol for different healthcare applications, contributing to more effective deployment of WBANs in medical environments.
This paper involves a comprehensive comparative analysis of IEEE 802.15.6 and LoRaWAN technologies for WBANs in healthcare applications, utilizing hyper MAC. Using a set of six crucial performance metrics—throughput, arrival rate, delay, energy consumption, PDR, and network lifetime—this study offers a thorough assessment of both technologies, in contrast to previous research that frequently concentrates on either general performance metrics or specific features of WBAN technologies. IEEE 802.15.6 is suitable for rapidly transmitting critical health data, such as in emergency scenarios, because of its high data throughput and low latency. However, its increased energy consumption can be a drawback for prolonged use. Conversely, LoRaWAN offers low-power, long-range connectivity that is ideal for remote and prolonged monitoring, and it places an emphasis on energy efficiency over data speed.
The organization of this paper is as follows. Section 2 discusses the research about health analysis using IEEE 802.15.6 and LoRaWAN. Section 3 outlines the experimental setup, detailing the MAC, IEEE 802.15.6, and LoRaWAN technologies, and performance metrics used in WBAN environments. Section 4 presents a detailed analysis of the comparative performance of IEEE 802.15.6 and LoRaWAN technologies, examining key network metrics. The section also discusses the trade-offs between the two technologies, offering insights into their suitability for different healthcare applications. Section 5 presents the conclusions of our paper.

2. Related Work

Choosing the appropriate communication technologies and protocols that can maximize power efficiency, data transmission rates, and delay is crucial to WBAN efficacy. Technologies such as IEEE 802.15.6, which was specifically made for low-power, short-range body area networks, and LoRaWAN, which is well known for its long-range, low-power capabilities appropriate for broader healthcare monitoring situations, are among the most extensively researched in this field. Improving patient outcomes and system efficiency requires an understanding of the advantages and disadvantages of these technologies in diverse healthcare applications. In order to assess their performance across several criteria, such as energy consumption, data rate, and network scalability, researchers have carried out thorough comparative evaluations.
In WBAN devices, the sensors are powered by small batteries. Some of these batteries, especially those in embedded sensors, cannot be changed or must operate for years without being charged because doing so would be too painful [17]. So, WBAN’s goal is to have a power supply that works well while using very small batteries. For the power system to work best, a WBAN wireless method should be chosen that consumes less power. The time it takes for data to move between devices is referred to as latency. Low delay guarantees that real-time data are successfully transmitted to the medical center. This is crucial for medical applications, particularly in emergency scenarios where a patient’s death could result from delayed data delivery. Certain medical WBAN applications require real-time transmission with guaranteed performance because the data are sensitive and cannot tolerate a long response time. Real-time sensors’ WBAN applications need to detect the feedback and transmit it right away to the medical staff so that they can process the data while meeting the delay time requirements. Wireless technologies enable the transmission of data gathered by sensors, therefore obviating the necessity for physical connections between the sensor and the access point. Optimal selection of wireless technology is crucial in WBAN systems, since an unsuitable technology can lead to inefficiency and energy wastage.
Technologies that can efficiently manage health in real time are urgently needed due to the increasing frequency of chronic diseases and the aging population. Improvements in patient outcomes and healthcare efficiency can be achieved through advancements in WBANs. Therefore, researchers are constantly working to develop these technologies so that they can better handle various healthcare settings and overcome performance issues. Research into WBAN improvement has been extensive, and studies evaluating network performance have employed a wide variety of methodologies. Zaouiat et al. [18] analyzed the performance of IEEE 802.15.4 and IEEE 802.15.6 MAC protocols in medical applications, focusing particularly on the data rates of medical sensors. Huang et al. [19] highlighted key differences in MAC features between these standards, conducting a comparative analysis of MAC format and access mechanisms. They emphasized the advantages of IEEE 802.15.6 in body area network (BAN) communications, especially regarding frequency, data rate, and range. Further, Rabarijaona et al. [20] introduced the IEEE 802.15.10 standard, a Layer 2 routing protocol that enables multi-hop communication in IEEE 802.15.4 networks. This standard was designed to extend the coverage area of IEEE 802.15.4 networks, focusing on efficient, low-energy transport, and applies to scenarios such as smart meters and intelligent transportation systems. A detailed analytical model for WBANs was introduced by Benmansour et al. [21] based on the IEEE 802.15.6 standard. This model uniquely accounts for heterogeneous traffic, acknowledging different data types with varying priority levels. Initially, an analytical sub-model for the CSMA/CA backoff process of IEEE 802.15.6 was developed using the renewal reward process of Kulkarni [22]. The model then proposed a non-preemptive priority M/G/1 queuing model to manage emergency traffic in MAC queues. Verified through extensive simulations with the Castalia Simulator [23], the model’s performance results align well with the QoS objectives of IEEE 802.15.6. The findings highlighted that prioritizing emergency traffic improves the delivery speed and success rate but may negatively impact non-emergency traffic. According to Raza et al. [24], the design of LoRaWAN is shown to heavily depend on network performance and scalability, influenced mainly by the choice of radio parameters and environmental conditions. Various studies have focused on the challenge of selecting optimal parameters for LoRaWAN, aiming to analyze and compare their performance and scalability. LoRaWAN technologies are deployed in license-exempt bands, such as TV-white areas or the industrial, scientific, and medical (ISM) band. Yousuf et al. [25] investigated the effect of indoor and urban outdoor environments on LoRaWAN signal performance. The study revealed minimal packet loss within a seven-floor building’s internal network and variable outdoor coverage depending on the environment. In a test covering 4.4 km, a 15% packet drop rate was noted, and packet size was found to impact the signal range. The authors’ findings for LoRaWAN over the unlicensed ISM band at 915 MHz were shown for a variety of network configurations in both indoor and outdoor settings. Mekki et al. [26] offered a comparative overview of Sigfox, LoRaWAN, and NB-IoT technologies and highlighted their advantages. Sigfox and LoRaWAN were distinguished for cost-effectiveness, long range, infrequent connection intervals, and extended battery life. Also, LoRaWAN was distinguished for supporting local network deployment and maintaining reliable connectivity even in high-speed networks. According to Ertürk et al. [27], LoRaWAN’s open design and the ability for private installations (operating independently of third parties) are significant advantages. The authors’ work focused on developing an e-Health device to test if LoRaWAN was the right solution for the e-Health application. Fraile et al. [28] compared the use of IEEE 802.15.4 and LoRaWAN in indoor network deployments for European school buildings. While these technologies might not seem directly competitive, they have distinct pros and cons in their application areas. The effectiveness of LoRaWAN and IEEE 802.15.6 technologies in WBAN was evaluated in [29], and the results showed that IEEE 802.15.6 was superior in terms of throughput, PDR rate, and access rate, while LoRaWAN was superior in terms of power consumption, residual power, network lifetime, and delay.
WBANs depend significantly on energy-efficient communication protocols to optimize efficiency and battery life. These networks operate with sensors that gather and send health-related data, necessitating an ideal equilibrium between power consumption and data transmission performance [30]. These protocols are specifically developed to prolong the performance lifetime of devices, therefore decreasing the need for frequent battery replacement or recharging. This is crucial for WBANs, as many devices are either integrated into the body or worn on the skin, which hampers the ease and convenience of regular maintenance. In addition to solving issues pertaining to data transfer dependability and network performance, the effectiveness of these protocols also encompasses various other aspects. Achieving precise and rapid data transmission while reducing battery consumption necessitates meticulous protocol design and execution. This entails the management of any interference, the optimization of data rates, and the preservation of network connectivity, even in contexts that may be dynamic and busy.
Studies have provided valuable insights into the performance of different communication protocols for WBANs, as shown in Table 1. Despite these contributions, there are still gaps in a comprehensive analysis of energy-saving technologies, especially regarding their performance in diverse environments and real-time monitoring scenarios. Our results can help stakeholders in choosing the suitable protocol in a specific requirement and enhancing the deployment of wireless communication technologies in healthcare.
As it involves human lives, research in remote patient monitoring (RPM) is regarded as one of the most important areas of healthcare. Since the start of the pandemic, RPM use has rapidly expanded. Despite the growth of these systems, there are still certain obstacles to overcome, including mobility, heterogeneous networks, RPM standardization, automation, and quality of service (QoS). Boikanyo, K. et al. [31] talked about the applications, architecture, and difficulties of RPM systems for physiological parameter monitoring.
Artificial intelligence (AI) use in healthcare is expanding quickly. RPM is a popular healthcare program that helps physicians keep an eye on patients who are hospitalized, elderly patients receiving in-home care, and patients with acute or chronic illnesses who are located in remote areas. Staff time management, which is based on their workload, determines how reliable manual patient monitoring methods are. Conventional patient monitoring involves invasive approaches, which require skin contact to monitor the health status. A thorough analysis of RPM systems, including adopted sophisticated technologies, the impact of AI on RPM, and the difficulties and developments in AI-enabled RPM, was conducted by Shaik, T. et al. [32].
With features such as remote critical care and real-time therapy, the combination of the IoT and the Internet of Medical Things (IoMT) has completely changed patient care. Abbas, T. et al. [33] investigated this integration in response to the evolving healthcare environment, where heightened connectivity of medical devices poses security risks that necessitate a comprehensive evaluation. The study categorized existing IoT communication devices, looked at their uses in IoMT, and explored significant facets of IoMT devices in healthcare, with a particular focus on resolving security and performance enhancement issues.

3. Materials and Methods

Multiple-access medium-access control (MAC) protocols have crucial roles in managing access to the transmission media to achieve efficient data transmission in wireless networks. Different multiple-access protocols stand out with their distinct approaches to managing network access and collision avoidance. Each protocol offers a unique technique for ensuring facilitation of communication between biosensor devices while addressing challenges, such as collision management and prioritization of traffic types. Examples of these protocols include:
  • ALOHA: It is one of the simplest methods for managing how devices send data over a shared network. It allows each device to send its data whenever it needs to, without coordination with others. If two devices send data at the same time, a collision can occur, and the data must be resent. To address the potential of collision issues, it uses an acknowledgment or a retransmission policy. When an ALOHA device has a new packet for sending, it just sends it right away. After that, the ALOHA device listens to the channel to determine whether the receiver received the packet successfully. When an acknowledgment is received, the sender assumes the packed has been successfully delivered. The sender interprets the absence of an acknowledged packet as evidence that a collision has happened. This simplicity makes ALOHA easy to implement but can lead to inefficiencies as more devices join the network.
  • Carrier sense multiple access with collision avoidance (CSMA/CA): CSMA/CA is a more advanced method that tries to prevent collisions before they happen. Devices first check if the channel is free before sending data. If it is busy, they wait and try again after a short, randomly determined period. In the IEEE 802.15.6 standard, a node uses CSMA/CA-based random access to obtain a contended allocation. To distinguish between various traffic types and provide different services, the CSMA/CA protocol assigns varying priorities. The node tracks a backoff counter and a contention window to manage new contested allocations. It initializes the backoff counter with a random number based on its priority. Nodes assign a user priority to each frame according to the traffic type. For every unused CSMA slot, the node decrements the backoff counter by one. The node transmits the frame when the backoff counter reaches zero. If the channel is busy due to other nodes’ transmissions, the node keeps its backoff counter locked until the channel is idle [34]. When the number of these devices increases, carrier sensing loses its ability to accurately detect ongoing transmissions and becomes more costly, which has a detrimental effect on network performance [35].
  • Time division multiple access (TDMA): TDMA divides the communication channel into time slots and assigns each device a specific slot to send its data. This ensures that only one device transmits at a time, completely avoiding collisions. Nodes can be allotted multiple time slots based on their requirements and data volume. To ensure nodes transmit packets during their designated time slots, synchronization using a distinct control packet is necessary. TDMA is suitable for WBANs with a limited number of nodes. However, the main challenge in WBANs using TDMA is allocating time slots to nodes with varying data rates, no periodic data, and scalability [36].
In this section, we setup two scenarios to simulate the LoRaWAN and IEEE 802.15.6 networks. NS3 [37] was used as a simulation tool because of its ability to model the behaviors and interactions of numerous technologies under a variety of settings.

3.1. IEEE 802.15.6 Network

To simulate the IEEE 802.15.6 protocol in NS3, the IEEE 802.15.4 [38] module was used as a foundation and modified, particularly in the MAC layer, to mimic IEEE 802.15.6 features using the CSMA/CA protocol. Integrating energy models allowed for examining energy usage, which is crucial for wearable and implantable devices in body area networks.

3.1.1. IEEE 802.15.6 Network Topology

IEEE 802.15.6 is a communication protocol specifically developed for WBANs, consisting of devices located on, near, or inside the human body. These networks support various applications, such as medical monitoring. The standard establishes a systematic structure for effective, secure, and reliable information transmission close to or within the human body [39]. Its network topology focuses on energy-efficient, short-range communications designed for medical, healthcare, and fitness purposes. Figure 3 depicts the network topology of the IEEE 802.15.6 standard. Sensor nodes can be generally classified into three types: (1) implantable devices that are used to monitor and/or treat health conditions, (2) wearable gadgets, such as fitness trackers, smartwatches, and other medical sensors, and (3) smartphones deployed as data aggregators or controllers are external devices. The WBAN’s major communication hub is the BAN coordinator, which organizes the network, controls medium access, and secures data. The hub has the ability to collect data from all nodes for processing, storage, or transmission to the Internet or a LAN. The primary drawbacks of the IEEE802.15.6 structure include contention-based channel distribution to bio-methodical sensor nodes, independent of the urgency or non-emergency nature of the data [40]. However, there is no distribution of life-critical suitable data slots, nor is there distinction based on low or high emergency data threshold values [41].
The IEEE 802.15.6 flowchart using CSMA/CA is displayed in Figure 4. A sender node must wait a certain amount of time before attempting to retransmit if it senses channel congestion. It keeps an eye on the channel throughout this period to see whether it opens up. IEEE 802.15.6 controls this process with a contention window (CW) and a back-off counter (BC). The BC is randomly set between 1 and CW, while the CW varies from CWmin to CWmax. For packet transmission, CW is set to CWmin if it is the node’s first time or if no acknowledgment is required. Unless it reaches CWmax, CW stays the same following an odd-numbered failure and doubles following an even-numbered failure if a transmission fails or no acknowledgment is received. To minimize delays and lower the likelihood of collisions, CW is capped to CWmax [42].

3.1.2. Network Setup for IEEE 802.15.6

The NS3 simulation environment was used to setup the configuration of the IEEE 802.15.6 network and enable efficient communication. The NS3 simulator is an open-source, discrete-event network simulator aimed at supporting research and training in network simulation. It provides a flexible environment for studying network protocols and system behavior, especially useful for controlled experiments that are challenging to perform on real systems. Unlike some simulation tools with integrated GUIs, such as OMNET++, NS-2, OPNET, and JiST, NS3 is built as a set of modular libraries that can be combined with other software. It focuses on modeling Internet and network protocols but can also be used for non-Internet-based models, making it versatile for various network studies [43]. Each node in the simulation has a hyper MAC, which consists of two different MACs. The first MAC layer is embedded into the node’s first Net Device, which uses the CSMA/CA protocol. At the same time, the second MAC layer is embedded into the second Net Device of the same node, which uses the TDMA MAC protocol. By using the hyper MAC, nodes can dynamically modify their access strategies in response to network conditions to increase efficiency and lower collision rates. The simulation also implemented the low-energy adaptive clustering hierarchy (LEACH) routing protocol, which divides energy usage evenly across cluster heads by scheduling nodes into clusters for data transmission [44]. The simulated network consisted of 1 gateway and a maximum of 50 nodes. A variety of real-world healthcare scenarios, where the number of connected WBAN devices might change based on patient monitoring needs, were reflected in the selection of different node counts to assess the network’s performance under various load levels. In order to ensure that the simulation closely resembles real wireless communication circumstances, the logarithm distance propagation loss model was utilized to replicate the realistic signal attenuation encountered in healthcare facilities, such as hospitals or home care settings. The nodes were moving in a randomly dispersed position in an area of 200 m × 200 m. The gateway stayed in constant position as a central location for receiving packets from the nodes. During the simulation period, 10,050 packets were transmitted, with each node assigned a fixed number of packets for transmission via CSMA/CA and TDMA. While the CSMA/CA-allocated packets represent less-essential data, such as temperature measurements, conveyed within the WBAN to the gateway, the TDMA-allocated packets imitate critical data, such electrocardiogram (ECG) data. In addition, every Net Device has a 250 J energy allotment to maintain its operational functions, with TDMA and CSMA/CA transmission consuming 0.028 A, reception consuming 0.0112 A, and idle-mode consuming 0.0013 A. The simulation runs for a total of 1000 s, allowing for thorough investigation and assessment of network performance under various workloads and situations.

3.2. LoRaWAN

A LoRaWAN was simulated to demonstrate how to model and analyze energy consumption using the NS3 network simulator.

3.2.1. LoRaWAN Topology

LoRaWAN topology is suitable for devices that are required to transmit small amounts of data over large distances. It provides long-range, low-power wireless communications with wide area coverage [45]. The star topology for LoRaWANs is typical. This structure has several important parts, as shown in Figure 5. End devices, or nodes, are sensors or actuators that collect data and follow commands. These battery-powered devices can survive years on a single charge due to their low energy usage. They use LoRaWAN modulation to wirelessly connect with gateways. Gateways bridge messages from various end devices to a network server. Gateways are powered and connected to the Internet via Ethernet, cellular networks, or other means, unlike end devices. Depending on the environment and capability, one gateway can communicate with thousands of end devices. Gateways are transparent and do not decode messages. Network management is centralized on the network server. This includes deduplicating messages from different gateways, confirming message integrity and authenticity, managing join requests from new devices, and routing messages to the right application server. Each end device’s data rate and RF power are optimized by the network server to maximize battery life and network capacity. Data from end devices are processed and analyzed by application servers. The business logic transforms raw data into actionable insights, choices, and actions that can be communicated to end devices or external systems. Application servers are usually tailored to the LoRaWAN use case.
Figure 6 illustrates the flowchart of the LoRaWAN transmitter module before transferring the packets to the receiver. The transmitter unit goes into sleep mode when there is no close receiver of the same frequency. But as the receiver is switched on, the transmitter quickly begins to send packets [46].

3.2.2. Network Setup for LoRaWAN

Each node in the LoRaWAN has a hyper MAC, which are TDMA and ALOHA MAC. The two MACs are allocated to each node, where the first MAC is allocated in the first Net Device and the second MAC is allocated in the second Net Device to enable flexible access and scheduling procedures. The access based on contention in ALOHA is appropriate for sporadic and low-traffic situations, while the TDMA’s deterministic scheduling is good for time-sensitive applications. The LEACH routing protocol was implemented in the network to allow data aggregation and clustering to achieve energy efficiency. Through the periodic rotation of cluster heads, the LEACH protocol distributes the energy around the network to maximize the network lifetime. Moreover, the distribution of energy between all Net Devices is essential to save network stability and extend the node lifetime. Through these components, the network configuration attains balancing between energy efficiency, scalability, and performance, which are crucial aspects for the deployment of LoRaWAN in varied contexts. With a maximum of 50 nodes and 1 gateway, a channel with a logarithm distance propagation loss function is used. The gateway stays stationary and works as the sink data receiver. The nodes move in a 200 m × 200 m area in randomly distributed positions. A total of 10,043 packets are sent during the 1000 s simulation period. Every node sets aside a certain number of packets for TDMA transmission, which symbolizes vital information, such as ECG pictures, and another set-aside number for ALOHA transmission, which represents less vital information, such as temperature readings. A total of 250 J of energy is allotted to each Net Device, guaranteeing operational sustainability in the simulated network environment. The TDMA and ALOHA transmission consume 0.028 A, reception consumes 0.0112 A, TDMA in idle mode consumes 0.0013 A, and ALOHA in idle mode consumes 0.0000015 A. Table 2 shows the network parameters of IEEE 802.15.6 and LoRaWAN. Table 3 shows the network implementation for IEEE 802.15.6 and LoRaWAN.

4. Results and Discussion

In this section, the IEEE 802.15.6 and LoRaWAN were compared, and the respective abilities were checked using six metrics. The metrics of throughput, arrival rate, delay, energy consumption, PDR and network lifetime were calculated to determine each protocol’s reliability and effectiveness. To provide a more detailed understanding of the performance, simulations were performed for each protocol for a different number of nodes (10, 20, 30, 40, and 50 nodes) to determine the ability of the two protocols to adapt to the increase in nodes within the network. The results provide a fine-grained view of the adaptability and performance of each protocol as the nodes in the network increased. The evaluation was performed to provide information that will guide the best choice of the two protocols in practical settings.
The effectiveness of WBAN is essential for quick medical interventions and dependable patient monitoring in healthcare applications. Real-time health monitoring is made possible by the throughput and arrival rate, which guarantee effective data transfer. In emergency situations, where prompt reactions are required, delay is essential. For wearable and implantable devices to operate for extended periods of time without requiring frequent battery replacements, energy consumption and network lifetime are critical. Finally, the dependability of health data transmission is ensured by a high PDR, which guarantees that vital information reaches healthcare practitioners without loss.

4.1. Throughput

Throughput is a crucial parameter for assessing a network’s capacity. Throughput is important for applications that transmit large amounts of data, including wearable device video footage. Equation (1) is used to calculate the throughput in kbps. There was a discernible difference in scalability between IEEE 802.15.6 and LoRaWAN when comparing their throughput as the number of nodes rose, as shown in Figure 7. Our results across the number of nodes showed that IEEE 802.15.6’s throughput was higher than the LoRaWAN throughput. IEEE 802.15.6 focuses on short-range communications, where bandwidth is more efficient, hence its throughput is higher than LoRaWAN. At the 50-node point, IEEE 802.15.6’s throughput of 45 kbps significantly exceeded that of LoRaWAN of 37 kbps. This observation suggests that IEEE 802.15.6 offers a more reliable solution for managing data in larger networks, where maintaining high throughput is crucial for efficient operations.
T h r o u g h p u t = ∑ n = 1 T o t a l   p a c k e t s P a c k e t   S i z e × 8 ( P a c k e t   R e c e i v e   T i m e n − P a c k e t   S e n d   T i m e n ) × 1000

4.2. Arrival Rate

The arrival rate refers to the rate at which data packets or requests are generated and introduced into a network. It measures the frequency of new arrivals at network nodes or queues and is often used to characterize the traffic intensity or workload in a network. Managing arrival rates is essential for ensuring that network resources are adequately provisioned to handle incoming data and maintain desired performance levels. Equation (2) is used to calculate the arrival rate. As shown in Figure 8, our results for arrival rate showed that IEEE 802.15.6 exhibited a higher arrival rate percentage at each node count. This efficiency stood out, especially in bigger node setups. At 50 nodes, IEEE 802.15.6 had an arrival rate of 0.33%, while LoRaWAN had 0.25%. The arrival rate is important for applications that need high packet delivery dependability. This demonstrates the superiority of IEEE 802.15.6 due to its nature in maintaining reliable transmissions.
A r r i v a l   R a t e = ∑ t = 1 T o t a l   T i m e N u m b e r   o f   R e c e i v e d   P a c k e t s   a t   t i m e t t

4.3. Delay

Delay estimates the time it takes for a packet to traverse from the source node to the destination node, which is important for time-sensitive applications. Equation (3) is used to calculate the delay in seconds. As shown in Figure 9, our results for delay showed that IEEE 802.15.6 had a lower delay increase than LoRaWAN as the number of nodes grew. This indicates that LoRaWAN was able to provide low delay and a more stable experience as the number of nodes increased, which is good for time-sensitive applications. The big difference between the two protocols appeared in the 50 nodes, where LoRaWAN had a delay of 7 s, whereas IEEE 802.15.6 had 17 s.
D e l a y = ∑ n = 1 T o t a l   p a c k e t s P a c k e t   R e c e i v e   T i m e n − P a c k e t   S e n d   T i m e n

4.4. Energy Consumption

The LoRaWAN and IEEE 802.15.6 are designed for battery-powered devices that do not need to be recharged. Comparing energy consumption helps choose the optimal methodology for application with energy limitations. Equation (4) is used to calculate the energy consumption in joules. As shown in Figure 10, our results for energy consumption showed that IEEE 802.15.6 uses more energy than LoRaWAN, and the difference became bigger when more nodes were added. Consequently, IEEE 802.15.6 may provide better performance metrics, but at the expense of using more energy. This may be a big factor to take into account when deploying in areas with limited energy. LoRaWAN showed a slight gradual increase, starting with an average power consumption of 10 J at 10 nodes and rising to 42 J at 50 nodes. The IEEE 802.15.6 showed a significant increase compared to LoRaWAN, which started with an average power consumption of 18 J at 10 nodes and increased to 75 J at 50 nodes.
E n e r g y   C o n s u m p t i o n = P × T
where:
  • P: Energy consumed at Tx, Rx, or idle mode.
  • T: Time taken for Tx, Rx, or idle mode.
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Figure 10. Average energy consumption changes across the number of nodes.
Figure 10. Average energy consumption changes across the number of nodes.
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4.5. Packet Delivery Ratio

The PDR measures network packet delivery success. Critical applications require reliable communication with high PDR. Long-distance LoRaWANs confront signal attenuation and interference, whereas IEEE 802.15.6 networks may operate in or on the body, facing body shadowing and mobility. Different PDR situations might show a protocol’s robustness and reliability. Equation (5) is used to calculate the PDR in percentage. Our results in Figure 11 show that whereas the behavior of IEEE 802.15.6 experienced a decrease in PDR, LoRaWAN maintained a comparatively constant PDR across node counts, but the PDR values of LoRaWAN were less than IEEE 802.15.6. The stability of IEEE 802.15.6’s PDR raises the possibility that it will be more dependable for steady packet delivery, which is important for applications requiring high data integrity. On the other hand, IEEE 802.15.6 showed a greater PDR. At 50 nodes, IEEE 802.15.6 showed a PDR of 30%, while LoRaWAN showed a PDR of 24%.
P a c k e t   D e l i v e r y   R a t i o = N u m b e r   o f   R e c e i v e d   P a c k e t s T o t a l   P a c k e t s × 100 %

4.6. Network Lifetime

Network lifetime is the time until nodes fail due to energy exhaustion. It is essential for inaccessible deployments. The choice of protocol can greatly impact battery replacements and recharging cycles. Equation (6) is used to calculate the network lifetime in hours. Our results, as shown in Figure 12, indicated that LoRaWAN has a longer network lifetime than IEEE 802.15.6, especially as the number of nodes increased. This means that applications where long-term operation is desired may benefit more from the use of LoRaWAN. LoRaWAN showed a long network lifetime, as it showed a network lifetime of 37 h at 10 nodes, and as the number of nodes increased to 50, it decreased to 18 h, while IEEE 802.15.6 showed a network lifetime of 30 h at 10 nodes and decreased to 14 h at 50 nodes. Table 4 shows a summary of our results for the comparison of LoRaWAN and IEEE 802.15.6 protocols at 10 nodes and 50 nodes in terms of throughput, arrival rate, delay, energy consumption, packet delivery ratio, and network lifetime.
N e t w o r k   L i f e t i m e = I n i t i a l   E n e r g y 3600 × E n e r g y   C o n s u m p t i o n / S e c
The results point to important compromises between IEEE 802.15.6 and LoRaWAN. IEEE 802.15.6 offers greater throughput (45 kbps vs. 37 kbps at 50 nodes), albeit at the expense of greater energy usage (75 J vs. 42 J). Because of this, LoRaWAN is more suitable for energy-critical medical applications where battery life is crucial. At smaller sizes (0.1 vs. 2 s at 10 nodes), LoRaWAN performs better in terms of delays. While IEEE 802.15.6 is superior for high-throughput, real-time data transmission, LoRaWAN is also good for long-term monitoring since it increases the network lifetime (37 h vs. 30 h at 10 nodes). Thus, the choice between these technologies is contingent upon whether energy efficiency and a longer device lifetime (LoRaWAN) or data throughput and dependability (IEEE 802.15.6) are the top priorities.
There are useful ramifications for healthcare from the performance disparities between IEEE 802.15.6 and LoRaWAN. Emergency response systems that need to transmit data quickly and reliably would benefit greatly from IEEE 802.15.6’s high data speeds and low delay. LoRaWAN, on the other hand, is more appropriate for chronic condition monitoring, where sensors must function continuously for prolonged periods of time without regular battery changes, because of its great range and low energy usage.
Power constraints are a crucial factor to consider in design, particularly for implantable devices that are not readily rechargeable or replaceable. Optimizing energy efficiency is crucial in the design of communication protocols and network architecture to guarantee sustained operation in the long run. Exploration is underway to develop advanced energy-harvesting systems that utilize body heat or movement in order to prolong battery life and decrease reliance on external power sources. Efficient management of many data streams and reduction in access time and power consumption are imperative as the number of sensors in a WBAN grows. A fundamental problem is in designing scalable networks capable of accommodating varying node densities without sacrificing performance. The architecture of WBANs should aim to minimize packet loss and guarantee low-delay connectivity, even in noisy settings or through patient movement. Anticipated advancements in WBANs include its integration with 5G and next-generation wireless networks, facilitating accelerated data transmission and enhanced communication with cloud-based health monitoring systems. The exceptional low delay and vast bandwidth of 5G can significantly augment the capabilities of real-time monitoring.

5. Conclusions

This study provided a comparative analysis of IEEE 802.15.6 and LoRaWAN protocols in WBAN to understand their suitability for network applications. The performance evaluation by using throughput, arrival rate, delay, energy consumption, PDR, and network lifetime metrics showed the distinct advantages and disadvantages of the two protocols. IEEE 802.15.6 outperformed in throughput of 11 kbps at 10 nodes and 45 kbps at 50 nodes, PDR of 36% at 10 nodes and 30% at 50 nodes, and arrival rate of 0.07% at 10 nodes and 0.33% at 50 nodes. On the other hand, LoRaWAN outperformed in energy consumption of 10 J at 10 nodes and 42 J at 50 nodes, network lifetime of 37 h at 10 nodes and 18 h at 50 nodes, and delay of 0.1 s at 10 nodes and 7 s at 50 nodes. The findings showed that IEEE 802.15.6 offers superior throughput, PDR, and arrival rate, making it ideal for applications that require fast, real-time data transmission, such as healthcare monitoring. However, its higher energy consumption limits its use in long-range communication and battery-operated devices. In contrast, LoRaWAN excels in energy efficiency, network lifetime, and low delay, making it more suitable for applications that require extensive coverage and low power consumption, such as remote healthcare systems. Despite its lower PDR and arrival rate, LoRaWAN’s long-range communication capabilities make it ideal for use in areas with limited connectivity. It is crucial to remember that the performance review was carried out in a simulation setting, which might not accurately represent the intricacies of actual healthcare implementations. This study did not take into consideration variables such as signal obstruction from the human body, interference from other devices, and different ambient environments. These elements may have an impact on how well the protocols work in real-world medical settings. To confirm these results and offer a more thorough evaluation of protocol applicability for WBANs, more research is advised, including practical testing and analysis in a range of healthcare contexts. Additionally, the electromagnetic interference (EMI) effect on medical devices should be considered, as the accuracy and operation of delicate medical equipment in hospital settings may be impacted by EMI. Hospitals can utilize radiofrequency devices to track patients and machinery and turn off life-saving equipment. Dynamic and multi-source EMI has become an issue for nations due to the quick growth of wireless communication, intelligent detection, and other technical disciplines, as well as the creation and use of sophisticated intelligent equipment or systems. Future work might examine hybrid strategies that combine the advantages of LoRaWAN and IEEE 802.15.6 to provide a more well-rounded response to different healthcare requirements. The choice between the two protocols should consider the needs of the specific application, energy capacity, and network lifetime. This study aids in choosing the most appropriate protocol for effective wireless communication in WBAN scenarios.

Author Contributions

Conceptualization, S.J.A.-S. and I.A.A.; methodology, S.J.A.-S. and I.A.A.; software, S.J.A.-S. and I.A.A.; validation, S.M.S.A. and I.A.A.; supervision, S.M.S.A. and I.A.A.; sample preparation, S.J.A.-S.; writing, S.J.A.-S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Bandwidth-range characteristics of different wireless technologies.
Figure 1. Bandwidth-range characteristics of different wireless technologies.
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Figure 2. Three-tier architecture for WBAN.
Figure 2. Three-tier architecture for WBAN.
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Figure 3. Network topology of the IEEE 802.15.6 network.
Figure 3. Network topology of the IEEE 802.15.6 network.
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Figure 4. IEEE 802.15.6 flowchart using CSMA/CA.
Figure 4. IEEE 802.15.6 flowchart using CSMA/CA.
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Figure 5. Network topology of LoRaWAN.
Figure 5. Network topology of LoRaWAN.
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Figure 6. Flowchart of the LoRaWAN transmitter module.
Figure 6. Flowchart of the LoRaWAN transmitter module.
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Figure 7. Average throughput changes across the number of nodes.
Figure 7. Average throughput changes across the number of nodes.
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Figure 8. Average arrival rate changes across the number of nodes.
Figure 8. Average arrival rate changes across the number of nodes.
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Figure 9. Average delay changes across the number of nodes.
Figure 9. Average delay changes across the number of nodes.
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Figure 11. Average packet delivery ratio changes across the number of nodes.
Figure 11. Average packet delivery ratio changes across the number of nodes.
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Figure 12. Average network lifetime changes across the number of nodes.
Figure 12. Average network lifetime changes across the number of nodes.
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Table 1. A summary of works carried out on health analysis using IEEE 802.15.6 or LoRaWAN.
Table 1. A summary of works carried out on health analysis using IEEE 802.15.6 or LoRaWAN.
AuthorsTechnologyHealth-Related Analysis
Zaouiat et al. [18]IEEE 802.15.6Analyzes the performance of IEEE 802.15.6 MAC protocols in medical applications, focusing particularly on the data rates of medical sensors.
Huang et al. [19]IEEE 802.15.6Highlights IEEE 802.15.6’s advantages in BAN for healthcare, emphasizing the frequency, data rate, and range for medical communication.
Benmansour et al. [21]IEEE 802.15.6Proposes a model to prioritize emergency traffic in WBANs, ensuring timely data delivery for critical health monitoring.
Raza et al. [24]LoRaWANExamines how LoRaWAN’s scalability can support health monitoring by optimizing network performance for diverse environments, such as TV-white areas or the industrial, scientific, and medical (ISM) band.
Yousuf et al. [25]LoRaWANStudies LoRaWAN’s reliability in transmitting health-related data, highlighting its indoor coverage and suitability for buildings over the unlicensed ISM band at 915 MHz.
Ertürk et al. [27]LoRaWANDiscusses LoRaWAN’s benefits for healthcare by allowing private, secure installations for testing if LoRaWAN is the right solution for the e-Health application.
Table 2. Network parameters of IEEE 802.15.6 and LoRaWAN used in NS3.
Table 2. Network parameters of IEEE 802.15.6 and LoRaWAN used in NS3.
ParameterValue
Simulation time1000 s
Simulation area200 m × 200 m
Number of nodesVariable (50)
Number of gateways1
Packet size19 Bytes
Channel lossLogarithm distance propagation loss
Channel path loss exponent3.76
Mobility typeRandom rectangle position
MAC protocol in IEEE 802.15.6Hyper MACs: CSMA/CA and TDMA
MAC protocol in LoWaRANHyper MACs: ALOHA and TDMA
Routing protocolLEACH
Energy250 J for the two Net Devices
TDMA and CSMA/CA Tx, Rx, idle current0.028 A, 0.0112 A, and 0.0013 A
TDMA and ALOHA Tx, Rx current0.028 A and 0.0112 A
TDMA and ALOHA idle current0.0013 A and 0.0000015 A
Table 3. IEEE 802.15.6 and LoRaWAN implementation.
Table 3. IEEE 802.15.6 and LoRaWAN implementation.
IEEE 802.15.6LoRaWAN
Network topologyStar topologyStar topology
MAC layerCSMA/CA and TDMAALOHA and TDMA
Network layerPackets are sent every 5 sPackets are sent every 10 s
Table 4. IEEE 802.15.6 and LoRaWAN summary results.
Table 4. IEEE 802.15.6 and LoRaWAN summary results.
ProtocolIEEE 802.15.6LoRaWAN
At 10 NodesAt 50 NodesAt 10 NodesAt 50 Nodes
Throughput11 kbps45 kbps7 kbps37 kbps
Arrival rate0.07%0.33%0.05%0.25%
Delay2 s17 s0.1 s7 s
Energy
Consumption		18 J75 J10 J42 J
PDR36%30%25%24%
Network
lifetime		30 h14 h37 h18 h
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Al-Sofi, S.J.; Atroshey, S.M.S.; Ali, I.A. IEEE 802.15.6 and LoRaWAN for WBAN in Healthcare: A Comparative Study on Communication Efficiency and Energy Optimization. Computers 2024, 13, 313. https://doi.org/10.3390/computers13120313

AMA Style

Al-Sofi SJ, Atroshey SMS, Ali IA. IEEE 802.15.6 and LoRaWAN for WBAN in Healthcare: A Comparative Study on Communication Efficiency and Energy Optimization. Computers. 2024; 13(12):313. https://doi.org/10.3390/computers13120313

Chicago/Turabian Style

Al-Sofi, Soleen Jaladet, Salih Mustafa S. Atroshey, and Ismail Amin Ali. 2024. "IEEE 802.15.6 and LoRaWAN for WBAN in Healthcare: A Comparative Study on Communication Efficiency and Energy Optimization" Computers 13, no. 12: 313. https://doi.org/10.3390/computers13120313

APA Style

Al-Sofi, S. J., Atroshey, S. M. S., & Ali, I. A. (2024). IEEE 802.15.6 and LoRaWAN for WBAN in Healthcare: A Comparative Study on Communication Efficiency and Energy Optimization. Computers, 13(12), 313. https://doi.org/10.3390/computers13120313

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