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Review

Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey

1
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2
School of Medical Information and Engineering, Southwest Medical University, Luzhou 646000, China
3
School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(9), 3539; https://doi.org/10.3390/s22093539
Submission received: 4 April 2022 / Revised: 24 April 2022 / Accepted: 28 April 2022 / Published: 6 May 2022
(This article belongs to the Topic Wireless Sensor Networks)

Abstract

:
With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient’s body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.

1. Introduction

The prevalence of chronic diseases such as hypertension, diabetes and obesity is currently aggravating the burden on public-funded healthcare systems and causing many economic and social challenges in some countries. According to the World Health Organization (WHO), the global population over the age of 60 will reach about 2.1 billion by 2050 [1]. These public healthcare problems are exacerbated by the rapid growth of the elderly population, as elderly people are more prone to suffer from chronic diseases. Chronic diseases constitute a major portion of human health risks, accounting for more than two-thirds of all deaths worldwide [2]. Cardiovascular disease accounts for 30 percent of all deaths. Globally, more than 180 million people have diabetes, which is estimated to affect about 360 million people by 2030 [3,4]. In 2015, more than 2.3 billion people were overweight, the leading cause of these chronic diseases. Moreover, the rapid rise in debilitating neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, threatens millions of people. These diseases are not simply the result of an aging population, but are caused by sedentary behavior, inappropriate eating habits and insufficient physical activity [5,6]. Many studies [7,8,9] have demonstrated that chronic diseases can be effectively prevented at an early stage, suggesting that early diagnosis and detection are vitally important. Therefore, it is of great and urgent significance to realize real-time monitoring of human health via disease surveillance and health evaluation.
Due to the current developments in sensors and wireless communication technology, wireless body area networks (WBANs) may alleviate or even solve the problems of rampant chronic diseases, an aging population, a shortage of medical facilities, etc. A WBAN is a body network that enables communication between people and things by connecting nodes with sensors in, on or around humans [10]. Data transmission between these nodes is limited to an ultra-short distance of 2 m by wireless means. Figure 1 illustrates the basic idea of the WBAN and its applications. The responsibility of each node is to collect physiological parameters, such as electrocardiogram (ECG), electroencephalogram (EEG), blood oxygen saturation (SpO2), blood pressure (BP) and heart rate variability (HRV). The terminal plays a role of a personal server to gather all the data from nodes and then transmit them to the Internet. Moreover, WBAN system can provide bio-feedback to the patients from the remote servers. The servers on the remote system cannot only process the data efficiently, but also provide some services, such as real-time monitoring and health consultation, which is helpful for the management of chronic diseases. As shown in Figure 1, WBAN has a huge market, from which equipment manufacturers, operators, solution providers and service providers can all take a profit. That is another important reason why WBANs attracted great attention around the world as soon as the idea emerged. Taking advantage of WBAN technology is important for economic development as a new growth engine. Although WBANs are attractive for many applications, they are still in their infancy, and this new wireless technology combines multiple disciplines, such as communication, bio-engineering and microelectronics, making it difficult to solve the key issues.
Figure 2 describes the communication architecture of a WBAN health monitoring system. WBANs can be categorized into three tiers of level communication: intra-WBAN, inter-WBAN, and beyond-WBAN communications [11]. The tier-1 is intra-WBAN communication, which consists of a set of sensors placed on or implanted into the human body. Sensors preserve star topology and are connected to a centralized node in this tier with the function of collecting and transmitting various human physiological parameters. This communication is only between the sensors and the sink, whose task here is to process the collected information and transmit it to Tier-2 through ZigBee, Bluetooth, Wi-Fi or some other short-distance transmission technology. Tier-2 is inter-WBAN communication, which uses smartphones, personal computers or other intelligent electronic devices. Ad hoc architecture is distributed to communicate in this tier with a random topology. The function of Tier-2 is to forward the information sent by the sensor to Tier-3 (terminal center) through 3G/4G/5G, WLAN and other wireless technologies. Tier-3 is the beyond-WBAN communication, in which the terminal center is mainly composed of remote servers. The function is to store and analyze the received information, which can be used for monitoring, diagnosis and treatment of diseases. Especially when the received data are abnormal, an emergency response and alarm can be initiated, contributing to speeding up emergency care. Each tier has special technical difficulties and requirements that need to be solved. Numerous papers [12,13,14,15,16] have summarized the challenges faced by WBANs, but these surveys, which focus on some specific aspects of WBANs, are limited to systematically describing WBANs and only refer to several technical points, failing to provide solution schemes using integrated circuits (IC). In this paper, we denote the application and technical requirements of WBAN, and give the problems systemic, including the sensor nodes, frequency bands, channel models, ultra-short range communication systems, networking and protocols, safety and privacy protection and energy consumption. For each problem, we present a comprehensive and deep analysis and try to present some hints for technical solutions. In order to solve the problems of safety and energy consumption in particular, we put forward designs for application-specific integrated circuit (ASIC) schemes which not only achieved good performances but also can contribute to the future batch production and application of WBANs.

2. WBAN Applications for Health Monitoring

WBAN technology is widely used in health monitoring, military, sports, entertainment, aerospace and many other fields involving human beings, presenting huge economic benefits and social value. As shown in Table 1, it is commonly divided into medical and non-medical applications [13]. WBANs could contribute to protecting those people who may be exposed to life-threatening conditions, such as firefighters, soldiers, deep-sea explores and space explorers. Examples include providing firefighters with monitoring of special environments, such as fire sites and areas with toxic gases; providing emergency early warnings to improve the safety of firefighters; monitoring the physical condition of athletes in real time to set appropriate training intensity; and wirelessly transmitting vital military information to control centers or remotely commanding units. The non-medical applications can be categorized as real-time streaming, entertainment and non-medical emergencies. In recent years, entertainment uses, such as gesture and motion detection for games and virtual experiences, have also emerged.
Due to the huge demands of telemonitoring and telemedicine, WBANs can greatly alleviate the lack of medical resources and improve their utilization rate, making medical applications the most important applications. Medical WBAN applications could be generally subcategorized into two groups: wearable WBANs and implantable WBANs according to the sensors being on or in the human body [29] (details shown in Table 1). The advantages of ceaseless body data capture from various sensors will lead to healthcare beyond hospital limitations and facilitate exceedingly personalized and individual care at any time and any place [30]. Health monitoring to provide chronic disease surveillance and medical care services can greatly enhance the patients’ quality of life and address the medical facility shortage [31]. WBAN is capable of wireless connectivity to ECG/EEG/electromyograms (EMG); SpO2, BP and body temperature monitors; implanted defibrillators; sleep staging monitors; devices for high-risk pregnancy monitoring; implanted drug delivery trackers; swallowed camera pills; gait analysis systems; and emotion detection systems [32,33,34,35,36,37,38].
WBAN systems for health monitoring are heterogeneous and have been developed for various diseases and disabilities. Table 2 summarizes some diseases monitored using WBANs in recent studies. Currently, various types of health monitoring systems have been used for various diseases. These disease monitoring systems vary widely in terms of physiological parameters, sensor design and transmission technology. Moreover, even monitoring systems for the same disease are not uniformly standardized. Different types of disease present specific challenges for WBAN technology. Thus, health monitoring systems using WBANs are heterogeneous and have been used for various diseases and disabilities. The various disease data needed necessitate different sensors, making sensor technology an important part of WBANs. In addition, it has been found that transmission protocols commonly involve Wi-Fi, 3G/4G/5G, GPRS, Bluetooth or ZigBee, and most of them are based on smartphones [39,40,41,42]. These two parts, i.e., sensor and transmission, will be discussed in detail in later sections.
With the widespread application of WBANs in health monitoring, technical requirements need to be made specific to the types of disease and different application scenarios. We must not only have different requirements for sensor nodes and topology, but also transmission rate and real-time performance. Table 3 describes some typical technical requirements of BAN applications. Three aspects, “power consumption”, “quality of service (QoS)” and “safety for the human body” have significant differences in the BAN standards compared to 802 other standards [52]. Table 4 shows the desired ranges matching the proposed requirements of WBAN standards.
To sum up, in this chapter, we introduced some typical application scenarios of WBAN, especially summarizing various diseases monitored by using WBANs. Obviously, each disease requires selecting appropriate sensors, and WBAN technology has its own technical requirements for various diseases and application scenarios. Therefore, the WBAN is a heterogeneous system with its special technical requirements, proprietary standards and challenges compared to the traditional wireless sensor networks.

3. WBAN Sensor Techniques

Sensors, which convert physical parameters into electronic signals, are the crucial components in the WBAN. Sensors have been mainly categorized into three types according to their functions [53]. Physiological sensors measure ambulatory BP, glucose (continuously), body temperature, blood oxygen, ECG, EEG, EMG, etc.; biokinetic sensors measure acceleration and the angular rate of human motion rotation; and ambient sensors measure humidity, light, sound pressure level and environmental temperature. Generally, WBAN sensors for obtaining physiological parameters of the human body are categorized into two groups: invasive (i.e., implanted sensors) and non-invasive (i.e., external sensors) [14], as shown in Table 5. Implanted sensors are inserted into the human body or under the skin with the help of surgery, whereas external sensors are directly attached to the skin or around the body.
Table 6 describes the mechanisms of several common sensors and their data rates in WBANs [54]. One important requirement is that the sensors can continuously monitor the patient’s health conditions without disturbing their activities. Since the physiological parameter signals are very weak, the correct detection and precise processing of these signals by sensor nodes is very crucial: these collected data are the basis of clinical diagnostics. In addition, sensors generate various types of data that require different processing to ensure the specific requirements are met. Types include general data, delay sensitive data, emergency data and reliability data. Due to the various data rates of sensors of human physiological parameters, how to merge multi-sensor data to describe one’s health condition in a uniform standard is difficult. To address these problems, several researchers [55,56,57] have designed novel flexible sensors to provide comfortable, soft and stretchable wearable systems for human health monitoring, enabling effective monitoring of physiological signs without affecting patients’ daily activities. Some studies have designed signal extraction schemes for each different body parameter which adapt to body status and environmental movement [58,59,60], and explored high-speed preprocessing and transmission algorithms for the weak physiological parameter signals of the human body [61,62]. There are also some studies [63,64,65] that proposed data fusion strategies for multiple sensor nodes based on different sensing mechanisms and physiological information relationships, forming a series of body parameter detection methods required for sensor node microarchitecture optimization [66,67,68].

4. Wireless Transmission in WBANs

Compared with other wireless transmission technology, WBANs which are placed on the human body focus on realizing short-range, low-cost, low-power and low-implementation transmission; the short-range communication channels especially are quite different. Electromagnetic waves among nodes or nodes and smart terminals would pass through the human body or spread along the body’s surface. The wireless nodes of WBAN require much lower power consumption than traditional sensor network nodes, generally with less than 1 mW peak power. WBAN communication distance is generally within 2 m, which is shorter than that of a general personal area network. These differences indicate that WBANs need to seek new wireless transmission schemes. Table 7 shows the main specifications for WBAN systems [69].

4.1. Frequency Bands for WBAN Channel Models

The choice of frequency bands for WBANs is an elementary factor affecting wireless communication. Table 8 provides the operation frequency bands for medical WBANs [12]. These frequency bands can be further divided into two groups (in-body or on-body) depending on the location of the sensor. The medical device radio communications frequency band (401–406 MHz) is mainly used in implantable medical devices. It has quiet channel properties and worldwide availability; it cannot be substituted by wearable sensor node frequencies. Industrial, scientific and medical (ISM) bands in the 2360–2500 MHz zone are exposed to less interference compared to the 2400–2483 MHz band [11], which is an unlicensed band occupied by Wi-Fi, Bluetooth and ZigBee as of IEEE 802.15.6. Some frequency bands are only for specific countries, such as the general telemetry (868–870 MHz) and some ISM bands (902–928 MHz) with limited bandwidth. The IEEE 802.15.6 standard presented a new communication medium, human body communications (HBC), with a frequency range of 5–50 MHz. A capacitively coupled HBC channel was developed based on the induction of the transmitter to the near electric field around the human body, and the weak coupling changes in the electric field near the human body channel are detected by the receiver. According to the application scenarios of WBANs, selecting different channel frequency bands can effectively avoid interference and solve the coexistence problem.

4.2. WBAN Channel Models

Various sensors are placed on the body or implanted into the body in WBAN health monitoring. Due to the complex environment of the human body and its surroundings, there are many interference signals in WBAN channels which will affect the quality of communication, directly affecting the performance of the WBAN system. WBAN channel characteristics are not only the key to constructing the network’s architecture, but also indispensable to the design of the upper-layer network protocol. Due to the complexity of human tissues and body shapes, and the diversity of environments we occupy, the difficulty of WBAN channel modeling is great. Therefore, the wireless transmission channel in the WBAN is divided into several modes, as shown in Figure 3 and Table 9. There are three typical nodes in WBANs: implanted, body surface and external nodes [76]. The channel characteristics have four main relevant factors: the frequency factor (WBAN frequency bands may include 400 MHz, 600 MHz, 2.4 GHz and UWB), environmental factors (WBAN’s environment, such as the anechoic chamber, outdoors or a hospital), antenna placement and status of human motion [77,78,79,80,81]. Environment factors mainly affect the multipath propagation caused by the surrounding complex environment. In an outdoor environment, the surface channel sees little attenuation. However, in an indoor environment, the attenuation of a WBAN channel with 900 MHz and 2.4 GHz frequency bands can be dramatic. Therefore, a perfect channel model should introduce the three modifying factors to satisfy the simulation of different applications.
Differently from common wireless communication channels, which are severely affected by frequency selective fading, WBAN channels are mainly affected by flat fading. The reason is that the multipath delay is tiny due to the short distances, so the effect of multipath fading can be ignored. Concurrently, due to the variations in the surrounding environment of the human body or movement of body parts, path loss will change violently, leading to shadowing from the mean value for a given distance. The main model is a shadow fading channel and can be adjusted dynamically by three parameters—antenna position, sensor position and personal environment. Therefore, the total path loss (PL) is defined as the following formula [82]:
P L = P L 0 + 10 n   log 10 d d 0 + S
where PL0 is the path loss at a reference distance d0, d is the distance between the antennas, n represents the path-loss exponent and S represents the shadow.
The first parameter is affected by the antennas’ size, shape and transmitting direction; the second parameter relies on its position in the human body; and the third parameter is based on statistical modifications for height, weight and gender. Furthermore, various environmental models should be established, such as family chambers, hospitals and outdoors, which have different impacts on path loss and fade. Therefore, the environment in which WBAN communication occurs is highly dynamic and unstable, and a specific communication channel must be considered to guarantee good communication performance in a highly dynamic and unstable environment [83]. The IEEE standard channel models are only static, having no time varying effects and correlation features. Several dynamic WBAN channel models have been proposed, as shown in Table 10.

4.3. Physical (PHY) Layer and Medium Access Control (MAC) Layer

The IEEE 802.15.6 standard defines new PHY and MAC layer specifications for WBANs, providing ultra-low-power, low-cost and short-range wireless communication which operates in or around the human body. IEEE 802.15.6 standard outlined three different PHY schemes: narrowband (NB), UWB and HBC. NB and UWB PHY are based on radio frequency (RF) propagation, and HBC is based on a new non-RF technique. NB PHY included seven frequency bands from 402 to 2483.5 MHz for a total of 230 channels, of which the 402–405 MHz band is used for implantable devices, three different frequency bands (863–956 MHz) are used for wearable applications and 2360–2400 MHz is used for medical demands. UWB PHY included 11 frequency bands ranging from 3494.4 to 9984.0 MHz. Due to the high signal attenuation and severe shadow effect through the human body, these radio frequency bands are not suitable for HBC. As a signal transmission medium, the frequency band of HBC ranges from 5 to 50 MHz; the center frequency band is at 21 MHz. The WBAN standard demands an ultra-short range communication mechanism limited to 2 m. As the lowest layer, PHY, determines the high-layer protocols, which requires minimizing power consumption and bit error rate (BER) [91]. Ideally, power consumption and BER increase linearly as the data rate increases from 1 kbps to 10 Mbps, resulting in consistent energy use per bit of information. In order to acquire low power consumption, the PHY signal processing algorithms at the receiver need to be designed carefully. Future works should focus on seamless connectivity in dynamic environments to minimize performance degradation in terms of data loss, latency and throughput.
WBAN is a severely resource-constrained network in which most of the power consumption is caused by the transceiver, whose duty cycle is controlled by the MAC layer. Therefore, it is very important to design an energy-saving MAC protocol to ensure efficient and reliable transmission of data using limited wireless channel resources in WBAN. The unique challenges of MAC protocol design are mainly focused on energy consumption, QoS and transmission efficiency. Early studies focused on addressing each individual problem, such as the dynamic traffic pattern [92,93] or the energy efficiency [94,95]. Liu et al. [96] proposed a TDMA-based MAC protocol that can adjust the duty cycle according to the types of nodes in the WBAN to guarantee both QoS and energy efficiency. Maman et al. [69] proposed an adaptive TDMA MAC protocol which could automatically detect the shadowing effect and adjust the superframe parameters; it provided good results in latency outage and energy consumption. Bai et al. [97] proposed a frame structure model for a self-adaptive guard band protocol based on TDMA which synchronizes the sleeping state of the nodes and the coordinator to effectively reduce the energy consumption. Lin et al. [98] proposed a MAC protocol based on channel-aware polling to optimize energy efficiency by adjusting the number of polling cycles in super frames to adapt to dynamic traffic requirements and channel fluctuations.

4.4. Network Protocols

WBAN is a dedicated network with special data transmission characteristics. Therefore, based on a very short-range wireless communication system, it is necessary to build new network architecture and corresponding protocols. On one hand, for the vital information, well-designed architecture optimizes network transmission [22]; on the other hand, for the body and the surface of the wireless environment, appropriate architecture optimizes protocol design [10,99]. Note that for WBAN architecture, some solutions can be obtained from wireless sensor networks. However, these results cannot be applied directly to get the best network performance because WBAN has many different characteristics. Consequently, there should be further exploration and research toward building WBAN-dedicated network protocol architecture. The main issues are listed as follows:
  • Multi-level QoS and cross-layer optimization. In a WBAN for various types of medical applications, the network should provide different levels and types of service quality. Thus, it would be necessary to design new or improved link layer, network layer and application layer protocol, to fully guarantee the data transmission QoS with changing demands depending on the characteristics of the information needed [22,100]. In addition, the layers’ protocol for low-power design strategy and cross-layer design and optimization methods are also worthy of attention [101,102].
  • Adaptive networking and topology control. WBANs usually consist of different types of nodes, but the node numbers of the same type are not large. The network is more focused on the different types of heterogeneous nodes in networking and service, which is one of the differences between WBANs and common WSNs [103]. Therefore, not only homogeneous nodes but also heterogeneous nodes can be supported in a self-organizing network scheme. To take the posture effects into account in the networking and managing network at the same time, the scheme requires a dynamic topology control method which is able to adapt changes to follow the physical state [10].
  • In-network cooperation and feedback optimization. Different heterogeneous nodes cooperate with each other and complete human monitoring and information processing and transmission. This is an important feature of WBANs. To establish a dedicated WBAN architecture, it is essential to develop a collaborative framework and mechanisms between network nodes. These mechanisms include sensor-related technology of event-driven information transfer methods, sleep–wake-up mechanisms and monitoring information data fusion mechanisms [104,105]. It is worth noting that there are usually a lot of feedback loops in a WBAN which could conveniently control the reverse information transmission; thus, how to design closed-loop controlling methods and the corresponding protocol is an important issue for WBANs.
  • Heterogeneous interconnection framework. Any one WBAN and other WBANs, personal area networks, LAN, mobile communication networks and the Internet connect together, which is affecting WBAN technology and the development of important technical factors. A heterogeneous network includes two aspects: the interconnection of heterogeneous nodes and a heterogeneous network. On one hand, a common data representation and flexible network connectivity structure should be proposed for internal heterogeneous nodes in a WBAN with the purpose of having interconnections between all kinds of sensor nodes and interconnections between nodes and gateways [106]. On the other hand, to aim at connections between the WBANs and other types of heterogeneous networks, it is necessary to build a common data communication and protocol conversion interface to complete the interconnection of WBANs and the Internet, mobile communications networks and other mainstream networks [107,108]. From the above, the former is conducive to interoperability and interconnection among WBAN devices, and the latter can provide network-level technical support for the implementation of telemedicine, which is significant for remote continuous monitoring.
  • With the development of the Internet of Things, the increasing number of WBANs and the mobility of WBANs, interference is becoming more challenging. For a single WBAN, intra-BAN interference can be effectively avoided by using TDMA techniques, but multiple WBANs interfere with each other when they are co-located (i.e., inter-WBAN interference). Figure 4 describes the different types of interference in WBANs [109] and the parameters that cause inter-BAN interference.

4.5. Chapter Summary

In response to the special technical requirements of WBAN, this chapter not only introduced the IEEE 802.15.6 standard channel models, but also discussed the existing WBAN channel models in different scenarios, including the methods of channel model building approaches and their respective advantages. Finally, the three important parts of a WBAN, i.e., PHY, MAC and networking layers, were expounded on, and their respective requirements and corresponding solutions were put forward. Therefore, WBANs focus on energy consumption, bit error rate, QoS transmission efficiency, security, privacy, etc.

5. Security and Privacy

The human physiological parameters collected or transmitted by WBANs are highly sensitive and confidential because these determine the outcomes of clinical diagnoses and belong to the patients. With the development of Internet of Things technology, WBANs contain an increasing number of nodes, resulting in more and more critical data transmission in the networks. Therefore, security and privacy are the utmost considerations for BANs and should be guaranteed carefully to prevent information leakage and tampering. However, security schemes proposed for other networks are not suitable for WBANs due to strict resource constraints, such as energy consumption, communication rate and computing power. Hence, addressing security in WBAN environment is a vital research topic and brings additional challenges to the design of WBANs.
The IEEE 802.15.6 standard defined three security levels for WBANs, as shown in Table 11. The specific level of security is determined by the types of data and their levels of the privacy. More precisely, for level 0, the lowest level of security, no algorithm or mechanism is used during communication. At level 1, secured authentication is provided during the data transmission. Level 2 requires both authentication and encryption. This standard also included four elliptic-curve-based protocols to guarantee security. However, researchers found that those four protocols are not secure enough in some practical applications and are vulnerable to multiple attacks [110]. Therefore, based on this standard, several security schemes for WBANs have been created to enhance security in recent studies. Table 12 shows a comparison of the recent security techniques in WBANs [111].
WBAN security schemes can be mainly divided into two groups: authentication and encryption. In terms of identity authentication, the WBAN system first needs to authenticate nodes trying to join the network and check whether they have the right to access the network to prevent illegal users from intruding. Nowadays, as the application scenarios of WBANs are becoming more and more diverse, authentication schemes need to be improved according to different application requirements. In response to the security requirements of WBANs in cloud-assisted environments, Mahender Kumar and Satish Chand proposed an identity-based anonymous authentication and key agreement protocol scheme, which was proven to be secure and achieved the required security properties [119]. A three-tier security approach was presented that uses lightweight cryptography to address security in a three-layered WBAN system [122]. In order to improve the adaptability to human motion and the integrity of data transmission, a multi-hop WBAN authentication method based on a lightweight physical unclonable function was proposed [123]. Liu et al. [124] performed a certificateless signature scheme to construct two efficient remote anonymous authentication (AA) schemes for WBANs. Debiao He et al. [125] proposed a new AA scheme to avoid imitative attacks on this basis of Liu et al.’s scheme. Gangadari et al. [126] designed an improved AES algorithm for identity authentication in BAN by using one-dimensional cellular automata to replace the traditional look up table (LUT)-based S-Box method. The results showed a high level of security.
Currently, there are roughly three schemes available for BAN data encryption. The first is the key pre-distribution scheme, distributing secret keys in sensor nodes before communication. Tripathy et al. [127] described a matrix-decomposition-technique-based key predistribution approach, which provides superior key connectivity and requires less storage memory. Saikia and Hussain proposed a combinatorial-group-based key distribution method to effectively enhance the security between nodes [128]. The second is to utilize physical sign information as a means of security. Unlike other sensor networks, WBAN sensor nodes collect physiological parameters. These biometric parameters of different individuals are discrepancies. Even for the same individual, the values are not constant but change slowly over time. Thus, these physiological signals, such as EEG, naturally act as individual keys in WBANs. Seepers et al. [129] presented a heart-beat-based security scheme that extracted an inter-pulse interval feature from R waves of ECG signals and used the key generated by heart-beat interval changes to encrypt. Bai et al. [130] proposed a novel encryption method for WBANs that uses the QRS complex of the ECG signals. It has the advantages of low energy consumption, dynamic key and simple hardware implementation. Another common approach is to utilize the properties of the BAN channel as the key, which realizes a one-time key to ensure the absolute safety of the eavesdropping outside λ/2. The basic idea of BAN channel encryption is to generate the key according to the intensity of the received signal. Shen et al. [131] proposed a multi-layer authentication protocol for WBANs which generates a secure session key for relatively little in the way of computational cost. Some other complex BAN channel encryption methods include ellipse curve cryptography [132] and advanced encryption standard (AES) encryption [126]. However, there are some disadvantages, such as high consumption cost and no dynamic updating of the key in BAN channel encryption [133,134].
The above-mentioned methods focused on software implementations. The hardware implementations of these security schemes have the advantages of lower power consumption and low latency. The authors of [135] proposed a hardware implementation named ASIC to solve the security problems. It was synthesized by using SIMC 65 nm complementary metal oxide semiconductor (CMOS) technology. This ASIC contains two modules, authentication and encryption modules, and these two modules could operate together or independently, depending on the security level. Therefore, lower power consumption was achieved in some environments because of only one module operating. This proposed ASIC scheme has obvious superiority in terms of power consumption, latency and steadiness of performance.
WBAN involves human body information; therefore, security and privacy are very important, which must be considered when designing a WBAN. This section went from illustrating the security privacy levels to an overview of the existing methods for security and privacy. In addition to outlining the three schemes available for BAN data encryption in software implementations, a hardware-based ASIC scheme was also proposed.

6. Energy Efficiency

Compared to conventional short-range wireless communication protocols (i.e., Zigbee and Bluetooth), WBAN is varies more in energy consumption. As WBANs involve the human body, in order to make them wearable, it is often necessary to limit the size of the hardware (small sizes of 1 to 3 cm or even less), which makes it difficult to expand the battery power. Additionally, frequent battery changes are extremely inconvenient for patients, especially for the implanted devices, which can only be replaced by surgery. The hardware size and battery power limitations of a WBAN directly affect its usability and the user’s satisfaction. Therefore, energy efficiency is very important for a well-designed WBAN, and the optimization of energy consumption has become one of the current focuses of WBANs in many studies. Some studies that worked on energy harvesting included solar, vibration and thermal energy to improve energy efficiency [136,137,138,139]. Others worked on extending the battery capacity by using PHY or MAC protocols with low power consumption; some studies on the ultra-low-power IC have provided a new window for these problems and achieved good results [140,141]. Liu et al. [142] designed an ultra-low power baseband transceiver IC with 0.18 μm CMOS technology. Chen et al. [143] proposed a low-power, area-efficient baseband processor for WBAN transmitters based on the IEEE 802.15.6, which supported the specifications of the narrowband (NB) PHY by optimizing the DPSK modulator and clock gating technology. Liang et al. [144] created a hardware scheme applicable to IEEE 802.15.6, which was implemented based on FPGA and supported NB PHY, including baseband processing, radio frequency and digital-to-analog conversion. Finally, the throughput, working frequency and energy consumption of this scheme were verified. Chougrani et al. [145] proposed a baseband architecture supporting the UWB digital baseband PHY in the IEEE 802.15.6 standard, and the FPGA experiment proved that this scheme has a low packet error rate and bit error rate. Mathew et al. [146] put forward a complete NB PHY transceiver implemented in FPGA, which consists of baseband transceiver modules. An ASIC of baseband processing module in NB has been performed to solve the energy consumption of WBANs [147]. This ASIC conforms to the IEEE 802.15.6 standard. Compared to other publicized IC design schemes, this proposed ASIC showed advantages in power consumption, throughput, small area and energy efficiency. The detailed results are demonstrated in Table 13.
Designing a suitable IC for WBANs requires not only optimizing the power consumption. This is also one of the key conditions to enable WBAN to be applied on a large scale. However, many of the technologies in IC for WBAN are still in the early stage and there is no common chip for WBAN yet. In the future, the proposed ASIC can be improved by considering the cooperation between the PHY and MAC layers and the security issues.

7. Conclusions

As a new, rapidly spreading technology, WBANs play a significant role in health monitoring. In this paper, we have reviewed the WBAN applications in healthcare, especially those for various diseases monitoring, along with their technical requirements and challenges. Several technique issues, such as sensor nodes, wireless transmission, safety and energy efficiency, need to be seriously considered, which we discussed deeply. More importantly, in terms of the safety and energy efficiency problems, a specific ASIC was proposed to improve security and reduce energy consumption, which may be conducive to the subsequent mass promotion and application of WBANs.

Author Contributions

Conceptualization, L.Z. and S.H.; methodology, L.Z. and Z.L.; investigation, J.W.; resources, Y.P.; data curation, X.L.; writing—original draft preparation, Z.L.; writing—review and editing, L.Z. and Z.L.; visualization, J.W.; supervision Z.L.; project administration, J.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China (grant number 62171073), the Doctoral Training Program of Chongqing University of Posts and Telecommunications (grant number BYJS202103) and the Open Project of Central Nervous System Drug Key Laboratory of Sichuan Province (grant number: 200027-01SZ).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Chongqing University of Post and Telecommunications (protocol code 62171073 and date of approval is 11 April 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. World Health Organization (WHO). World Report on Ageing and Health; World Health Organization: Geneva, Switzerland, 2016. [Google Scholar]
  2. Raghupathi, W.; Raghupathi, V. An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health. Int. J. Environ. Res. Public Health 2018, 15, 431. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. World Health Organization (WHO). Cardiovascular Diseases (CVDs). Available online: http://www.who.int/mediacentre/factsheets/fs317/en/ (accessed on 6 July 2019).
  4. World Health Organization (WHO). Diabetes. Available online: http://www.who.int/mediacentre/factsheets/fs312/en/ (accessed on 6 July 2021).
  5. Roberts, C.K.; Barnard, R.J. Effects of exercise and diet on chronic disease. J. Appl. Physiol. 2005, 98, 3–30. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Kimokoti, R.W.; Millen, B.E. Nutrition for the Prevention of Chronic Diseases. Med. Clin. N. Am. 2016, 100, 1185–1198. [Google Scholar] [CrossRef] [PubMed]
  7. Chen, L.N.; Liu, R.; Liu, Z.P.; Li, M.Y.; Aihara, K. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci. Rep. 2012, 2, 8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Daowd, A.; Faizan, S.; Abidi, S.; Abusharekh, A.; Shehzad, A.; Abidi, S.S.R. Towards Personalized Lifetime Health: A Platform for Early Multimorbid Chronic Disease Risk Assessment and Mitigation. In Proceedings of the 17th World Congress of Medical and Health Informatics (MEDINFO), Lyon, France, 25–30 August 2019; pp. 935–939. [Google Scholar]
  9. He, K.; Huang, S.; Qian, X.N. Early detection and risk assessment for chronic disease with irregular longitudinal data analysis. J. Biomed. Inform. 2019, 96, 12. [Google Scholar] [CrossRef]
  10. Qu, Y.T.; Zheng, G.Q.; Ma, H.H.; Wang, X.T.; Ji, B.F.; Wu, H.H. A Survey of Routing Protocols in WBAN for Healthcare Applications. Sensors 2019, 19, 1638. [Google Scholar] [CrossRef] [Green Version]
  11. Haider, Z.; Jamal, T.; Asam, M.; Butt, S.; Ajaz, A. Mitigation of wireless body area networks challenges using cooperation. Int. J. Secur. Its Appl. 2020, 14, 15–30. [Google Scholar] [CrossRef]
  12. Sodagari, S.; Bozorgchami, B.; Aghvami, H. Technologies and Challenges for Cognitive Radio Enabled Medical Wireless Body Area Networks. IEEE Access 2018, 6, 29567–29586. [Google Scholar] [CrossRef]
  13. Movassaghi, S.; Abolhasan, M.; Lipman, J.; Smith, D.; Jamalipour, A. Wireless Body Area Networks: A Survey. IEEE Commun. Surv. Tutor. 2014, 16, 1658–1686. [Google Scholar] [CrossRef]
  14. Khan, R.A.; Pathan, A.K. The state-of-the-art wireless body area sensor networks: A survey. Int. J. Distrib. Sens. Netw. 2018, 14, 1550147718768994. [Google Scholar] [CrossRef] [Green Version]
  15. Ghamari, M.; Janko, B.; Sherratt, R.S.; Harwin, W.; Piechockic, R.; Soltanpur, C. A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments. Sensors 2016, 16, 831. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Nidhya, R.; Arunachalamand, V.; Karthik, S. A study on requirements, challenges and applications of wireless body area network. Asian J. Electr. Sci. 2017, 6, 30–36. [Google Scholar]
  17. Salayma, M.; Al-Dubai, A.; Romdhani, I.; Nasser, Y. Wireless Body Area Network (WBAN): A Survey on Reliability, Fault Tolerance, and Technologies Coexistence. ACM Comput. Surv. 2017, 50, 38. [Google Scholar] [CrossRef] [Green Version]
  18. Taleb, H.; Nasser, A.; Andrieux, G.; Charara, N.; Cruz, E.M. Wireless technologies, medical applications and future challenges in WBAN: A survey. Wirel. Netw. 2021, 27, 5271–5295. [Google Scholar] [CrossRef]
  19. Pan, Q.; Brulin, D.; Campo, E.J.J.B.E. Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review. Wirel. Commun. Mob. Comput. 2020, 5, e20921. [Google Scholar] [CrossRef]
  20. Ajerla, D.; Mahfuz, S.; Zulkernine, F.H. A Real-Time Patient Monitoring Framework for Fall Detection. Wirel. Commun. Mob. Comput. 2019, 2019, 9507938. [Google Scholar] [CrossRef]
  21. Joshi, R.; Constantinides, C.; Podilchak, S.K.; Soh, P.J. Dual-Band Folded-Shorted Patch Antenna for Military Search and Rescue Operations and Emergency Communications. In Proceedings of the 18th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM), Waterloo, ON, Canada, 19–22 August 2018. [Google Scholar]
  22. Ullah, S.; Higgins, H.; Braem, B.; Latre, B.; Blondia, C.; Moerman, I.; Saleem, S.; Rahman, Z.; Kwak, K.S. A Comprehensive Survey of Wireless Body Area Networks On PHY, MAC, and Network Layers Solutions. J. Med. Syst. 2012, 36, 1065–1094. [Google Scholar] [CrossRef] [Green Version]
  23. Chakraborty, C.; Gupta, B.; Ghosh, S.K. A Review on Telemedicine-Based WBAN Framework for Patient Monitoring. Telemed. e-Health 2013, 19, 619–626. [Google Scholar] [CrossRef]
  24. Teshome, A.K.; Kibret, B.; Lai, D.T.H. A Review of Implant Communication Technology in WBAN: Progress and Challenges. IEEE Rev. Biomed. Eng. 2019, 12, 88–99. [Google Scholar] [CrossRef]
  25. Bouazizi, A.; Zaibi, G.; Samet, M.; Kachouri, A. A Miniaturized Invasive Antenna Study for a Better performance in Medical Application. In Proceedings of the 32nd IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA), Krakow, Poland, 16–18 May 2018; pp. 98–103. [Google Scholar]
  26. Yu, Y.C.; Nguyen, T.; Tathireddy, P.; Roundy, S.; Young, D.J. An In-Vitro Study of Wireless Inductive Sensing and Robust Packaging for Future Implantable Hydrogel-Based Glucose Monitoring Applications. IEEE Sens. J. 2020, 20, 2145–2155. [Google Scholar] [CrossRef]
  27. Mahmood, S.N.; Ishak, A.J.; Saeidi, T.; Soh, A.C.; Jalal, A.; Imran, M.A.; Abbasi, Q.H. Full Ground Ultra-Wideband Wearable Textile Antenna for Breast Cancer and Wireless Body Area Network Applications. Micromachines 2021, 12, 322. [Google Scholar] [CrossRef] [PubMed]
  28. Chen, D.R. A real-time streaming control for quality-of-service coexisting wireless body area networks. Appl. Soft. Comput. 2018, 68, 719–732. [Google Scholar] [CrossRef]
  29. Darwish, A.; Hassanien, A.E. Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring. Sensors 2011, 11, 5561–5595. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Punj, R.; Kumar, R. Technological aspects of WBANs for health monitoring: A comprehensive review. Wirel. Netw. 2019, 25, 1125–1157. [Google Scholar] [CrossRef]
  31. Wang, J.C.; Han, K.N.; Alexandridis, A.; Chen, Z.Y.; Zilic, Z.; Pang, Y.; Jeon, G.; Piccialli, F. A blockchain-based eHealthcare system interoperating with WBANs. Futur. Gener. Comp. Syst. 2020, 110, 675–685. [Google Scholar] [CrossRef]
  32. Yang, Y.; Chae, S.; Shim, J.; Han, T.-D. EMG sensor-based two-hand smart watch interaction. In Proceedings of the UIST ’15 Adjunct, 28th Annual ACM Symposium on User Interface Software & Technology, Daegu, Korea, 8–11 November 2015; pp. 73–74. [Google Scholar]
  33. Al Rasyid, M.U.H.; Lee, B.H.; Sudarsono, A.; Taufiqurrahman. Implementation of Body Temperature and Pulseoximeter Sensors for Wireless Body Area Network. Sens. Mater. 2015, 27, 727–732. [Google Scholar]
  34. Muramatsu, D.; Koshiji, F.; Koshiji, K.; Sasaki, K. Effect of User’s Posture and Device’s Position on Human Body Communication with Multiple Devices. In Proceedings of the 2015 International Conference on Electronic Packaging and iMAPS All Asia Conference (ICEP-IAAC), Kyoto, Japan, 14–17 April 2015; pp. 124–127. [Google Scholar]
  35. Hess, P.L.; Al-Khatib, S.M.; Han, J.Y.; Edwards, R.; Bardy, G.H.; Bigger, J.T.; Buxton, A.; Cappato, R.; Dorian, P.; Hallstrom, A.; et al. Survival Benefit of the Primary Prevention Implantable Cardioverter-Defibrillator Among Older Patients Does Age Matter? An Analysis of Pooled Data From 5 Clinical Trials. Circ. Cardiovasc. Qual. Outcomes 2015, 8, 179–186. [Google Scholar] [CrossRef] [Green Version]
  36. Sajatovic, M.; Levin, J.B.; Sams, J.; Cassidy, K.A.; Akagi, K.; Aebi, M.E.; Ramirez, L.F.; Safren, S.A.; Tatsuoka, C. Symptom severity, self-reported adherence, and electronic pill monitoring in poorly adherent patients with bipolar disorder. Bipolar Disorders 2015, 17, 653–661. [Google Scholar] [CrossRef] [Green Version]
  37. Sugiura, T.; Imai, M.; Yu, J.; Takeuchi, Y. A low-energy application specific instruction-set processor towards a low-computational lossless compression method for stimuli position data of artificial vision systems. J. Inf. Processing 2017, 25, 210–219. [Google Scholar] [CrossRef] [Green Version]
  38. Xu, H.; Hua, K. Secured ECG signal transmission for human emotional stress classification in wireless body area networks. EURASIP J. Inf. Secur. 2016, 2016, 5. [Google Scholar] [CrossRef] [Green Version]
  39. Balasubramanian, V.; Stranieri, A. A scalable cloud Platform for Active healthcare monitoring applications. In Proceedings of the 2014 IEEE Conference on e-Learning, e-Management and e-Services (IC3e), Hawthrone, Australia, 10–12 December 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 93–98. [Google Scholar]
  40. Msayib, Y.; Gaydecki, P.; Callaghan, M.; Dale, N.; Ismail, S. An Intelligent Remote Monitoring System for Total Knee Arthroplasty Patients. J. Med. Syst. 2017, 41, 90. [Google Scholar] [CrossRef] [PubMed]
  41. Elgazzar, K.; Aboelfotoh, M.; Martin, P.; Hassanein, H.S. Ubiquitous health monitoring using mobile web services. Procedia Comput. Sci. 2012, 10, 332–339. [Google Scholar] [CrossRef] [Green Version]
  42. Hassan, M.K.; El Desouky, A.I.; Elghamrawy, S.M.; Sarhan, A.M. Intelligent hybrid remote patient-monitoring model with cloud-based framework for knowledge discovery. Comput. Electr. Eng. 2018, 70, 1034–1048. [Google Scholar] [CrossRef]
  43. Saha, J.; Biswas, S.; Bhattacharyya, T.; Chowdhury, C. A Framework for Monitoring of Depression Patient using WBAN. In Proceedings of the IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Dept Elect & Commun Engn, Chennai, India, 23–25 March 2016; pp. 410–415. [Google Scholar]
  44. Yang, G.; Jiang, M.Z.; Ouyang, W.; Ji, G.C.; Xie, H.B.; Rahmani, A.M.; Liljeberg, P.; Tenhunen, H. IoT-Based Remote Pain Monitoring System: From Device to Cloud Platform. IEEE J. Biomed. Health Inform. 2018, 22, 1711–1719. [Google Scholar] [CrossRef] [PubMed]
  45. Li, C.; Hu, X.P.; Zhang, L.L. The IoT-based heart disease monitoring system for pervasive healthcare service. In Proceedings of the 21st International Conference on Knowledge—Based and Intelligent Information and Engineering Systems (KES), Marseille, France, 6–8 September 2017; pp. 2328–2334. [Google Scholar]
  46. Hidalgo, J.A.; Cajiao, A.; Hernández, C.M.; López, D.M.; Quintero, V.M. VISIGNET: A wireless body area network with cloud data storage for the telemonitoring of vital signs. Health Technol. 2015, 5, 115–126. [Google Scholar] [CrossRef]
  47. Melillo, P.; Orrico, A.; Scala, P.; Crispino, F.; Pecchia, L. Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients. J. Med. Syst. 2015, 39, 109. [Google Scholar] [CrossRef]
  48. Boursalie, O.; Samavi, R.; Doyle, T.E. M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease. In Proceedings of the 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN)/5th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH), Berlin, Germany, 27–30 September 2015; pp. 384–391. [Google Scholar]
  49. Zulj, S.; Seketa, G.; Dzaja, D.; Sklebar, F.; Drobnjak, S.; Celic, L.; Magjarevic, R. Supporting diabetic patients with a remote patient monitoring systems. In Proceedings of the VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Colombia, 26–28 October 2016; pp. 577–580. [Google Scholar]
  50. Vivekanandan, S.; Devanand, M. Remote monitoring for diabetes disorder: Pilot study using InDiaTel prototype. Eur. Res. Telemed. 2015, 4, 63–69. [Google Scholar] [CrossRef]
  51. Mohammed, M.S.; Sendra, S.; Lloret, J.; Bosch, I. Systems and WBANs for Controlling Obesity. J. Healthc. Eng. 2018, 2018, 21. [Google Scholar] [CrossRef]
  52. Patel, M.; Wang, J.F. Applications, Challenges, and Prospective in Emerging Body Area Networking Technologies. IEEE Wirel. Commun. 2010, 17, 80–88. [Google Scholar] [CrossRef]
  53. Hanson, M.A.; Powell, H.C.; Barth, A.T.; Ringgenberg, K.; Calhoun, B.H.; Aylor, J.H.; Lach, J. Body area sensor networks: Challenges and Opportunities. Computer 2009, 42, 58–65. [Google Scholar] [CrossRef]
  54. Cao, H.S.; Leung, V.; Chow, C.; Chan, H. Enabling Technologies for Wireless Body Area Networks: A Survey and Outlook. IEEE Commun. Mag. 2009, 47, 84–93. [Google Scholar] [CrossRef]
  55. Chen, C.; Wang, Z.Y.; Li, W.; Chen, H.Y.; Mei, Z.N.; Yuan, W.; Tao, L.K.; Zhao, Y.T.; Huang, G.S.; Mei, Y.F.; et al. Novel Flexible Material-Based Unobtrusive and Wearable Body Sensor Networks for Vital Sign Monitoring. IEEE Sens. J. 2019, 19, 8502–8513. [Google Scholar] [CrossRef]
  56. Chou, J.C.; Chen, J.T.; Liao, Y.H.; Lai, C.H.; Chen, R.T.; Tsai, Y.L.; Lin, C.Y.; Chen, J.S.; Huang, M.S.; Chou, H.T. Wireless Sensing System for Flexible Arrayed Potentiometric Sensor Based on XBee Module. IEEE Sens. J. 2016, 16, 5588–5595. [Google Scholar] [CrossRef]
  57. Chen, C.M.; Anastasova, S.; Zhang, K.; Rosa, B.G.; Lo, B.P.L.; Assender, H.E.; Yang, G.Z. Towards Wearable and Flexible Sensors and Circuits Integration for Stress Monitoring. IEEE J. Biomed. Health Inform. 2020, 24, 2208–2215. [Google Scholar] [CrossRef] [PubMed]
  58. Rahman, H.; Ahmed, M.U.; Begum, S. Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration. IEEE Trans. Biomed. Eng. 2020, 67, 88–98. [Google Scholar] [CrossRef] [PubMed]
  59. Manas, M.; Sinha, A.; Sharma, S.; Mahboob, M.R. A novel approach for IoT based wearable health monitoring and messaging system. J. Ambient Intell. Humaniz. Comput. 2019, 10, 2817–2828. [Google Scholar] [CrossRef]
  60. Gao, L.; Zhang, G.F.; Yu, B.; Qiao, Z.W.; Wang, J.C. Wearable human motion posture capture and medical health monitoring based on wireless sensor networks. Measurement 2020, 166, 12. [Google Scholar] [CrossRef]
  61. Al-Naggar, N.Q.; Al-Hammadi, H.M.; Al-Fusail, A.M.; Al-Shaebi, Z.A. Design of a Remote Real-Time Monitoring System for Multiple Physiological Parameters Based on Smartphone. J. Healthc. Eng. 2019, 2019, 13. [Google Scholar] [CrossRef] [Green Version]
  62. Lv, W.; Guo, J.J. Real-time ECG signal acquisition and monitoring for sports competition process oriented to the Internet of Things. Measurement 2021, 169, 9. [Google Scholar] [CrossRef]
  63. Mendes, J.J.A.; Vieira, M.E.M.; Pires, M.B.; Stevan, S.L. Sensor Fusion and Smart Sensor in Sports and Biomedical Applications. Sensors 2016, 16, 1569. [Google Scholar] [CrossRef]
  64. King, R.C.; Villeneuve, E.; White, R.J.; Sherratt, R.S.; Holderbaum, W.; Harwin, W.S. Application of data fusion techniques and technologies for wearable health monitoring. Med. Eng. Phys. 2017, 42, 1–12. [Google Scholar] [CrossRef] [PubMed]
  65. Abdelmoneem, R.M.; Shaaban, E.; Benslimane, A. A Survey on Multi-Sensor Fusion Techniques in IoT for Healthcare. In Proceedings of the 13th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, 18–19 December 2018; pp. 157–162. [Google Scholar]
  66. Hanebeck, U.; Baum, M.; Huber, M.F. Guest Editorial Special Section on Multisensor Fusion and Integration for Intelligent Systems. IEEE Trans. Ind. Inform. 2018, 14, 1124–1126. [Google Scholar] [CrossRef]
  67. Zhou, T.L.; Chen, M.; Zou, J. Reinforcement Learning Based Data Fusion Method for Multi-Sensors. IEEE-CAA J. Autom. Sin. 2020, 7, 1489–1497. [Google Scholar] [CrossRef]
  68. Pan, D.H.; Liu, H.W.; Qu, D.M.; Zhang, Z. Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM. Mob. Inf. Syst. 2020, 2020, 9. [Google Scholar] [CrossRef]
  69. Maman, M.; Ouvry, L. BATMAC: An adaptive TDMA MAC for body area networks performed with a space-time dependent channel model. In Proceedings of the 2011 5th International Symposium on Medical Information and Communication Technology, Montreux, Switzerland, 27–30 March 2011; pp. 1–5. [Google Scholar]
  70. Wang, J.F.; Ghosh, M.; Challapali, K. Emerging Cognitive Radio Applications: A Survey. IEEE Commun. Mag. 2011, 49, 74–81. [Google Scholar] [CrossRef]
  71. Kang, T.; Oh, K.; Park, H.; Kang, S. Review of capacitive coupling human body communications based on digital transmission. ICT Express 2016, 2, 180–187. [Google Scholar] [CrossRef]
  72. Alam, M.M.; Ben Hamida, E. Surveying Wearable Human Assistive Technology for Life and Safety Critical Applications: Standards, Challenges and Opportunities. Sensors 2014, 14, 9153–9209. [Google Scholar] [CrossRef] [Green Version]
  73. Chávez-Santiago, R.; Mateska, A.; Chomu, K.; Gavrilovska, L.; Balasingham, I. Applications of software-defined radio (SDR) technology in hospital environments. In Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013; pp. 1266–1269. [Google Scholar]
  74. Chepuri, S.P.; Francisco, R.d.; Leus, G. Performance evaluation of an IEEE 802.15.4 cognitive radio link in the 2360-2400 MHz band. In Proceedings of the 2011 IEEE Wireless Communications and Networking Conference, Cancun, Mexico, 28–31 March 2011; pp. 2155–2160. [Google Scholar]
  75. Chávez-Santiago, R.; Balasingham, I. Cognitive radio for medical wireless body area networks. In Proceedings of the 2011 IEEE 16th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Osaka, Japan, 10–11 June 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 148–152. [Google Scholar]
  76. Taparugssanagorn, A.; Pomalaza-Ráez, C.; Isola, A.; Tesi, R.; Hämäläinen, M.; Iinatti, J. UWB channel modeling for wireless body area networks in medical applications. In Proceedings of the Proceedings International Symposium on Medical Information and Communication Technology (ISMICT), Osaka, Japan, 13–16 September 2009. [Google Scholar]
  77. Cho, N.; Yoo, J.; Song, S.; Lee, J.; Jeon, S.; Yoo, H. The Human Body Characteristics as a Signal Transmission Medium for Intrabody Communication. IEEE Trans. Microw. Theory Technol. 2007, 55, 1080–1086. [Google Scholar] [CrossRef] [Green Version]
  78. Wegmueller, M.S.; Kuhn, A.; Froehlich, J.; Oberle, M.; Felber, N.; Kuster, N.; Fichtner, W. An Attempt to Model the Human Body as a Communication Channel. IEEE Trans. Biomed. Eng. 2007, 54, 1851–1857. [Google Scholar] [CrossRef]
  79. Fort, A.; Ryckaert, J.; Desset, C.; Doncker, P.D.; Wambacq, P.; Biesen, L.V. Ultra-wideband channel model for communication around the human body. IEEE J. Sel. Areas Commun. 2006, 24, 927–933. [Google Scholar] [CrossRef]
  80. Cotton, S.L.; Conway, G.A.; Scanlon, W.G. A Time-Domain Approach to the Analysis and Modeling of On-Body Propagation Characteristics Using Synchronized Measurements at 2.45 GHz. IEEE Trans. Antennas Propag. 2009, 57, 943–955. [Google Scholar] [CrossRef]
  81. Hasan, K.; Biswas, K.; Ahmed, K.; Nafi, N.S.; Islam, M.S. A comprehensive review of wireless body area network. J. Netw. Comput. Appl. 2019, 143, 178–198. [Google Scholar] [CrossRef]
  82. IEEE P802.15 Wireless Personal Area Networks. Available online: https://mentor.ieee.org/802.15/dcn/08/15-08-0780-09-0006-tg6-channel-model.pdf (accessed on 24 April 2022).
  83. Ferreira, V.; Muchaluat-Saade, D.; Albuquerque, C. B-Move: A Transmission Scheduler Based on Human Body Movements for WBANs. In Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, 28–30 July 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 315–320. [Google Scholar]
  84. Mohamed, M.; Joseph, W.; Vermeeren, G.; Tanghe, E.; Cheffena, M. Characterization of dynamic wireless body area network channels during walking. Eurasip J. Wirel. Commun. Netw. 2019, 2019, 104. [Google Scholar] [CrossRef]
  85. Sun, W.Y.; Zhao, J.; Huang, Y.X.; Sun, Y.N.; Yang, H.Z.; Liu, Y.P. Dynamic Channel Modeling and OFDM System Analysis for Capacitive Coupling Body Channel Communication. IEEE Trans. Biomed. Circuits Syst. 2019, 13, 735–745. [Google Scholar] [CrossRef] [PubMed]
  86. Maman, M.; Dehmas, F.; Errico, R.D.; Ouvry, L. Evaluating a TDMA MAC for body area networks using a space-time dependent channel model. In Proceedings of the 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, Tokyo, Japan, 13–16 September 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 2101–2105. [Google Scholar]
  87. Errico, R.D.; Ouvry, L. Time-variant BAN channel characterization. In Proceedings of the 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, Tokyo, Japan, 13–16 September 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 3000–3004. [Google Scholar]
  88. Roberts, N.E.; Oh, S.; Wentzloff, D.D. Exploiting Channel Periodicity in Body Sensor Networks. IEEE J. Emerg. Sel. Top. Circuits Syst. 2012, 2, 4–13. [Google Scholar] [CrossRef]
  89. Oliveira, C.; Mackowiak, M.; Correia, L.M. Modelling on- and off-body channels in Body Area Networks. In Proceedings of the 2013 SBMO/IEEE MTT-S International Microwave & Optoelectronics Conference (IMOC), Rio de Janeiro, Brazil, 4–7 August 2013; IEEE: Piscataway, NJ, USA, 2013; pp. 1–5. [Google Scholar]
  90. IEEE Std 802.15.6-2012; IEEE Standard for Local and Metropolitan Area Networks—Part 15.6: Wireless Body Area Networks. IEEE: New York, NY, USA, 2012. [CrossRef]
  91. Otal, B.; Alonso, L.; Verikoukis, C. Highly reliable energy-saving mac for wireless body sensor networks in healthcare systems. IEEE J. Sel. Areas Commun. 2009, 27, 553–565. [Google Scholar] [CrossRef]
  92. Lin, L.; Wong, K.J.; Kumar, A.; Tan, S.L. A novel TDMA-based MAC protocol for mobile in-vivo body sensor networks. In Proceedings of the 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom), Beijing, China, 10–13 October 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 273–278. [Google Scholar]
  93. Donovan, T.O.; Donoghue, J.O.; Sreenan, C.; Sammon, D.; Reilly, P.O.; Connor, K.A.O. A context aware wireless body area network (BAN). In Proceedings of the 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare, London, UK, 1–3 April 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 1–8. [Google Scholar]
  94. Omeni, O.; Wong, A.C.W.; Burdett, A.J.; Toumazou, C. Energy Efficient Medium Access Protocol for Wireless Medical Body Area Sensor Networks. IEEE Trans. Biomed. Circuits Syst. 2008, 2, 251–259. [Google Scholar] [CrossRef]
  95. Li, H.; Tan, J. Heartbeat-Driven Medium-Access Control for Body Sensor Networks. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 44–51. [Google Scholar] [CrossRef]
  96. Liu, B.; Yan, Z.; Chen, C.W. Medium Access Control for Wireless Body Area Networks with QoS Provisioning and Energy Efficient Design. IEEE Trans. Mob. Comput. 2017, 16, 422–434. [Google Scholar] [CrossRef]
  97. TONG, B.; LIN, J.; PANG, Y. A protocol with self-adaptive guard band for body area networks. IET Commun. 2018, 12, 1042–1047. [Google Scholar]
  98. Lin, C.H.; Lin, K.C.J.; Chen, W.T. Channel-Aware Polling-Based MAC Protocol for Body Area Networks: Design and Analysis. IEEE Sens. J. 2017, 17, 2936–2948. [Google Scholar] [CrossRef]
  99. Alam, M.M.; Ben-Hamida, E. Strategies for Optimal MAC Parameters Tuning in IEEE 802.15.6 Wearable Wireless Sensor Networks. J. Med. Syst. 2015, 39, 16. [Google Scholar] [CrossRef] [PubMed]
  100. Waheed, T.; Rehman, A.U.; Karim, F.; Ghani, S. QoS Enhancement of AODV Routing for MBANs. Wirel. Pers. Commun. 2021, 116, 1379–1406. [Google Scholar] [CrossRef]
  101. Shahbazi, Z.; Byun, Y.C. Towards a Secure Thermal-Energy Aware Routing Protocol in Wireless Body Area Network Based on Blockchain Technology. Sensors 2020, 20, 3604. [Google Scholar] [CrossRef] [PubMed]
  102. Chen, X.; Xu, Y.X.; Liu, A.F. Cross Layer Design for Optimizing Transmission Reliability, Energy Efficiency, and Lifetime in Body Sensor Networks. Sensors 2017, 17, 900. [Google Scholar] [CrossRef] [Green Version]
  103. Bakin, E.; Ivanov, I.; Shelest, M.; Turlikov, A. Analysis of Energy Harvesting Efficiency for Power Supply of WBAN Nodes in Heterogeneous Scenarios. In Proceedings of the 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Lisbon, Portugal, 18–20 October 2016; pp. 111–118. [Google Scholar]
  104. Al Ameen, M.; Hong, C.S. An On-Demand Emergency Packet Transmission Scheme for Wireless Body Area Networks. Sensors 2015, 15, 30584–30616. [Google Scholar] [CrossRef] [Green Version]
  105. Chelloug, S.A. An intelligent closed-loop learning automaton for real-time congestion control in wireless body area networks. Int. J. Sens. Netw. 2018, 26, 190–199. [Google Scholar] [CrossRef]
  106. Mehmood, G.; Khan, M.Z.; Abbas, S.; Faisal, M.; Rahman, H.U. An Energy-Efficient and Cooperative Fault-Tolerant Communication Approach for Wireless Body Area Network. IEEE Access 2020, 8, 69134–69147. [Google Scholar] [CrossRef]
  107. Rekha, K.S.; Sreenivas, T.H.; Kulkarni, A.D. Remote Monitoring and Reconfiguration of Environment and Structural Health Using Wireless Sensor Networks. In Proceedings of the International Conference on Processing of Materials, Minerals and Energy (PMME), Ongole, India, 29–30 July 2016; pp. 1169–1175. [Google Scholar]
  108. Kaur, R.; Kaur, B.P.; Singla, R.P.; Kaur, J. AMERP: Adam moment estimation optimized mobility supported energy efficient routing protocol for wireless body area networks. Sust. Comput. 2021, 31, 9. [Google Scholar] [CrossRef]
  109. Movassaghi, S.; Majidi, A.; Jamalipour, A.; Smith, D.; Abolhasan, M. Enabling interference-aware and energy-efficient coexistence of multiple wireless body area networks with unknown dynamics. IEEE Access 2016, 4, 2935–2951. [Google Scholar] [CrossRef]
  110. Toorani, M. Cryptanalysis of Two PAKE Protocols for Body Area Networks and Smart Environments. Int. J. Netw. Secur. 2015, 17, 629–636. [Google Scholar]
  111. Ananthi, J.V.; Jose, P. A Perspective Review of Security Challenges in Body Area Networks for Healthcare Applications. Int. J. Wirel. Inf. Netw. 2021, 28, 451–466. [Google Scholar] [CrossRef] [PubMed]
  112. Bengag, A.; Bengag, A.; Moussaoui, O. Effective and Robust Detection of Jamming Attacks for WBAN-Based Healthcare Monitoring Systems. In Proceedings of the International Conference on Electronic Engineering and Renewable Energy, Saidia, Morocco, 13–15 April 2020; Springer: Berlin/Heidelberg, Germany, 2020; pp. 169–174. [Google Scholar]
  113. Arya, K.; Gore, R. Data security for WBAN in e-health IoT applications. In Intelligent Data Security Solutions for e-Health Applications; Elsevier: Amsterdam, The Netherlands, 2020; pp. 205–218. [Google Scholar]
  114. Al Hayajneh, A.; Bhuiyan, M.Z.A.; McAndrew, I. Security of Broadcast Authentication for Cloud-Enabled Wireless Medical Sensor Devices in 5G Networks. Comput. Inf. Sci. 2020, 13, 1–13. [Google Scholar] [CrossRef]
  115. Thamilarasu, G.; Odesile, A.; Hoang, A. An Intrusion Detection System for Internet of Medical Things. IEEE Access 2020, 8, 181560–181576. [Google Scholar] [CrossRef]
  116. Umar, M.; Wu, Z.; Liao, X. Mutual Authentication in Body Area Networks Using Signal Propagation Characteristics. IEEE Access 2020, 8, 66411–66422. [Google Scholar] [CrossRef]
  117. Dharshini, S.; Subashini, M.M. DMASK-BAN: Improving the Security of Body Area Networks. Comput. Fraud. Secur. 2020, 2020, 13–19. [Google Scholar] [CrossRef]
  118. Suchithra, M.; Baskar, M.; Ramkumar, J.; Kalyanasundaram, P.; Amutha, B. Invariant packet feature with network conditions for efficient low rate attack detection in multimedia networks for improved QoS. J. Ambient Intell. Humaniz. Comput. 2021, 12, 5471–5477. [Google Scholar] [CrossRef]
  119. Kumar, M.; Chand, S. A Lightweight Cloud-Assisted Identity-Based Anonymous Authentication and Key Agreement Protocol for Secure Wireless Body Area Network. IEEE Syst. J. 2021, 15, 2779–2786. [Google Scholar] [CrossRef]
  120. Rao, J.D.; Sridevi, K. Novel security system for wireless body area networks based on fuzzy logic and trust factor considering residual energy. In Proceedings of the International Conference on Advances in Materials Research (ICAMR), Bannari Amman Inst Technol, Sathyamangalam, India, 6–7 December 2019; pp. 1498–1501. [Google Scholar]
  121. Ali, Z.; Ghani, A.; Khan, I.; Chaudhry, S.A.; Islam, S.K.H.; Giri, D. A robust authentication and access control protocol for securing wireless healthcare sensor networks. J. Inf. Secur. Appl. 2020, 52, 14. [Google Scholar] [CrossRef]
  122. Morales-Sandoval, M.; De-la-Parra-Aguirre, R.; Galeana-Zapien, H.; Galaviz-Mosqueda, A. A Three-Tier Approach for Lightweight Data Security of Body Area Networks in E-Health Applications. IEEE Access 2021, 9, 146350–146365. [Google Scholar] [CrossRef]
  123. Tan, X.; Zhang, J.L.; Zhang, Y.J.; Qin, Z.; Ding, Y.; Wang, X.W. A PUF-Based and Cloud-Assisted Lightweight Authentication for Multi-Hop Body Area Network. Tsinghua Sci. Technol. 2021, 26, 36–47. [Google Scholar] [CrossRef]
  124. Liu, J.W.; Zhang, Z.H.; Chen, X.F.; Kwak, K.S. Certificateless Remote Anonymous Authentication Schemes for Wireless Body Area Networks. IEEE Trans. Parallel Distrib. Syst. 2014, 25, 332–342. [Google Scholar] [CrossRef]
  125. He, D.B.; Zeadally, S.; Kumar, N.; Lee, J.H. Anonymous Authentication for Wireless Body Area Networks With Provable Security. IEEE Syst. J. 2017, 11, 2590–2601. [Google Scholar] [CrossRef]
  126. Gangadari, B.R.; Ahamed, S.R. Design of cryptographically secure AES like S-Box using second-order reversible cellular automata for wireless body area network applications. Healthc. Technol. Lett. 2016, 3, 177–183. [Google Scholar] [CrossRef] [Green Version]
  127. Tripathy, A.; Pradhan, S.K.; Nayak, A.K.; Tripathy, A.R. Key Predistribution Technique based on Matrix Decomposition in Wireless Sensor Network. In Proceedings of the 2021 1st Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology(ODICON), Bhubaneswar, India, 8–9 January 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 1–5. [Google Scholar]
  128. Saikia, M.; Hussain, M.A. Combinatorial group based approach for key pre-distribution scheme in wireless sensor network. In Proceedings of the 2017 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India, 5–6 May 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 498–503. [Google Scholar]
  129. Seepers, R.M.; Strydis, C.; Sourdis, I.; Zeeuw, C.I.D. Enhancing Heart-Beat-Based Security for mHealth Applications. IEEE J. Biomed. Health Inform. 2017, 21, 254–262. [Google Scholar] [CrossRef]
  130. Bai, T.; Lin, J.Z.; Li, G.Q.; Wang, H.Q.; Ran, P.; Li, Z.Y.; Li, D.; Pang, Y.; Wu, W.; Jeon, G. A lightweight method of data encryption in BANs using electrocardiogram signal. Futur. Gener. Comp. Syst. 2019, 92, 800–811. [Google Scholar] [CrossRef]
  131. Shen, J.; Chang, S.H.; Shen, J.; Liu, Q.; Sun, X.M. A lightweight multi-layer authentication protocol for wireless body area networks. Futur. Gener. Comp. Syst. 2018, 78, 956–963. [Google Scholar] [CrossRef]
  132. Shou, Y.; Guyennet, H.; Lehsaini, M. Parallel scalar multiplication on elliptic curves in wireless sensor networks. In Proceedings of the International Conference on Distributed Computing and Networking, Mumbai, India, 3–6 January 2013; Springer: Berlin/Heidelberg, Germany, 2013; pp. 300–314. [Google Scholar]
  133. Al-Janabi, S.; Al-Shourbaji, I.; Shojafar, M.; Shamshirband, S. Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications. Egypt. Inform. J. 2017, 18, 113–122. [Google Scholar] [CrossRef] [Green Version]
  134. Cavallari, R.; Martelli, F.; Rosini, R.; Buratti, C.; Verdone, R. A Survey on Wireless Body Area Networks: Technologies and Design Challenges. IEEE Commun. Surv. Tutor. 2014, 16, 1635–1657. [Google Scholar] [CrossRef]
  135. Wang, J.C.; Han, K.N.; Alexandridis, A.; Zilic, Z.; Pang, Y.; Lin, J.Z. An ASIC Implementation of Security Scheme for Body Area Networks. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy, 27–30 May 2018. [Google Scholar]
  136. Rabby, M.K.M.; Alam, M.S.; Shawkat, M. A priority based energy harvesting scheme for charging embedded sensor nodes in wireless body area networks. PLoS ONE 2019, 14, e0214716. [Google Scholar] [CrossRef]
  137. Hao, Y.X.; Peng, L.M.; Lu, H.M.; Hassan, M.M.; Alamri, A. Energy Harvesting Based Body Area Networks for Smart Health. Sensors 2017, 17, 1602. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  138. Xu, Y.H.; Xie, J.W.; Zhang, Y.G.; Hua, M.; Zhou, W. Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network. Sensors 2020, 20, 44. [Google Scholar] [CrossRef] [Green Version]
  139. Hovakeemian, Y.; Naik, K.; Nayak, A. A survey on dependability in Body Area Networks. In Proceedings of the 2011 5th International Symposium on Medical Information and Communication Technology, Montreux, Switzerland, 27–30 March 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 10–14. [Google Scholar]
  140. Dey, N.; Hassanien, A.E.; Bhatt, C.; Ashour, A.; Satapathy, S.C. Internet of Things and Big Data Analytics Toward Next-Generation Intelligence; Springer: Berlin/Heidelberg, Germany, 2018; Volume 35. [Google Scholar]
  141. Samal, T.; Kabat, M.R.; Priyadarshini, S.B.B. Energy Saving Delay Constraint MAC Protocol in Wireless Body Area Network. In Intelligent and Cloud Computing; Springer: Berlin/Heidelberg, Germany, 2021; pp. 623–630. [Google Scholar]
  142. Liu, X.; Zheng, Y.J.; Zhao, B.; Wang, Y.S.; Phyu, M.W. An Ultra Low Power Baseband Transceiver IC for Wireless Body Area Network in 0.18-mu m CMOS Technology. IEEE Trans. Very Large Scale Integr. Syst. 2011, 19, 1418–1428. [Google Scholar] [CrossRef]
  143. Chen, M.; Han, J.; Fang, D.; Zou, Y.; Zeng, X. An ultra low-power and area-efficient baseband processor for WBAN transmitter. In Proceedings of the 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Kaohsiung, Taiwan, 29 October–1 November 2013; IEEE: Piscataway, NJ, USA, 2013; pp. 1–4. [Google Scholar]
  144. Liang, Y.; Zhou, Y.; Li, Y. The design and implementation of IEEE 802.15. 6 Baseband on FPGA. In The International Conference on Health Informatics; Springer: Berlin/Heidelberg, Germany, 2014; pp. 231–235. [Google Scholar]
  145. Chougrani, H.; Schwoerer, J.; Horren, P.H.; Baghdadi, A.; Dehmas, F. UWB-IR digital baseband architecture for IEEE 802.15.6 wireless BAN. In Proceedings of the 2014 21st IEEE International Conference on Electronics, Circuits and Systems (ICECS), Marseille, France, 7–10 December 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 866–869. [Google Scholar]
  146. Mathew, P.; Augustine, L.; Kushwaha, D.; Desalphine, V.; Selvakumar, A.D. Implementation of NB PHY transceiver of IEEE 802.15.6 WBAN on FPGA. In Proceedings of the 2015 International Conference on VLSI Systems, Architecture, Technology and Applications (VLSI-SATA), Bengaluru, India, 8–10 January 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 1–6. [Google Scholar]
  147. Wang, J.C.; Han, K.N.; Alexandridis, A.; Zilic, Z.; Lin, J.Z.; Pang, Y.; Yang, X.M. A baseband processing ASIC for body area networks. J. Ambient Intell. Humaniz. Comput. 2019, 10, 3975–3982. [Google Scholar] [CrossRef]
Figure 1. The basic idea of the WBAN system and its applications.
Figure 1. The basic idea of the WBAN system and its applications.
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Figure 2. The architecture of the WBAN system.
Figure 2. The architecture of the WBAN system.
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Figure 3. Possible communication links for body area networking.
Figure 3. Possible communication links for body area networking.
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Figure 4. BAN interference.
Figure 4. BAN interference.
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Table 1. Some typical applications of WBANs.
Table 1. Some typical applications of WBANs.
MedicalWearable WBANAiding Professional and Amature Athletic Training [17]
Wearable Health Monitoring [18]
Asthma Monitoring [18]
Sleep Staging or Monitoring [19]
Fall Detection [20]
Assessing Soldier Fatigue and Battle Readiness [21]
Patient Monitoring [22]
Telemedicine Systems [23]
Implantable WBAN [24]Cardiovascular Diseases [25]
Diabetes Control [26]
Cancer Detection [27]
Non-MedicalReal-Time Streaming [28]Video steaming
Data file transfer
3D video
Sports
Emergency (non-medical) [13]Life-threatening conditions monitoring:
firefighters, soldiers, deep-sea explorers, and space explorers.
Entertainment Applications [17]Games
Social networking
Table 2. WBAN applications for monitoring various diseases.
Table 2. WBAN applications for monitoring various diseases.
DiseasesCollected DataSensorTransmission Protocol
Depression [43]the location, the posture,
pressure→accelerometers
Barometric pressure sensor data
Pain assessment [44]facial surface EMGwearable sensor with a biosensing facial maskhotspot of a cellphone/a smart gateway/a general router
Heart diseases [45] BP, ECG, SpO2, heart rate, pulse rate, blood fat blood glucose, patients’ risk and locationECG;
blood sensing device
Bluetooth
Knees rehabilitation [39]EMG; ECGAccelerometer; EMG; ECGSmartphones act as a gateway
Knee arthroplasty [40] the angles of knee flexiona master and slave sensor unit, the flexion angle sensormobile telephone network
Chronic diseases [46]heart rate, body temperature, and blood pressurecorresponding three sensorsBluetooth
Hypertension [47]ECG, HRVECGBluetooth
Ubiquitous monitoring
system [41]
four types of vital signs, oxygen saturation, blood pressure, heart
rate, and sugar level
body sensor network3G/Wi-Fi/Bluetooth
Cardiovascular diseases [48] Physiological signals include ECG, BP, stress level, SpO2Accelerometers;
ECG
Mobile device
Heart diseases [42]BP, pulse, body temperature, patient position, ECGECG; airflow; body position; BP sensor; Ambient sensorsWi-Fi/3G/GPRS, ZigBee/Bluetooth
Diabetes [49]blood glucose; Blood pressure; ECGcorresponding sensorsBluetooth
Diabetes [50]EMG, Body temperature, Heart rate, Blood pressure, Blood glucosecorresponding sensorsZigBee
Fall detection [20]real-time activity and fall datamotion sensorsBluetooth
Obesity [51]heart rate, waist circumference, physical activity, weight, glucosechest strap; band; pedometer; pressure sensor; patch GPRS/3G/4G/Wi-Fi
Table 3. Technical requirements of BAN applications.
Table 3. Technical requirements of BAN applications.
ApplicationData RateNodes NumberTopologySetup TimeP2P
Latency
BERDuty CycleBattery Lifetime
ECG72 kb/s<6Star<3 s<250 ms<10–10<10%>1 week
EMG1.54 Mb/s<6Star<3 s<250 ms<10–10<10%>1 week
EEG86.4 kb/s<6Star<3 s<250 ms<10–10<10%>1 week
Drug dosage<1 kb/s2P2P<3 s<250 ms<10–10<1%>24 h
Hearing aid200 kb/s3Star<3 s<250 ms<10–10<10%>40 h
Capsule endoscope1 Mb/s2P2P<3 s<250 ms<10–10<50%>24 h
Deep brain stimulation1 Mb/s2P2P<3 s<250 ms<10–3<50%>3 years
Imaging<10 Mb/s2P2P<3 s<100 ms<10–5<50%>12 h
Audio1 Mb/s3Star<3 s<100 ms<10–5<50%>24 h
temp/respiration/glucose monitor/accelerometer<10 kb/s<12Star<3 s<250 ms<10–10<10%>1 week
Table 4. Summary of technical requirements and their desired ranges.
Table 4. Summary of technical requirements and their desired ranges.
Characteristic RequirementDesired Range
Operating distanceIn, on or around the bodyTypically limited in 3 m
Peak power consumptionUltra-lowµW level in sleep mode, up to 30 mW fully active mode
Data rateScalableFrom 1 kb/s to 10 Mb/s
Network sizeModest ~50 devices per BAN
Frequency bandGlobal unlicensed and medical bandsMedRadio, ISM, WMTS, UWB
MACScalable, reliable, versatile, self-formingLow power, synchronization, listening, wake up, turn-around
QoSReal-time data, periodic parametric data, episodic data and emergency alarmsP2P latency: from 10 ms to 250 ms, BER: from 10–10 to 10–3, reservation and prioritization
Coexistence Coexistence with legacy devices and self-coexistenceSimultaneous co-located operation of up to 10 independent WBANs
TopologyStar, Mesh or TreeSelf-forming, distributed with multi-hop support
EnvironmentBody shadowing, attenuationSeamless operation of multiple nodes in and out of scope with each other
Setup timeNot to be perceived Up to 3 s
Security Various levelsAuthentication, Encryption, Authorization, Privacy, Confidentiality, Message integrity
Safety/BiocompatibilityLong-term continuous use without harmful effectsregulatory requirements
Ergonomic considerationSize, weight, shape and form factors limited by location and organNon-invasive, appropriate size, weight and form factors
Reprogramming, Calibration,
Customization
Personalized, configurable, integrated and context-aware servicesreprogram, recalibrate, tune and configure devices wirelessly
Table 5. Non-invasive and invasive sensors.
Table 5. Non-invasive and invasive sensors.
Non-Invasive SensorsInvasive/Implantable Sensors
EEG/ECG/EMGPacemaker
Position/Motion sensorDeep brain stimulator
BP/SpO2Implantable defibrillators
Glucose sensorCochlear implants
Temperature/Pressure sensorElectronic pill for drug delivery
Pulse oximeterWireless capsule endoscope (electronic pill)
Oxygen, pH valueRetina implants
Table 6. Working mechanism of biosensors and their data rates in WBANs.
Table 6. Working mechanism of biosensors and their data rates in WBANs.
SensorWorking MechanismPower ConsumptionData Rate
Blood sugarUses non-invasive methods such as optical measurement at the eye and breath analysisVery lowLow
Blood pressureMeasures systolic and diastolic pressureHighLow
ECG/EEG/EMGDifferential measurement via electrodes placed on the bodyLowHigh
TemperatureUses an integrated circuit to detect the temperature changes by measuring resistanceLowVery low
RespirationMeasures the dissolved oxygen in a liquid with two electrodes, a cathode and an anode covered by a thin membraneLowLow
AccelerometerMeasures the acceleration relative to freefall in three axesHighHigh
Carbon dioxidemeasures the gas absorption using infrared lightLowLow
GyroscopeMeasures the orientation based on the principles of angular momentumHighHigh
Pulse oximetryMeasures the changes of absorbance ratio by the red or infrared light passing through the fingertip or earlobeLowLow
HumidityMeasures the conductivity changes LowVery low
Table 7. Main specifications for WBAN systems.
Table 7. Main specifications for WBAN systems.
SpecificationsRequirements
TopologyStar or star mesh hybrid, bidirectional link
Devices Number Typically 6, Up to 16
Data Rate10 Kb/s–10 Mb/s
Range >3 m with low data rate under IEEE Channel Model
PER <10% with a link success probability of 95% overall channel conditions
Latency <125 ms (medical), <250 ms (non-medical)
Reliability <1 s for alarm, <10 ms for applications with feedback
Power Consumption>1 year (1% LDC and 500 mAh battery), >9 h (always “on” and 50 mAh battery)
CoexistenceLess than 10 BANs in a volume of 6 m × 6 m × 6 m
Table 8. Medical body area networks’ operation bands.
Table 8. Medical body area networks’ operation bands.
Operation BandsFrequency RangeDisadvantagesApplication
>Medical device radio communications [12] 401–406, 413–419, 426–432, 438–444, 451–457 MHzLimited bandwidth [70]In-body and on-body
Human body communications (HBC)5–50 MHzAffected by the human posture and surroundings [71]In-body [72] and on-body [13]
Medical implant communication service spectrum [70] 402–405 MHzLimited bandwidthIn-body [13]
Wireless medical telemetry service 608–614, 1395–1400,
1427–1432 MHz
Limited bandwidth [70]
Not harmonized globally or regionally [73]
On-body
Industrial, scientific and medical (ISM)2360–2500 MHz2360–2390 MHzNot suitable for critical life situations due to coexistence with aeronautical mobile telemetry [74]On-body
2390–2400 MHzLimited bandwidthOn-body
2400–2500 MHzUnlicensed WBAN, occupied by IEEE 802.15.6, Wi-Fi, Blue-tooth, ZigBee.On-body
Ultra wideband (UWB)3.1–10.6 GHzIncomplete spectrum monitoring campaign [75]On-body
Table 9. Descriptions of IEEE 802.15.6 channel models. Data from Ref. [82].
Table 9. Descriptions of IEEE 802.15.6 channel models. Data from Ref. [82].
ScenarioDescriptionFrequency BandChannel Model
S1Implant to Implant420-405 MHzCM1
S2Implant to Body Surface420-405 MHzCM2
S3Body surface to Body Surface13.5, 50, 400, 600, 900 MHz
2.4, 3.1–10.6 GHz
CM3
S4Body Surface to External900MHz, 2.4, 3.1–10.6 GHzCM4
Table 10. The existing WBAN channel models.
Table 10. The existing WBAN channel models.
Model DescriptionsScenariosMethodPropagation EffectsMobilityLink Type
Dynamic channel model [84]on-body, off-body, and body-to-bodyfinite-difference time-domainfade variation and their corresponding amplitude distributionswalkinghand and thigh
A filter based probabilistic model [85]Intra-WBANorthogonal frequency-division
multiplexing
fading and dynamic variation challengesstatic sitting and dynamic walkinghand
Simulations-based space-time dependent channel model [86]Intra-WBAN, Indoor or Anechoic ChamberCombination of frequency, distances in free space and around the bodySpatial and temporal characteristics-based fading. Shadowing due to body parts length and size.Standing. walking and runningHip to Wrist/Foot/Thigh. Arm to Foot and Head to Head
Measurement-based time-varying model [87]Intra-WBAN, Indoor or Anechoic ChamberTime-frequency and scenario-basedSlow and fast fading. Shadowing correlation between links.Standing still, walking and running on the spotHip to Chest/right thigh/right wrist/right foot. etc.
Measurement and periodic characteristics-based model [88]Intra-WBAN, Indoor or AnechoicDistance and periodic functionSlow and fast fading along with Periodic CorrelationStanding, walking and runningHip to Ankle/Wrist, Wrist to Wrist/Chest, Chest to Wrist/Hip
Simulation-based On and Off Body Multi antenna-channel model [89]Intra-WBAN, Indoor Geometrical-based statistical modelMultipath cluster of scattersWalking Head to Front/Back
IEEE proposed models [90]Intra-WBAN. Indoor or Anechoic ChamberDistance-basedWithout spatial or temporal featuresStaticAround torso and on-front part on the body
Table 11. Three security levels in the IEEE 802.15.6 standard for WBANs.
Table 11. Three security levels in the IEEE 802.15.6 standard for WBANs.
Security LevelsProtection LevelsTransmitted Frames
Level 0: lowest security levelUnsecured CommunicationData are transmitted in unsecured frames without encryption and authentication.
Level 1: medium security levelAuthentication but no EncryptionData are transmitted in plaintext form but secured authentication are involved.
Level 2: highest security levelAuthentication and EncryptionData are transmitted in secured authentication and encryption.
Table 12. Comparison of security techniques in WBANs.
Table 12. Comparison of security techniques in WBANs.
AuthorsResearch IssuesMethodologyOutcome
Bengag et al. [112]Jamming AttacksTwo MAC Protocols involved (ZIGBEE and TMAC)Successful packet delivery rate
Arya et al. [113]Data securityConstant monitoring for critical patientsData authentication and authorization
Hayajneh et al. [114]Lesser usersIncreased storage levelMore users and network lifetime
Thamilarasu et al. [115]Network-level intrusion attacksMachine learning and regression algorithmsAccurate results and lesser resource overhead
Umar et al. [116]Active and passive network attacksEnables mutual trust and used seed update algorithmMinimal routing overhead and less computational cost
Dharshini et al. [117]Vulnerable attacksSecret key extraction with movement aided from DoS attacksMinimum power consumption with high QoS
Suchithra et al. [118].High-rate attacksMaintain the bandwidth conditions in cooperative routingLow-rate attacks
Kumar et al. [119].Several security issuesCloud technology and wireless communicationHigh storage and low computation cost
Rao et al. [120].High residual powerFuzzy logic technique Secure and stable performance
Ali et al. [121].User impersonation attacksBilinear pairing and elliptic curve cryptographyHigh security
Table 13. Performance comparison of the existing ASIC design methods.
Table 13. Performance comparison of the existing ASIC design methods.
AuthorsWang et al. [147]Chen et al. [143]Liu et al. [142]
Process technology (nm)65130180
ModulationDBPSK, DQPSK, D8PSKDBPSK, DQPSK, D8PSKBFSK a
Power supply (V)1.21.01.1
Core power of transmitter (μW)1.699.8934
Core power of receiver (μW)20.4639.6
Maximum throughput (Mbps)100.970.625
Core size (mm2)0.0170.016 b0.31
Size of transmitter (mm2)0.0020.97
Size of receiver (mm2)0.015
a BPSK is not supported in the IEEE 802.15.6 standard. b It only contains the transmitter without the receiver.
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Zhong, L.; He, S.; Lin, J.; Wu, J.; Li, X.; Pang, Y.; Li, Z. Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey. Sensors 2022, 22, 3539. https://doi.org/10.3390/s22093539

AMA Style

Zhong L, He S, Lin J, Wu J, Li X, Pang Y, Li Z. Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey. Sensors. 2022; 22(9):3539. https://doi.org/10.3390/s22093539

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Zhong, Lisha, Shuling He, Jinzhao Lin, Jia Wu, Xi Li, Yu Pang, and Zhangyong Li. 2022. "Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey" Sensors 22, no. 9: 3539. https://doi.org/10.3390/s22093539

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Zhong, L., He, S., Lin, J., Wu, J., Li, X., Pang, Y., & Li, Z. (2022). Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey. Sensors, 22(9), 3539. https://doi.org/10.3390/s22093539

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