Healthcare Cyber-Physical System (H-CPS)
Transcript of Healthcare Cyber-Physical System (H-CPS)
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Healthcare Cyber-
Physical System (H-CPS)
1Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
21 May 2020
Oriental University, Indore21 May 2020
Saraju P. Mohanty
University of North Texas, USA.
Email: [email protected]
More Info: http://www.smohanty.org
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Outline
◼ Healthcare → Smart Healthcare
◼ Smart Healthcare - Characteristics
◼ Smart Healthcare - Components and
Technologies
◼ Smart Healthcare - Challenges and Solutions
◼ Smart Healthcare - Selected Examples
21 May 2020 2Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
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Healthcare
to
Smart Healthcare
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Human Body and Health
21 May 2020 4Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Human Body
◼ From an engineering
perspective, the human body
can be defined as a
combination of multi-
disciplinary subsystems
(electro-mechanical-
chemical…).
Health
◼ Human health is a state of
complete physical, mental
and social well-being.
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Traditional Healthcare
21 May 2020 5Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Patient
Primary Care
Specialists
Hospitals
Laboratories
Long Term Care
Public Health
Pharmacies
Health Insurance
➢Physical presence needed
➢Deals with many stakeholders
➢Stakeholders may not interact
➢May not be personalized
➢Not much active feedback
➢No follow-up from physicians
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Electronic Health (eHealth)
◼ eHealth: The use of information and
communication technologies (ICT) to improve
healthcare services.
21 May 2020 6Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: W. O. Nijeweme-d'Hollosy, L. van Velsen, M. Huygens and H. Hermens, "Requirements for and Barriers towards
Interoperable eHealth Technology in Primary Care," IEEE Internet Computing, vol. 19, no. 4, pp. 10-19, July-Aug. 2015.
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Telemedicine
21 May 2020 7Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Telemedicine is the use of telecommunication and information
technology to provide clinical health care from a distance.
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Mobile Health (mHealth)
◼ mHealth: Healthcare supported
by mobile devices that uses
mobile telecommunications and
multimedia technologies for the
delivery of healthcare services
and health information.
21 May 2020 8Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: H. Zhu, C. K. Wu, C. H. KOO, Y. T. Tsang, Y.Liu, H. R. Chi, and K. F. Tsang, "Smart Healthcare in the Era of
Internet-of-Things", IEEE Consumer Electronics Magazine, vol. 8, no. 5, pp. 26-30, Sep 2019.
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Connected Health (cHealth)
21 May 2020 9Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: https://www.slideshare.net/tibisay_hernandez/connected-health-venfinal
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Smart Healthcare (sHealth)
21 May 2020 10Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
IoMT
Embedded
Skin Patches
Headband with Embedded
Neurosensors
Fitness
Trackers
Sethi 2017: JECE 2017
Quality and
sustainable healthcare
with limited resources.Source: P. Sundaravadivel, E. Kougianos, S. P. Mohanty, and M. Ganapathiraju, “Everything You Wanted to Know
about Smart Health Care”, IEEE Consumer Electronics Magazine (MCE), Vol. 7, Issue 1, January 2018, pp. 18-28.
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Wearable Medical Devices (WMDs)
21 May 2020 11Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Embedded
Skin
Patches
Headband with
Embedded
NeurosensorsFitness
Trackers
Source: https://www.empatica.com/embrace2/
Medical grade smart
watch to detect seizure
Insulin Pump
Source: https://www.webmd.com
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Implantable Medical Devices (IMDs)
21 May 2020 12Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Implantable MEMS
Device
Pill Camera
Source: http://web.mit.edu/cprl/www/research.shtml
Data Recorder
LEDProcessor Antenna
Electromagnet
Battery
RF Transmitter
Image Sensors
Source: P. Sundaravadivel, E. Kougianos, S. P. Mohanty, and M. Ganapathiraju,
“Everything You Wanted to Know about Smart Health Care”, IEEE Consumer
Electronics Magazine (MCE), Volume 7, Issue 1, January 2018, pp. 18-28.
Collectively:
Implantable and Wearable
Medical Devices (IWMDs)
Brain
Pacemaker
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What is Smart Healthcare?
21 May 2020 13Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Smart Healthcare
Conventional Healthcare
+ Body sensors
+ Smart Technologies
+Information & Communication Technology (ICT)
+ AI/ML
Source: P. Sundaravadivel, E. Kougianos, S. P. Mohanty, and M. Ganapathiraju, “Everything You Wanted to Know about
Smart Health Care”, IEEE Consumer Electronics Magazine (MCE), Volume 7, Issue 1, January 2018, pp. 18-28.
Healthcare Cyber-Physical Systems (CPS)
Internet of Medical Things (IoMT) Internet of Health Things (IoHT)
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Smart Healthcare - 4-Layer Architecture
21 May 2020 14Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: M. Ghamari, B. Janko, R.S. Sherratt, W. Harwin, R. Piechockic, and C. Soltanpur, "A Survey on Wireless Body
Area Networks for eHealthcare Systems in Residential Environments", Sensors, 2016. 16(6): p. 831.
Implantable
BANs (IBAN)
Body Area
Network (BAN)
Decision
Measuring
Unit
(DMU)
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Smart Healthcare -
Characteristics
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Smart Healthcare
21 May 2020 16Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Healthy Living
• Fitness Tracking
• Disease Prevention
• Food monitoring
Home Care
• Mobile health
• Telemedicine
• Self-management
• Assisted Living
Acute Care
• Hospital
• Specialty clinic
• Nursing Home
• Community Hospital
January 2018
Frost and Sullivan predicts smart healthcare
market value to reach US$348.5 billion by 2025.
Source: P. Sundaravadivel, E. Kougianos, S. P. Mohanty, and M. Ganapathiraju,
“Everything You Wanted to Know about Smart Health Care”, IEEE Consumer
Electronics Magazine (MCE), Vol. 7, Issue 1, January 2018, pp. 18-28.
Internet of Medical Things (IoMT)
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IoMT - Impacts
21 May 2020 17Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: Y. Shelke and A. Sharma, "Internet of Medical Things", 2016, Aranca, https://www.aranca.com/knowledge-
library/special-reports/ip-research/the-internet-of-medical-things-iomt, Last Visited 10/18/2017.
Patient-specific care
with context and
enabled through
past health records.
Real-time
tracking and
intervention
Data driven
health prediction
Development of evidence-
based guidelines which can
helpful to incorporate the local
intelligence in future machine.
Improved inter-
device connection
and synchronization
Healthcare Cyber-Physical Systems (CPS)
Patient Healthcare
Provider
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7PPersuasive
Predictive
Participatory
Preventative
Programmable Perpetual
Source: H. Zhu, C. K. Wu, C. H. KOO, Y. T. Tsang, Y.Liu, H. R. Chi, and K. F. Tsang, "Smart Healthcare in the Era of
Internet-of-Things", IEEE Consumer Electronics Magazine, vol. 8, no. 5, pp. 26-30, Sep 2019.
Smart Healthcare – 7Ps
21 May 2020Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
18
Personalized
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Smart Healthcare - Tasks
21 May 2020 19Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: Hongxu Yin, Ayten Ozge Akmandor, Arsalan Mosenia and Niraj K. Jha (2018), "Smart Healthcare", Foundations
and Trends® in Electronic Design Automation: Vol. 12: No. 4, pp 401-466. http://dx.doi.org/10.1561/1000000054
Daily Healthcare Clinical Healthcare
Smart Healthcare - Tasks
(ii) Daily Diagnosis
•Wearable Medical Sensor
(WMS)-based diagnosis
(i) Daily Prevention
• Fitness checkup
• Activity tracking
• Emotion analysis
• Disease risk prediction
(v) Daily Treatment
• Out-patient therapy
• Ambient healthcare
• Disease status monitoring
• Precision medicine
(iv) Clinical Treatment
• Treatment plan selection
• Treatment method evaluation
• In-patient monitoring
• Precision medicine
(iii) Clinical Diagnosis
•Physician variance reduction
•Personalized diagnosis
Clinical Boundary
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IoMT Advantages & Limitations
21 May 2020 20Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: Y. Shelke and A. Sharma, "Internet of Medical Things", 2016, Aranca, https://www.aranca.com/knowledge-
library/special-reports/ip-research/the-internet-of-medical-things-iomt, Last Visited 10/18/2017.
AdvantagesPatients/Users➢ Real-time interventions in emergency
➢ Cost reduction
➢ Reduced morbidity and financial
burden due to less follow up visits
Healthcare Service Providers
➢ Optimal utilization of resources
➢ Reduced response time in
emergency
Manufacturers
➢ Standardization/compatibility and
uniformity of data available
➢ Capability to sense and
communicate health related
information to remote location
LimitationsTechnical Challenges
❖ Security of IoT data - hacking and
unauthorized use of IoT
❖ Lack of standards and
communication protocols
❖ Errors in patient data handling
❖ Data integration
❖ Need for medical expertise
❖ Managing device diversity and
interoperability
❖ Scale, data volume and performance
Market Challenges
❖ Physician compliance
❖ Data overload on healthcare facility
❖ Mobile hesitation
❖ Security policy compliance
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Smart Healthcare -
Components
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Internet of Medical Things (IoMT)
Source: http://www.icemiller.com/ice-on-fire-insights/publications/the-internet-of-health-things-privacy-and-security/
IoMT is a collection of medical sensors, devices, healthcare
database, and applications that connected through Internet.
Source: http://internetofthingsagenda.techtarget.com/definition/IoMT-Internet-of-Medical-Things
Healthcare CPS -- Prof./Dr. Saraju P. Mohanty2421 May 2020
Internet of Health Things (IoHT)
Requires: ❖ Data and Device Security
❖ Data Privacy
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Electroencephalogram (EEG)
Electrocardiogram (ECG)
Oximeter
Electrodermograph
Electromyograph
Photoplethysmography (PPG)
Glucose monitoring
Thermal sensor
Pulse sensor
Accelerometer
Gyroscope
Magnetometer
Altimeter
Smart Pill
Smart Healthcare Sensors
21 May 2020 25Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Types of Sensors
Brain related applications
Imaging applications
Heart related applications
Skin related applications
Blood related applications
Ingestible sensors
Motion Detection
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Smart Healthcare Communication
21 May 2020 26Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: V. Custodio, F.J. Herrera, G. López, and J. I. Moreno, “A Review on Architectures and Communications
Technologies for Wearable Health-Monitoring Systems”, Sensors, 2012. 12(10): p. 13907-13946.
Technology Frequency Band Data Rate Range Transmission Power
Bluetooth 4.0 (LE) 2.4 GHz 50–200 Kbps
30 m ~10 mW
Zigbee 868 MHz/ 915 MHz/ 2.4 GHz
20–250 Kbps
30 m 30 mW
ANT 2400-2485 MHz 1 Mbps Up to 10 m
0.01–1 mW
IEEE 802.15.6 2,360-2,400/ 2,400-2,483.5 MHz UWB: 3–10 GHz HBC: 16/27 MHz
NB: 57.5–485.7 Kbps UWB: 0.5–10 Mbps
1.2 m 0.1 μW
Medical Implant Communications Service (MICS)
402-405 MHz Up to 500 Kbps
2 m 25 μW
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Smart Healthcare - Framework
21 May 2020 27Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: Hongxu Yin, Ayten Ozge Akmandor, Arsalan Mosenia and Niraj K. Jha (2018), "Smart Healthcare", Foundations
and Trends® in Electronic Design Automation, Vol. 12: No. 4, pp 401-466. http://dx.doi.org/10.1561/1000000054
Systems & Analytics
•Health cloud server
•Edge server
•Implantable Wearable
Medical Devices (IWMDs)
Data
•Physiological data
•Environmental data
•Genetic data
•Historical records
•Demographics
Systems & Analytics
• Clinical Decision Support Systems
(CDSSs)
• Electronic Health Records (EHRs)
Machine Learning Engine
Data
•Physician observations
•Laboratory test results
•Genetic data
•Historical records
•Demographics
Smart Healthcare - System and Data Analytics : To Perform Tasks
Machine Learning Engine
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Electronics Health Record (EHR)
21 May 2020 28Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Electronic Health
Record (EHR) is the
systematized
collection of health
information of
individuals stored in a
digital format.
Created by various
health providers such
as hospitals and
clinics.
Electronic Medical Record (EMR)
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Machine Learning (ML)
Supervised ML
◼ Data instance: features + label
◼ Data instance sets: training, testing
◼ Inference: Mathematical Model
Enhancement Techniques
◼ Ensemble method: base vs. meta
◼ Feature filtering: redundant vs. informative
21 May 2020 29Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: Hongxu Yin, Ayten Ozge Akmandor, Arsalan Mosenia and Niraj K. Jha (2018), "Smart Healthcare", Foundations
and Trends® in Electronic Design Automation, Vol. 12: No. 4, pp 401-466. http://dx.doi.org/10.1561/1000000054.
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Brain Computer Interface (BCI)
21 May 2020 Healthcare CPS -- Prof./Dr. Saraju P. Mohanty 30
Sources: http://brainpedia.org/elon-musk-wants-merge-human-brain-ai-launches-neuralink/
“Currently, people interact with their devices by thumb-typing on their
phones. A high-bandwidth interface to the brain would help achieve a
symbiosis between human and machine intelligence and could make
humans more useful in an AI-driven world.”
-- Neuralink - neurotechnology company - Elon Musk.
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Virtual Reality in Healthcare
21 May 2020 32Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: https://touchstoneresearch.com/tag/applied-vr/
In Surgery For TherapySource: http://medicalfuturist.com/5-ways-medical-vr-is-changing-healthcare/
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Crowdsourcing for Smart Cities
21 May 2020 33Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Crowdsourcing for Smart Cities
Smart Healthcare Data Gathering (Diet
Dataset, Healthcare Dataset)
Urban Data Gathering (Bike
Data, Energy Usage Data)
City Service Monitoring (Park
Maintenance, Waste Disposal)
Last-Mile Logistics (Package
Pickup Delivery)
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Smart Healthcare –
Challenges and Solutions
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Requirements
Low power
Higher efficiency
Small form factor
Inter operability
Continuous connectivity
High speed
Security
Privacy
Smart Healthcare Architecture –
Requirements
21 May 2020 36Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
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Data
Quality
Validity
Reliability
Objectivity
Generalizability
Utility
Completeness
Relevance
Integrity
Smart Healthcare – Data Quality
21 May 2020Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
37
Source: H. Zhu, C. K. Wu, C. H. KOO, Y. T. Tsang, Y.Liu, H. R. Chi, and K. F. Tsang, "Smart Healthcare in the Era of
Internet-of-Things", IEEE Consumer Electronics Magazine, vol. 8, no. 5, pp. 26-30, Sep 2019.
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21 May 2020 39
Selected Smart Healthcare
Security/Privacy Challenges
Data Eavesdropping
Data Confidentiality
Data Privacy
Location Privacy
Identity Threats
Access Control
Unique Identification
Data Integrity
Source: P. Sundaravadivel, E. Kougianos, S. P. Mohanty, and M. Ganapathiraju, “Everything You Wanted to Know about
Smart Health Care”, IEEE Consumer Electronics Magazine (CEM), Volume 7, Issue 1, January 2018, pp. 18-28.
Smart Healthcare - Security Challenges
Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
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Health Insurance Portability and
Accountability Act (HIPPA)
21 May 2020 43Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
HIPPA Privacy Violation by Types
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IoMT Security Issue is Real & Scary
◼ Insulin pumps are vulnerable to hacking, FDA warns
amid recall:
https://www.washingtonpost.com/health/2019/06/28/insulin-pumps-are-
vulnerable-hacking-fda-warns-amid-recall/
◼ Software vulnerabilities in some medical devices could
leave them susceptible to hackers, FDA warns:
https://www.cnn.com/2019/10/02/health/fda-medical-devices-hackers-
trnd/index.html
◼ FDA Issues Recall For Medtronic mHealth Devices Over
Hacking Concerns:
https://mhealthintelligence.com/news/fda-issues-recall-for-medtronic-
mhealth-devices-over-hacking-concerns
21 May 2020 46Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
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Implanted Medical Devices - Attacks
21 May 2020 49Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: Emily Waltz, Can "Internet-of-Body" Thwart Cyber Attacks on Implanted Medical Devices?, IEEE Spectrum, 28 Mar 2019,
https://spectrum.ieee.org/the-human-os/biomedical/devices/thwart-cyber-attacks-on-implanted-medical-devices.amp.html.
▪ The vulnerabilities affect
implantable cardiac
devices and the external
equipment used to
communicate with them.
▪ The devices emit RF
signals that can be
detected up to several
meters from the body.
▪ A malicious individual
nearby could conceivably
hack into the signal to jam
it, alter it, or snoop on it.
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IoMT Security Measures is Hard -
Energy Constrained
21 May 2020 50Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
➢ Implantable Medical Devices (IMDs) have integrated battery to
provide energy to all their functions → Limited Battery Life
depending on functions
➢Higher battery/energy usage → Lower IMD lifetime
➢Battery/IMD replacement → Needs surgical risky proceduresSource: Carmen Camara, PedroPeris-Lopeza, and Juan E.Tapiadora, “Security and privacy issues in implantable medical
devices: A comprehensive survey", Elsevier Journal of Biomedical Informatics, Volume 55, June 2015, Pages 272-289.
Pacemaker
Battery Life
- 10 years
Neurostimulator
Battery Life
- 8 years
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IoMT Security Measures is Hard
21 May 2020 51Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Radio
Attacks
Reverse
Engineering
Attacks
Eavesdropping
Attacks
Impersonation
Attacks
Insulin Pump
Implantable and
Wearable Medical
Devices (IWMDs) --
Battery Characteristics:
→ Longer life
→ Safer
→ Smaller size
→ Smaller weight
Collectively
(WMD+IMD):
Implantable and
Wearable Medical
Devices (IWMDs)
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Our Secure by Design Approach for
Robust Security in Healthcare CPS
21 May 2020 52Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Threat Model
Physical Unclonable Function
(PUF) based Solution
Source: V. P. Yanambaka, S. P. Mohanty, E. Kougianos, and D. Puthal, “PMsec: Physical Unclonable Function-Based Robust
and Lightweight Authentication in the Internet of Medical Things”, IEEE Transactions on Consumer Electronics (TCE), Volume
65, Issue 3, August 2019, pp. 388--397.
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Our Secure by Design Approach for
Robust Security in Healthcare CPS
21 May 2020 53Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
IMD
WMD
Source: V. P. Yanambaka, S. P. Mohanty, E. Kougianos, and D. Puthal, “PMsec: Physical Unclonable Function-Based
Robust and Lightweight Authentication in the Internet of Medical Things”, IEEE Transactions on Consumer Electronics
(TCE), Volume 65, Issue 3, August 2019, pp. 388--397.
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IoMT Security – Our Proposed PMsec
21 May 2020 54Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Enrollment Phase
Source: V. P. Yanambaka, S. P. Mohanty, E. Kougianos, and D. Puthal,
“PMsec: Physical Unclonable Function-Based Robust and Lightweight
Authentication in the Internet of Medical Things”, IEEE Transactions on
Consumer Electronics (TCE), Volume 65, Issue 3, August 2019, pp. 388--397.
Device Registration Procedure
PUF Security Full Proof:
➢ Only server PUF Challenges
are stored, not Responses
➢ Impossible to generate
Responses without PUF
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IoMT Security – Our Proposed PMsec
21 May 2020 55Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Authentication Phase
Device Authentication Procedure
Source: V. P. Yanambaka, S. P. Mohanty, E. Kougianos, and D.
Puthal, “PMsec: Physical Unclonable Function-Based Robust and
Lightweight Authentication in the Internet of Medical Things”,
IEEE Transactions on Consumer Electronics (TCE), Vol. 65, No
3, Aug 2019, pp. 388--397.
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IoMT Security – Our PMsec in Action
21 May 2020 56Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: V. P. Yanambaka, S. P. Mohanty, E. Kougianos, and D. Puthal, “PMsec: Physical Unclonable Function-Based Robust and Lightweight
Authentication in the Internet of Medical Things”, IEEE Transactions on Consumer Electronics (TCE), Volume 65, Issue 3, August 2019, pp. 388--397.
Output from Server
during Enrollment
Output from IoMT Device
Output from Server during Authentication
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IoMT Security – Our Proposed PMsec
21 May 2020 57Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Proposed Approach Characteristics Value (in a FPGA / Raspberry Pi Platform)
Time to Generate the Key at Server 800 ms
Time to Generate the Key at IoMT Device 800 ms
Time to Authenticate the Device 1.2 sec - 1.5 sec
Average Power
Overhead –
~ 200 mW
Source: V. P. Yanambaka, S. P. Mohanty, E. Kougianos, and D. Puthal, “PMsec: Physical Unclonable Function-Based
Robust and Lightweight Authentication in the Internet of Medical Things”, IEEE Transactions on Consumer Electronics
(TCE), Volume 65, Issue 3, August 2019, pp. 388--397.
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Blockchain for Smart Healthcare?
21 May 2020 60Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
➢Energy for mining of 1 bitcoin → 2 years
consumption of a US household.
➢Energy consumption for each bitcoin
transaction → 80,000X of energy
consumption of a credit card processing.
Source: D. Puthal, N. Malik, S. P. Mohanty, E. Kougianos, and G. Das, “Everything you Wanted to Know about the
Blockchain”, IEEE Consumer Electronics Magazine (CEM), Volume 7, Issue 4, July 2018, pp. 06--14.
July 2018
Blockchain Challenges
Lack of Scalability
High Energy Consumption
High LatencyLack of Privacy
Fake Block Generation
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Blockchain Challenges - Energy
21 May 2020 61Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: D. Puthal, N. Malik, S. P. Mohanty, E. Kougianos, and G. Das, “Everything you Wanted to Know about the
Blockchain”, IEEE Consumer Electronics Magazine (CEM), Volume 7, Issue 4, July 2018, pp. 06--14.
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Hash PoAh
T1T
2T3
HashPoAh
T1 T2T
3
Hash PoAh
T1 T2 T3
Hash PoAh
T1 T2 T3
Hash PoAh
T1 T2 T3
Hash PoAh
T1 T2 T3
Hash PoAh
T1 T2 T3
Hash PoAh
T1 T2 T3
Hash PoAh
T1 T2 T3
Hash PoAh
T1 T2 T3
Prev-Hash
Trx-1 Trx-pTrx-2 …
PoAhPrev-Hash
Trx-1 Trx-pTrx-2 …
PoAh
Fog
Edge
End Devices
Cloud
IoT
Blockchain Blockchain
Edge
Devices
Our PoAh-Chain: The IoT Friendly
Private Blockchain for Authentication
21 May 2020Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
64
Source: D. Puthal and S. P. Mohanty, "Proof of Authentication: IoT-Friendly Blockchains", IEEE Potentials Magazine,
Volume 38, Issue 1, January 2019, pp. 26--29.
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Blockchain Consensus Algorithm
Validation Based
Proof of Work (PoW)
Proof of Stack (PoS)
Proof of Activity (PoA)
Proof of Relevance (PoR)
Proof of Elapsed Time
Voting Based
Ripple
Proof of Vote
Proof of Trust
Authentication Based
Proof of Authentication (PoAh)
Proof of PUF-Enabled Authentication (PoP) (Current Paper)
Blockchain Consensus Types
21 May 2020 65Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
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Our Proof-of-Authentication (PoAh)
21 May 2020 66Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Create Block Solve Puzzle Broadcast the
Proof-of-Work
(PoW)Process
Starts Again
Bi
BiBi-1Bi-2
Proof-of-Work (PoW)
Nodes form Block
of Transactions
Add the
Device-ID
Transmit to
Trusted Nodes
Yes
No
BiBi-1Bi-2
Bi
Device
Authenticated
?
Proof of Authentication (PoAh) Trusted Nodes
Network
Eliminates cryptographic
“puzzle” solving to
validate blocks.
Uses a cryptographic
authentication mechanism.
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Our PoAh-Chain: Proposed New
Block Structure
21 May 2020 67Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Hash of the
following:
• Hash of Bi-2
• Nonce of Bi-1
• Transactions
of Bi-1
Number only
used once
(Nonce)
Transactions
Tx1, Tx2, …, TxN
Block in
Conventional
Blockchain (Bi)
Hash of
Previous
Block
Conventional Block Structure
Hash of the
following:
• Hash of Bi-2
• PoAh of Bi-1
• Device ID
• Transactions
of Bi-1
Hash of
Previous
Block
Unique Block
Token (UBT)
Transactions
Tx1, Tx2, …, TxN
Block in
PoAh (Bi)
PoAh Block Structure
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Our PoAh is 200X Faster than PoW
21 May 2020 68Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: D. Puthal, S. P. Mohanty, P. Nanda, E. Kougianos, and G. Das, "Proof-of-Authentication for Scalable Blockchain in
Resource-Constrained Distributed Systems", in Proc. 37th IEEE International Conference on Consumer Electronics (ICCE), 2019.
Proof-of-
Work (PoW)
Proof-of-
Stake (PoS)
Proof-of-
Activity (PoA)
Proof-of-
Authentication(PoAh)
Energy consumption High High High Low
Computation requirements
High High High Low
Latency High High High Low
Search space High Low NA NA
PoW - 10 min in cloud PoAh - 3 sec in Rasperry Pi PoAh - 200X faster than PoW
Eliminates cryptographic
“puzzle” solving to
validate blocks.
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Machine Learning Challenges
21 May 2020 70Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Machine Learning Issues
High Energy Requirements
High Computational Resource Requirements
Large Amount of Data Requirements
Underfitting/Overfitting Issue
Class Imbalance Issue
Fake Data Issue
Source: Mohanty ISCT Keynote 2019
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Deep Neural Network (DNN) -
Resource and Energy Costs
21 May 2020 71Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: https://www.mathworks.com/campaigns/offers/mastering-machine-learning-with-matlab.html
OPTIMIZE
PARAMETERSTRAIN MODEL
A C C E S S A N DPREPROCESS DATA
EXTRACT
FEATURESMODEL
A B C
CAPTURESENSOR DATA
EXTRACT FEATURES
RUN MODEL
A B C
PREDICTION
TRAIN: Iterate until you achieve satisfactory performance.
PREDICT: Integrate trained models into applications.
Needs Significant:
➢Resource
➢Energy
Needs: ➢ Resource➢ Energy
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DNN Training - Energy Issue
21 May 2020 72Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Images
Labels
Car
Source: Mohanty iSES 2018 Keynote
➢ DNN considers many training parameters, such as the
size, the learning rate, and initial weights.
➢ High computational resource and time: For sweeping
through the parameter space for optimal parameters.
➢ DNN needs: Multicore processors and batch processing.
➢ DNN training happens mostly in cloud not at edge or fog.
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CE/IoT System - Multi-Objective Tradeoffs
21 May 2020 75Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Recurr
ing O
pera
tional C
ost
Source: Mohanty ICCE 2019 Keynote
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Smart Healthcare – Edge Vs Cloud
21 May 2020 76Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Gateway
Local
Area
Network
(LAN)
Internet
Cloud
Services
A Thing
Sensors
(Things) Cluster
End/Sensing Devices
Edge Data Center
Edge Router
Edge / Fog PlaneMiddleware
(Communication)
Upload
Download
Upload
Emotions
Heart Rate
Blood Pressure
Source: Our IFIP IoT 2019 Talk (Good-Eye: A Combined Computer-Vision and Physiological-Sensor based Edge Device
for Full-Proof Prediction and Detection of Fall of Adults)
Edge Security/Intelligence
➢Less Data
➢Less Computational Resource
➢Less Accurate Data Analytics
➢Rapid Response
➢Minimal Data
➢Minimal Computational
Resource
➢ Least Accurate Data Analytics
➢ Very Rapid Response
End Security/Intelligence
Cloud
Security/Intelligence
➢Big Data
➢Lots of
Computational
Resource
➢Accurate Data
Analytics
➢Latency in Network
➢Energy overhead in
Communications
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Smart Healthcare –
Specific Examples
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Stress is a Major Health Issue
21 May 2020 80Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
❑Stress is the relationship between a person and a situation, which adversely
impacts the happiness and health of the sufferer or physiological reactions.
❑Stress can be divided into two parts: stressor and reaction.
❑Stressor is the activity or effect that triggers a change in the physiological
parameter values of the human body.
❑Reaction is the deviation of these parameter values from their normal levels.
Good Stress
Bad Stress
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Body
Temperature
Sensor
Sweat Sensor
Physical Activity
Monitoring
Sensor
Stress-Level
Detection Unit
Wi-Fi
Module
Various
Data
Internet
CloudStress
Level
Stress
Value
Food Intake
Monitoring
Components
Stress
Management
Unit
Advise Examples: Specific Music, Shower, Physical Exercise, Breathing Exercise, Meditation, Yoga, …
Edge Deep
Learning
Based Stress
Models
Cloud Deep
Learning Based
Stress Models
Short-Term Advise
Long-
Term
Advise
Smart Healthcare - Stress Monitoring & Control
Source: L. Rachakonda, S. P. Mohanty, E. Kougianos, and P. Sundaravadivel, "Stress-Lysis: A DNN-Integrated Edge Device for
Stress Level Detection in the IoMT", IEEE Transactions on Consumer Electronics (TCE), Vol 65, No 4, Nov 2019, pp. 474--483.
Sensor Low Stress Normal Stress High Stress
Accelerometer
(steps/min)
0-75 75-100 101-200
Humidity (RH%) 27-65 66-91 91-120
Temperature ̊F 98-100 90-97 80-90
21 May 2020Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
81
Automated Stress
Level Detection
and Management
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Humidity Sensor
(For Sweat
Analysis)
Deep Learning
Based Stress
Detection
Stress-Level
Detection
Unit
Physical Activity
Monitoring
Sensor
Temperature
Sensor
Wi-Fi
Module
Sens
or
Data Internet
Cloud
Stress-Lysis Thing
Neutral
Stress
Level
Stress
Value
Stress-Lysis: From Physiological Signals
Source: L. Rachakonda, S. P. Mohanty, E. Kougianos,
and P. Sundaravadivel, "Stress-Lysis: A DNN-
Integrated Edge Device for Stress Level Detection in the
IoMT", IEEE Transactions on Consumer Electronics
(TCE), Vol 65, No 4, Nov 2019, pp. 474--483.
21 May 2020Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
83
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Stress-Lysis: Experiments
21 May 2020 84Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: L. Rachakonda, S. P. Mohanty, E. Kougianos, and P. Sundaravadivel, "Stress-Lysis: A DNN-Integrated Edge Device for
Stress Level Detection in the IoMT", IEEE Transactions on Consumer Electronics (TCE), Vol 65, No 4, Nov 2019, pp. 474--483.
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Smart Healthcare – Smart-Pillow
21 May 2020 86Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: Mohanty iSES 2018: “Smart-Pillow: An IoT based Device for Stress Detection Considering
Sleeping Habits”, in Proc. of 4th IEEE International Symposium on Smart Electronic Systems (iSES) 2018.
Heart Rate
Snoring
Respiration Rate
Automatically monitors
stress levels during the day
and relates to sleeping
behaviors at night.
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Automatic Food Intake Monitoring
and Diet Management is Important
21 May 2020 88Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
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Smart Healthcare – Diet Monitoring
21 May 2020 89Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: L. Rachakonda, S. P. Mohanty, and E. Kougianos, “iLog: An Intelligent Device for Automatic Food Intake Monitoring
and Stress Detection in the IoMT”, IEEE Transactions on Consumer Electronics (TCE), Vol. 66, No. 2, May 2020, pp. 115--124.
Captures
Food
Images
IoT Cloud
Automatically monitors
food intake to determine if
the eating is stress-eating
or normal-eating.
Automatic Food
Image Analysis
Accuracy of detecting food - 97%
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Smart Healthcare – iLog
21 May 2020 90Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: L. Rachakonda, S. P. Mohanty, and E. Kougianos, “iLog: An Intelligent Device for Automatic Food Intake Monitoring
and Stress Detection in the IoMT”, IEEE Transactions on Consumer Electronics (TCE), Vol. 66, No. 2, May 2020, pp. 115--124.
Reference Image
Edge Platform
Classification
Feature
Extraction
Object
Detection
Nutrition Dataset
Stress-Eating
Analysis
Mobile
Application
Interface
Cloud
Platform
Database
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Smart Healthcare – iLog
21 May 2020 91Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
The data collected is sent to the Firebase Database in which
the calorie count is generated by using a dataset with
calories and sugars count of individual items from data.gov.
Source: L. Rachakonda, S. P. Mohanty, and E. Kougianos, “iLog: An Intelligent Device for Automatic Food Intake Monitoring
and Stress Detection in the IoMT”, IEEE Transactions on Consumer Electronics (TCE), Vol. 66, No. 2, May 2020, pp. 115--124.
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Seizure
Detection
Drug
Delivery
Unit Cloud Storage
Hospital
Doctor
Transmission and Storage Access Unit
Wireless
Transfer
Yes Dosage
Information
Seizure State
Smart Healthcare - Seizure Detection & Control
A
BC
A – Typical Latency - 6 sec
B – Early Detection - 1 to 2 sec
C – Seizure Predication -
at least 6 sec before
Seizure
Detection
Seizure
Onset
21 May 2020Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
94
Sensor Unit
EEG Data
Acquisition
EEG Signal
Drug
Injection
Source: M. A. Sayeed, S. P. Mohanty, E. Kougianos, and H. Zaveri, “eSeiz: An Edge-Device for Accurate Seizure Detection
for Smart Healthcare”, IEEE Transactions on Consumer Electronics (TCE), Volume 65, Issue 3, August 2019, pp. 379--387.
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21 May 2020 95Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Cloud Vs Edge Latency Accuracy
Cloud-IoT based Detection
2.5 sec 98.65%
Edge-IoT based Detection
1.4 sec 98.65%
Source: M. A. Sayeed, S. P. Mohanty, E. Kougianos, and H. Zaveri, “Neuro-Detect: A Machine Learning Based Fast and Accurate
Seizure Detection System in the IoMT”, IEEE Transactions on Consumer Electronics (TCE), Vol 65, No 3, Aug 2019, pp. 359--368.
Seizure Detection Approaches
Cloud Vs Edge Computing
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Smart Healthcare – Brain as a
Spatial Map → Kriging Methods
21 May 2020 96Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: I. L. Olokodana, S. P. Mohanty, and E. Kougianos, "Ordinary-Kriging Based Real-Time Seizure Detection in an Edge
Computing Paradigm", in Proceedings of the 38th IEEE International Conference on Consumer Electronics (ICCE), 2020, Accepted.
Source: http://desktop.arcgis.com/en/arcmap/10.3/tools/3d-
analyst-toolbox/how-kriging-works.htm
Spatial autocorrelation principle
- things that are closer are more
alike than things farther
Spatial modeling or Variography
- Correlation Function is
“Variogram”
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Kriging based Seizure Detection
21 May 2020 97Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Source: I. L. Olokodana, S. P. Mohanty, and E. Kougianos, "Ordinary-Kriging Based Real-Time Seizure Detection in an Edge
Computing Paradigm", in Proceedings of the 38th IEEE International Conference on Consumer Electronics (ICCE), 2020, Accepted.
Signal
Denoising
Wavelet
Transformations
Feature
Extraction
Fractal
Dimension
Seizure State
Classification
Kriging
Classifier
Seizure
Status
Works Extracted Features Classification Algorithm
Sensitivity
Latency
Zandi, et al. 2012 [23]
Regularity, energy & combined seizure indices
Cumulative Sum thresholding
91.00% 9 sec.
Altaf,etal. 2015 [24] Digital hysteresis Support Vector Machine 95.70% 1 sec
Vidyaratne, et al. 2017 [25]
Fractal dimension, spatial/ temporal features
Relevance Vector Machine (RVM)
96.00% 1.89 sec
Our Proposed Petrosian fractal dimension Kriging Classifier 100.0% 0.85 s
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Seizure Control Methods
21 May 2020 98Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Piezoelectric: Latency – 1.8 Sec,
Power – 29 mW, Flow Rate – 3 mL/min
Source: M. A. Sayeed, S. P. Mohanty, E. Kougianos, and H. Zaveri, “iDDS: An Edge-Device in IoMT for Automatic Seizure Control
using On-Time Drug Delivery", in Proceedings of the 38th IEEE International Conference on Consumer Electronics (ICCE), 2020.
Electromagnetic: Latency –
1.8 Sec, Power – 12.81 mW,
Flow Rate – 0.34 mL/min
Source: M. A. Sayeed, S. P. Mohanty, E. Kougianos,
and H. Zaveri, "An IoT-based Drug Delivery System
for Refractory Epilepsy", in Proceedings of the 37th
IEEE International Conference on Consumer
Electronics (ICCE), 2019.
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Diabetes is a Global Crisis
21 May 2020 99Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Estimations
Source: https://diabetesatlas.org/en/sections/worldwide-toll-of-diabetes.html
Estimated Number of Adults
with Diabetes (in Millions)
Projections
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Blood Glucose Monitoring –Invasive Vs Noninvasive
21 May 2020 100Healthcare CPS -- Prof./Dr. Saraju P. Mohanty
Invasive Approach –
Processing Blood/Serum
Traditional –
Finger Pricking
Noninvasive Approach
– Processing Light
Noninvasive – Wearable
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Noninvasive Glucose-Level Monitoring
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Photoplethysmogram (PPG) Near Infrared (NIR)
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Our Vision – iGLU (Intelligent
Noninvasive Monitoring and Control)
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Insulin
SecretionDisplay
of
Parameters
Continuous
Glucose
Monitoring
Artificial Pancreases
System (APS)
Cloud Storage
Hospital
Doctor
Privacy-Assured
Health Data Storage
Security-
Assured System
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iGLU 1.0: Capillary Glucose
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Source: P. Jain, A. M. Joshi, and S. P. Mohanty, “iGLU: An Intelligent Device for Accurate Non-Invasive Blood Glucose-Level
Monitoring in Smart Healthcare”, IEEE Consumer Electronics Magazine (MCE), Vol. 9, No. 1, January 2020, pp. 35-42.
Analog-to-Digital
Converter
(ADS1115)
Near Infrared
(NIR) Emitters
(940nm,
1300nm)
Transmitted Wave
Attenuated
Wave
Vibrations
(Stretching,
Wagging, Bending)
Infrared Detector
Infrared
Detector
Cost - US$ 20
Accuracy - 100%Clinically tested in an hospital.
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iGLU 2.0: Serum Glucose
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Source: P. Jain, A. M. Joshi, N. Agrawal, and S. P. Mohanty, “iGLU 2.0: A New Non-invasive, Accurate Serum Glucometer
for Smart Healthcare”, arXiv Electrical Engineering and Systems Science, arXiv:2001.09182, January 2020, 19-pages.
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Conclusions and
Future Research
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Conclusions
◼ Healthcare has been evolving to Healthcare-
Cyber-Physical-System (CPS) i.e. smart
healthcare.
◼ Internet of Medical Things (IoMT) plays a key
role smart healthcare.
◼ Smart healthcare can reduce cost of
healthcare and give more personalized
experience to the individual.
◼ IoMT provides advantages but also has
limitations in terms of security, privacy, etc.
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Future Research
◼ Internet-of-Everything (IoE) with Human as
active part as crowdsourcing need research.
◼ IoE will need robust data, device, and CPS
security need more research.
◼ Security of IWMDs needs to have extremely
minimal energy overhead to be useful and
hence needs research.
◼ Integration of blockchain for smart healthcare
need research due to energy and
computational overheads associated with it.
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Acknowledgement(s)
This material is based upon work supported by
the National Science Foundation under Grant
Nos. OAC-1924112. Any opinions, findings,
and conclusions or recommendations
expressed in this material are those of the
author(s) and do not necessarily reflect the
views of the National Science Foundation.
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