Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey
Abstract
:1. Introduction
2. WBAN Applications for Health Monitoring
3. WBAN Sensor Techniques
4. Wireless Transmission in WBANs
4.1. Frequency Bands for WBAN Channel Models
4.2. WBAN Channel Models
4.3. Physical (PHY) Layer and Medium Access Control (MAC) Layer
4.4. Network Protocols
- 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
5. Security and Privacy
6. Energy Efficiency
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Medical | Wearable WBAN | Aiding 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-Medical | Real-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 |
Diseases | Collected Data | Sensor | Transmission Protocol |
---|---|---|---|
Depression [43] | the location, the posture, pressure→accelerometers | Barometric pressure sensor data | — |
Pain assessment [44] | facial surface EMG | wearable sensor with a biosensing facial mask | hotspot 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 location | ECG; blood sensing device | Bluetooth |
Knees rehabilitation [39] | EMG; ECG | Accelerometer; EMG; ECG | Smartphones act as a gateway |
Knee arthroplasty [40] | the angles of knee flexion | a master and slave sensor unit, the flexion angle sensor | mobile telephone network |
Chronic diseases [46] | heart rate, body temperature, and blood pressure | corresponding three sensors | Bluetooth |
Hypertension [47] | ECG, HRV | ECG | Bluetooth |
Ubiquitous monitoring system [41] | four types of vital signs, oxygen saturation, blood pressure, heart rate, and sugar level | body sensor network | 3G/Wi-Fi/Bluetooth |
Cardiovascular diseases [48] | Physiological signals include ECG, BP, stress level, SpO2 | Accelerometers; ECG | Mobile device |
Heart diseases [42] | BP, pulse, body temperature, patient position, ECG | ECG; airflow; body position; BP sensor; Ambient sensors | Wi-Fi/3G/GPRS, ZigBee/Bluetooth |
Diabetes [49] | blood glucose; Blood pressure; ECG | corresponding sensors | Bluetooth |
Diabetes [50] | EMG, Body temperature, Heart rate, Blood pressure, Blood glucose | corresponding sensors | ZigBee |
Fall detection [20] | real-time activity and fall data | motion sensors | Bluetooth |
Obesity [51] | heart rate, waist circumference, physical activity, weight, glucose | chest strap; band; pedometer; pressure sensor; patch | GPRS/3G/4G/Wi-Fi |
Application | Data Rate | Nodes Number | Topology | Setup Time | P2P Latency | BER | Duty Cycle | Battery Lifetime |
---|---|---|---|---|---|---|---|---|
ECG | 72 kb/s | <6 | Star | <3 s | <250 ms | <10–10 | <10% | >1 week |
EMG | 1.54 Mb/s | <6 | Star | <3 s | <250 ms | <10–10 | <10% | >1 week |
EEG | 86.4 kb/s | <6 | Star | <3 s | <250 ms | <10–10 | <10% | >1 week |
Drug dosage | <1 kb/s | 2 | P2P | <3 s | <250 ms | <10–10 | <1% | >24 h |
Hearing aid | 200 kb/s | 3 | Star | <3 s | <250 ms | <10–10 | <10% | >40 h |
Capsule endoscope | 1 Mb/s | 2 | P2P | <3 s | <250 ms | <10–10 | <50% | >24 h |
Deep brain stimulation | 1 Mb/s | 2 | P2P | <3 s | <250 ms | <10–3 | <50% | >3 years |
Imaging | <10 Mb/s | 2 | P2P | <3 s | <100 ms | <10–5 | <50% | >12 h |
Audio | 1 Mb/s | 3 | Star | <3 s | <100 ms | <10–5 | <50% | >24 h |
temp/respiration/glucose monitor/accelerometer | <10 kb/s | <12 | Star | <3 s | <250 ms | <10–10 | <10% | >1 week |
Characteristic | Requirement | Desired Range |
---|---|---|
Operating distance | In, on or around the body | Typically limited in 3 m |
Peak power consumption | Ultra-low | µW level in sleep mode, up to 30 mW fully active mode |
Data rate | Scalable | From 1 kb/s to 10 Mb/s |
Network size | Modest | ~50 devices per BAN |
Frequency band | Global unlicensed and medical bands | MedRadio, ISM, WMTS, UWB |
MAC | Scalable, reliable, versatile, self-forming | Low power, synchronization, listening, wake up, turn-around |
QoS | Real-time data, periodic parametric data, episodic data and emergency alarms | P2P latency: from 10 ms to 250 ms, BER: from 10–10 to 10–3, reservation and prioritization |
Coexistence | Coexistence with legacy devices and self-coexistence | Simultaneous co-located operation of up to 10 independent WBANs |
Topology | Star, Mesh or Tree | Self-forming, distributed with multi-hop support |
Environment | Body shadowing, attenuation | Seamless operation of multiple nodes in and out of scope with each other |
Setup time | Not to be perceived | Up to 3 s |
Security | Various levels | Authentication, Encryption, Authorization, Privacy, Confidentiality, Message integrity |
Safety/Biocompatibility | Long-term continuous use without harmful effects | regulatory requirements |
Ergonomic consideration | Size, weight, shape and form factors limited by location and organ | Non-invasive, appropriate size, weight and form factors |
Reprogramming, Calibration, Customization | Personalized, configurable, integrated and context-aware services | reprogram, recalibrate, tune and configure devices wirelessly |
Non-Invasive Sensors | Invasive/Implantable Sensors |
---|---|
EEG/ECG/EMG | Pacemaker |
Position/Motion sensor | Deep brain stimulator |
BP/SpO2 | Implantable defibrillators |
Glucose sensor | Cochlear implants |
Temperature/Pressure sensor | Electronic pill for drug delivery |
Pulse oximeter | Wireless capsule endoscope (electronic pill) |
Oxygen, pH value | Retina implants |
Sensor | Working Mechanism | Power Consumption | Data Rate |
---|---|---|---|
Blood sugar | Uses non-invasive methods such as optical measurement at the eye and breath analysis | Very low | Low |
Blood pressure | Measures systolic and diastolic pressure | High | Low |
ECG/EEG/EMG | Differential measurement via electrodes placed on the body | Low | High |
Temperature | Uses an integrated circuit to detect the temperature changes by measuring resistance | Low | Very low |
Respiration | Measures the dissolved oxygen in a liquid with two electrodes, a cathode and an anode covered by a thin membrane | Low | Low |
Accelerometer | Measures the acceleration relative to freefall in three axes | High | High |
Carbon dioxide | measures the gas absorption using infrared light | Low | Low |
Gyroscope | Measures the orientation based on the principles of angular momentum | High | High |
Pulse oximetry | Measures the changes of absorbance ratio by the red or infrared light passing through the fingertip or earlobe | Low | Low |
Humidity | Measures the conductivity changes | Low | Very low |
Specifications | Requirements |
---|---|
Topology | Star or star mesh hybrid, bidirectional link |
Devices | Number Typically 6, Up to 16 |
Data Rate | 10 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) |
Coexistence | Less than 10 BANs in a volume of 6 m × 6 m × 6 m |
Operation Bands | Frequency Range | Disadvantages | Application | |
---|---|---|---|---|
>Medical device radio communications [12] | 401–406, 413–419, 426–432, 438–444, 451–457 MHz | Limited bandwidth [70] | In-body and on-body | |
Human body communications (HBC) | 5–50 MHz | Affected by the human posture and surroundings [71] | In-body [72] and on-body [13] | |
Medical implant communication service spectrum [70] | 402–405 MHz | Limited bandwidth | In-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 MHz | 2360–2390 MHz | Not suitable for critical life situations due to coexistence with aeronautical mobile telemetry [74] | On-body |
2390–2400 MHz | Limited bandwidth | On-body | ||
2400–2500 MHz | Unlicensed WBAN, occupied by IEEE 802.15.6, Wi-Fi, Blue-tooth, ZigBee. | On-body | ||
Ultra wideband (UWB) | 3.1–10.6 GHz | Incomplete spectrum monitoring campaign [75] | On-body |
Scenario | Description | Frequency Band | Channel Model |
---|---|---|---|
S1 | Implant to Implant | 420-405 MHz | CM1 |
S2 | Implant to Body Surface | 420-405 MHz | CM2 |
S3 | Body surface to Body Surface | 13.5, 50, 400, 600, 900 MHz 2.4, 3.1–10.6 GHz | CM3 |
S4 | Body Surface to External | 900MHz, 2.4, 3.1–10.6 GHz | CM4 |
Model Descriptions | Scenarios | Method | Propagation Effects | Mobility | Link Type |
---|---|---|---|---|---|
Dynamic channel model [84] | on-body, off-body, and body-to-body | finite-difference time-domain | fade variation and their corresponding amplitude distributions | walking | hand and thigh |
A filter based probabilistic model [85] | Intra-WBAN | orthogonal frequency-division multiplexing | fading and dynamic variation challenges | static sitting and dynamic walking | hand |
Simulations-based space-time dependent channel model [86] | Intra-WBAN, Indoor or Anechoic Chamber | Combination of frequency, distances in free space and around the body | Spatial and temporal characteristics-based fading. Shadowing due to body parts length and size. | Standing. walking and running | Hip to Wrist/Foot/Thigh. Arm to Foot and Head to Head |
Measurement-based time-varying model [87] | Intra-WBAN, Indoor or Anechoic Chamber | Time-frequency and scenario-based | Slow and fast fading. Shadowing correlation between links. | Standing still, walking and running on the spot | Hip to Chest/right thigh/right wrist/right foot. etc. |
Measurement and periodic characteristics-based model [88] | Intra-WBAN, Indoor or Anechoic | Distance and periodic function | Slow and fast fading along with Periodic Correlation | Standing, walking and running | Hip 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 model | Multipath cluster of scatters | Walking | Head to Front/Back |
IEEE proposed models [90] | Intra-WBAN. Indoor or Anechoic Chamber | Distance-based | Without spatial or temporal features | Static | Around torso and on-front part on the body |
Security Levels | Protection Levels | Transmitted Frames |
---|---|---|
Level 0: lowest security level | Unsecured Communication | Data are transmitted in unsecured frames without encryption and authentication. |
Level 1: medium security level | Authentication but no Encryption | Data are transmitted in plaintext form but secured authentication are involved. |
Level 2: highest security level | Authentication and Encryption | Data are transmitted in secured authentication and encryption. |
Authors | Research Issues | Methodology | Outcome |
---|---|---|---|
Bengag et al. [112] | Jamming Attacks | Two MAC Protocols involved (ZIGBEE and TMAC) | Successful packet delivery rate |
Arya et al. [113] | Data security | Constant monitoring for critical patients | Data authentication and authorization |
Hayajneh et al. [114] | Lesser users | Increased storage level | More users and network lifetime |
Thamilarasu et al. [115] | Network-level intrusion attacks | Machine learning and regression algorithms | Accurate results and lesser resource overhead |
Umar et al. [116] | Active and passive network attacks | Enables mutual trust and used seed update algorithm | Minimal routing overhead and less computational cost |
Dharshini et al. [117] | Vulnerable attacks | Secret key extraction with movement aided from DoS attacks | Minimum power consumption with high QoS |
Suchithra et al. [118]. | High-rate attacks | Maintain the bandwidth conditions in cooperative routing | Low-rate attacks |
Kumar et al. [119]. | Several security issues | Cloud technology and wireless communication | High storage and low computation cost |
Rao et al. [120]. | High residual power | Fuzzy logic technique | Secure and stable performance |
Ali et al. [121]. | User impersonation attacks | Bilinear pairing and elliptic curve cryptography | High security |
Authors | Wang et al. [147] | Chen et al. [143] | Liu et al. [142] |
---|---|---|---|
Process technology (nm) | 65 | 130 | 180 |
Modulation | DBPSK, DQPSK, D8PSK | DBPSK, DQPSK, D8PSK | BFSK a |
Power supply (V) | 1.2 | 1.0 | 1.1 |
Core power of transmitter (μW) | 1.69 | 9.89 | 34 |
Core power of receiver (μW) | 20.46 | — | 39.6 |
Maximum throughput (Mbps) | 10 | 0.97 | 0.625 |
Core size (mm2) | 0.017 | 0.016 b | 0.31 |
Size of transmitter (mm2) | 0.002 | 0.97 | — |
Size of receiver (mm2) | 0.015 | — | — |
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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
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
Chicago/Turabian StyleZhong, 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
APA StyleZhong, 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