Nowadays, smart health is becoming a very important and challenging domain with a huge probability for modern global-level WBAN health applications. It is a purely data-intensive service with two major essentials are Data Sensing and Learning. These two essentials have a great impact on intelligent gathering and analyzing health records. This article is intended to reveal the innovations in the current Wireless Body Area Networks Thesis with its best research topics!!!
For instance:In order to improve the quality of sensed medical image/signal, advanced techniques (learning) have to be used in sensing array to assemble the information for the further analysis process. In such cases, we can use non-intrusive multi-modal approaches and machine learning-based multi-modal approaches. However, these two essential mechanisms have some technical issues particularly in the point of intersection.
What is meant by a wireless body area network?
A Wireless Body Area Network (WBAN) is the individual network of medical sensors and actuators for clinical treatment and prognosis. The sensors are placed inside and outside the human body to screen the continuous health state of the patient. In general, these sensors and the human body are linked up together with wireless communication technologies by considering the following features for effective analysis.
Mobility
Power Utilization
Network Bandwidth
Communication IPs
Transmission Range
Security / Encryption
Authentication Mechanisms
Various Frequency Bands
WBAN Communication Standards
In WBAN, the communication standard used for the low current and short-range is IEEE 802.15.6. It is commonly used in numerous in-bodies and out-bodies area networks applications/services. And, it also supports other kinds of various communications as follows,
Human Body Communication (HBC)
Ultra-wideband (UNB)
Narrowband (NB)
Insteon
Bit rate –13kbps
Frequency Band – 131.65 KHz (power line) and 902 to 924 MHz
Network Structure – Mesh
Coverage Range – Home Area (in meter)
Multiple Access Mechanism – Unknown
Bluetooth Low Energy (BLE)
Bit rate – 1mbps
Frequency band – 2.4 GHz 1SM
Network Structure – Star
Coverage Range – 10 meter
Multiple Access Mechanism – TDMA + PH
Z-Wave
Bit rate – 9.6kbps
Frequency Band – 900MHz 1SM
Network Structure – Mesh
Coverage Range –30 meter
Multiple Access Mechanism – Unknown
Bluetooth 3.0 in High Speed
Bit rate – 3-24mbps
Frequency Band – 2.4 GHz ISM
Network Structure – Star
Coverage Range -10 meter
Multiple Access Mechanism – FH+ CSMA/TDMA (Wi-Fi)
ANT
Bit rate – 1mbps
Frequency Band – 2.4 GHz ISM
Network Structure – Star/Mesh
Coverage Range – local area (in meter)
Multiple Access Mechanism – TDMA
UWB (ECMA – 368)
Bit rate – 480mbps
Frequency Band – 3.1 to 10.6 GHz
Network Structure – Star
Coverage Range – <10 meter
Multiple Access Mechanism – TDMA/CSMA
RunBee (IEEE 1902.1)
Bit rate – 9.6kbps
Frequency Band –131 KHz
Network Structure – Peer-to-Peer (P2P)
Coverage Range – 30 meter
Multiple Access Mechanism –Unknown
RFID (ISO/IEC 18000-6)
Bit rate – 10-100kbps
Frequency Band – 860 to 960 MHz
Network Structure – Peer-to-Peer (P2P)
Coverage Range – 1 to 100 (in meter)
Multiple Access Mechanism – Binary tree / Slotted Aloha
ZigBee (IEEE 802.15.4)
Bit rate –250kbps
Frequency Band – ISM
Network Structure – Mesh or Star
Coverage Range – 30 to 100 meter
Multiple Access Mechanism – CSMA
Further, if you want more information on WBAN advancement, make a bond with us. Since we have developed an infinite number of projects in WBAN through smart resource teams from different parts of the world. So, we have sufficient updates in all recent WBAN research areas with their high-demanding innovative Wireless Body Area Networks Thesis topics. For your information, we have given you how the pulse data is fetched from sensors and how they are processed in different layers to yield the final outcome. Let’s see:
Typical Layout of WBAN
Internet of Things layer
Sense the pulse data
Gather the data through different implanted sensors and variables
Pre-process the collected data and remove unwanted data on the device itself
Fog layer for Distributed Computing
Distribute the data to the nearer fog node in fog layers
Communication Layer
Supported Technologies: ZigBee, BLE, 6LoWPAN, Wi-Fi, NB-IoT and more
Distribute the data between deployed devices and processor and again do the same process in reverse i.e., vice-versa
Ensure the data security and integrity during distribution with greater reliability and lower delay
Processing Layer
Extract the essential feature from pre-processed data
Generate the warnings if required
Analyze the data in large volume
Store the data in the database in full security
Next, we can see some wireless body areas challenges still looking for the best solutions to be solved. These are issues has less attention among scholars because of its complication. So, it is a good opportunity for scholars to uplift their research weightage on selecting the topics related to these areas.
WBAN Research Issues
Issues regarding usage of health applications and service in mobility
Inconvenience in assuring security and confidentiality of the sensitive health info
Yielding precise results for assisting physicians to take effective decisions
Challenges in real-world deployment, computing complexity, result accuracy, efficiency (in classification), and many more.
Maintaining the real-world medical electronic records in deployed health applications
Key Enabling Technologies of WBAN
In spite of issues, the WBAN has gained incredible attention in the medical research field for constantly creating new innovations in IoMT. In this, the heterogeneous data analysis and storage capabilities are improved by using learning approaches and distributed computing services. Further, we have also included the latest Wireless Body Area Networks Thesis topics for your knowledge.
PhD / MS Thesis Topics in WBAN
Cloud-based WBAN in smart health informatics system
Deep learning-based Energy-Aware Data Analytics in IoMT
Real-world applications for Wearable/Implanted devices
Flexible wearable sensors for medical diagnosis
WBAN antenna/channel modeling and characterization
Advanced IoMT applications for resource-constrained medical sensors
No matter what topic you are choosing, the matter is what techniques and algorithms you are choosing for solving the problem. Since it showcases your ability and smart moves in solving complicated problems. So, we will help you to select the appropriate solutions for your handpicked topic. Further, we also create our own innovative models, algorithms, and architecture for your research topic in WBAN if there is a need. Some of the techniques for WBAN development are given below,
Methodologies for WBAN
Chaotic systems
Artificial Life / A-Life
Probabilistic Rough Sets
Deep Neural Learning
Optimization Algorithms
Evolutionary Algorithms
Artificial Immune Systems (AIS)
Fuzzy Set and Logics
Computational Mechanisms
Artificial Neural Networks (ANN)
Next, we can see the performance metrics used for the WBAN projects to evaluate the working and behavior of the developed system. These are metrics are suitable for bringing out the actual efficiency of the system.
Simulation Parameters of WBAN
Jitter – <50 ms
Number of Devices– 6 to 256
Data Transfer Rate – 10 Kb/s to 10Mb/s
Setup Time – < 3s (add and delete)
Intra-Coexistence – 10 WBANs (6x6x6 m volume)
Network Structure – bidirectional link and one/two-hope star
Delay – <250 ms (for non-health data) and <125 ms (for health data)
Range – 3 m along with minimum bit rate (for IEEE Channel)
Dependability – <10 ms (for applications) and <1 s (for alarm)
PER – <10% along with 95% link POS over the whole network channel
Inter-Coexistence – Short-range Communication Environs (Such as: Bluetooth, Wi-Fi, etc.)
Power Utilization – >9h (constantly ON + 50 mAh battery) and >1 year (1% LDC + 500 mAh battery)
For illustration purposes, we have taken four key metrics that are used in the majority of the WBAN applications/systems for assessing the performance. In the following, we mentioned what exactly these metrics measures with their purposes.
How WBAN performance is measured?
End-to-End Latency:
It measures the total time taken by packets to reach the destination from the source.
It will vary each time based on traffic in the channel so we need to calculate the time for each and every packet and then make an average to find the actual delay.
Throughput:
It measures the number of successful packets delivered to the destination
And, it depicts the ratio between the number of packets passed/generated in the source and the number of packets acknowledged in the destination
Also, it states in percentage the ratio of Packet
Delivery:
It is the same as throughput which measures the ratio of the number of packets in source and destination.
Also, the packet length is represented in Mbps or Kbps.
Jitter:
It measures the differences in the packet delivery since all packets do not have the same delay.
So far, we have discussed the research and development viewpoints, now we can see the Wireless Body Area Networks Thesis writing. Since it is equally important two R&D phases describe the capabilities and competence to do the research work in a chain of valuable words. Here, our native writers have given you some important steps that we follow in writing the “perfect” thesis.
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MS
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75
117
95
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98
119
206
OMNET++
103
95
87
OPNET
36
64
89
QULANET
30
76
60
MININET
71
62
74
MATLAB
96
185
180
LTESIM
38
32
16
COOJA SIMULATOR
35
67
28
CONTIKI OS
42
36
29
GNS3
35
89
14
NETSIM
35
11
21
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4
8
9
TRANS
9
5
4
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8
8
12
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6
10
6
RTOOL
13
15
8
KATHARA SHADOW
9
8
9
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8
7
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9
9
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6
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4
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8
7
9
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7
11
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BOSON NETSIM
6
8
9
VIRL
9
9
8
CISCO PACKET TRACER
7
7
10
SWAN
9
19
5
JAVASIM
40
68
69
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7
9
8
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5
7
4
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7
8
6
PETRI NET
4
6
4
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5
10
5
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32
64
24
DIVERT
4
9
8
TINY OS
19
27
17
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7
8
6
OPENPANA
8
9
9
SECURE CRT
7
8
7
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6
7
5
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7
19
6
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5
12
9
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8
10
7
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5
7
9
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6
8
7
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9
17
8
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7
5
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5
10
9
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7
8
4
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