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Multi-Access Edge Computing and Blockchain-based Secure Telehealth System Connected with 5G and IoT


Abstract and Figures

There is a global hype in the development of digital healthcare infrastructure to cater the massive elderly population and infectious diseases. The digital facilitation is expected to ensure the patient privacy, scalability, and data integrity on the sensitive life critical healthcare data, while aligning to the global healthcare data protection standards. The patient data sharing to third parties such as research institutions and universities is also concerned as a significant contribution to the society to sharpen the research and investigations. The emergence of 5G communication technologies eradicates the borders between patients, hospital and other institutions with high end service standards. In patients' perspective, healthcare service delivery through the digital medium is beneficial in terms of time, costs, and risks. In this paper, we propose a novel Multi-access Edge Computing(MEC) and blockchain based service architecture utilizing the lightweight ECQV (Elliptic Curve Qu-Vanstone) certificates for the realtime data privacy, integrity, and authentication between IoT, MEC, and cloud. We further attached storage offloading capability to the blockchain to ensure scalability with a massive number of connected medical devices to the cloud. We introduced a rewarding scheme to the patients and hospitals through the blockchain to encourage data sharing. The access control is handled through the smart contracts. We evaluated the proposed system in a near realistic implementation using Hyperledger Fabric blockchain platform with Raspberry Pi devices to simulate the activity of the medical sensors.
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Multi-Access Edge Computing and Blockchain-based
Secure Telehealth System Connected with 5G and
Tharaka Hewa, An Braeken, Mika Ylianttila, Madhusanka Liyanage§
∗‡ §Centre for Wireless Communications, University of Oulu, Finland, Vrije Universiteit Brussel, Brussels, Belgium
§School of Computer Science, University College Dublin, Ireland
Email: ∗‡ §[firstname.lastname], §
Abstract—There is a global hype in the development of digital
healthcare infrastructure to cater the massive elderly population
and infectious diseases. The digital facilitation is expected to ensure
the patient privacy, scalability, and data integrity on the sensitive
life critical healthcare data, while aligning to the global healthcare
data protection standards. The patient data sharing to third parties
such as research institutions and universities is also concerned as a
significant contribution to the society to sharpen the research and
investigations. The emergence of 5G communication technologies
eradicates the borders between patients, hospital and other insti-
tutions with high end service standards. In patients’ perspective,
healthcare service delivery through the digital medium is beneficial
in terms of time, costs, and risks. In this paper, we propose a
novel Multi-access Edge Computing(MEC) and blockchain based
service architecture utilizing the lightweight ECQV (Elliptic Curve
Qu-Vanstone) certificates for the realtime data privacy, integrity,
and authentication between IoT, MEC, and cloud. We further
attached storage offloading capability to the blockchain to ensure
scalability with a massive number of connected medical devices
to the cloud. We introduced a rewarding scheme to the patients
and hospitals through the blockchain to encourage data sharing.
The access control is handled through the smart contracts. We
evaluated the proposed system in a near realistic implementation
using Hyperledger Fabric blockchain platform with Raspberry Pi
devices to simulate the activity of the medical sensors.
Index Terms—Elliptic Curve Cryptography, Elliptic Curve Qu
Vanstone Certificates, Blockchain, Smart Contracts, 5G, IoT
The classical healthcare systems require patients to admit the
hospitals and connect to the biomedical equipment including
oxygen sensors, blood pressure meters, glucose sensors and
so on. However, the classical procedures are challenging to
cope with extensive demands of healthcare, which is a vital
consideration due to the increase of elderly population as well
as an events such as global pandemics. Presently, the global
enthusiasm towards the Telehealth techniques is accelerated with
the COVID-19 pandemic. Furthermore, the pandemic situation
restricts the utilization of healthcare resources to minimize the
risk of spreading diseases. A certain amount of the patients, who
do not require a robust medical intervention can be connected
to the hospitals for monitoring and remote treatments. Elderly
people and post-treatment monitoring are potential patient cat-
egories. It reduces risk, improves the patient’s comfort and
satisfaction with eventually cutting the costs.
Telehealth systems [1] include among others the application
of robotics to deliver medicines, monitoring of patients and
offering remote consultation of the patients. It can seamlessly
connect patients with medical professionals including nurses,
general practitioners, and consultants through the Internet while
are patients in their homes.
The 5G and MEC are identified as prominent technologies to
leverage future telehealth systems by satisfying above require-
ments. The 5G network ecosystem provides seamless connectiv-
ity between medical sensors, actuators and the cloud with ultra
high speed and extensive bandwidth support. Furthermore, the
high reliable high quality video streaming for patient screening
and Augmented Reality (AR) assisted consultation are can be
provided by utilizing 5G MEC technologies [2]. Connectivity
with the MEC elevates the service capabilities of connected
IoT (Internet of Things) nodes with offloading some resource
intensive computations to MEC nodes.
To realize this setup, we propose a novel MEC and blockchain
based secure telehealth system using lightweight Elliptic Curve
Qu Vanstone (ECQV) certificates [3] and symmetric keys which
connects IoT nodes with the cloud using MEC nodes. The
solution utilizes blockchain-based smart contracts in the key
establishment, data access and data sharing processes. The
blockchain storage is extended with the IPFS (InterPlanetary
File System) to reduce blockchain storage growth to support
the massive number of connective nodes. The access control to
the patients’ data and the rewarding scheme for the hospitals
and patients for data sharing are handled through the smart
contracts. We evaluated the proposed system with a prototype
implementation by using Hyperledger blockchain platform.
The rest of the paper is organized as follows. Section II
presents a background on Telehealth systems and 5G. Section
III presents the proposed architecture. Security of the proposed
system is analysed in Section IV. Section V presents the
prototype implementation and experimental results. Finally, the
Section VI concludes the paper.
A. Telehealth systems
The telehealth is defined as a technique which delivers med-
ical services, such as clinical consultation, patient monitoring,
and remote treatment over the digital infrastructure. The key
distinguishing parties in classical telehealth systems are the
patients, interfaced with the digital medium, and the hospitals
which operate the system and data storage, typically hosted in
cloud computing infrastructure. The telecommunication services
are provided by a third party and the data is stored in the cloud.
Hence, there are many security considerations for the data in
transit and the data stored in the cloud in terms of privacy,
integrity and compliance with standards.
1) Benefits of telehealth systems: The telehealth systems
enable seamless connectivity with the patients and consultants
beyond frontiers with various types of healthcare service de-
livery. Through the realtime connected telehealth systems, the
healthcare response is achievable with minimal time and the
healthcare professionals are not unnecessarily exposed to the
2) Key requirements of the telehealth systems:
a) Data security: The data security is a vital concern in
telehealth systems and the data security mechanisms must be
aligned with ultra low latency requirements and computational
resource restrictions associated with the lightweight computing
nodes operating in the telehealth domain along with the compli-
ance standards such as Health Insurance Portability and Privacy
Act (HIPAA).
b) Authentication and access control: Authentication and
access control in telehealth systems should be applicable to
the data and services. The data authentication ensures the
data in transit is not being tampered and the service access
control ensures that the services consumed by the patients are
aligned with their subscription plans. However, the importance
of authentication is vital since the data exchanged is life critical
and the tampering of data could lead the human life at risk.
c) High-end connectivity: The primary anticipations of the
connectivity include ultra-low latency with higher bandwidth.
Furthermore, the limited and customized operational require-
ments also exist.
d) Data sharing capability: The data sharing capability
is a vital requirement and the privacy must be balanced in-
between the applications since the privacy violations will deviate
the systems with compliance standards. The shared data will be
used in improving accuracy of the future research conducted in
disease control.
B. 5G for Telehealth
The 5G communication technologies facilitate to leverage the
healthcare context by the distinguishing advancements compared
to the previous communication infrastructure. The high through-
put, extremely low latency and a vast array of customized
techniques including micro operators have potential to expand
the usability towards diverse use cases.
The IoT initiatives in the healthcare are widespread in differ-
ent avenues including treatments in the infectious diseases and
adult care, monitoring and remote treatments with 5G connected
sensors and actuators.
The MEC based architecture is fostered in different applica-
tion contexts since the edge computing infrastructure shrinks the
gap of cloud-quality computation in contrast with the cloud.
C. Related works
Most of the communications in the telehealth context are
performed in a wireless medium, open for a wide range of
attackers, the inclusion of sufficient security mechanisms should
be guaranteed [4]. In particular, authentication of legitimate IoT
devices is very important in the telehealth applications. The
advantages of including blockchain in healthcare are discussed
in [5]. Xiong et al. [6] highlighted the significance of integrating
edge computing nodes for offloading computational resource
intensive tasks in the blockchain networks.
Theodouli et al. [7] presented a blockchain based architecture
for healthcare data sharing and access permission handling
utilizing the smart contracts. Chen et. al [8] presented edge and
cognitive computing based healthcare system which monitors
and analyzes the physical health of smart clothing users. Pace
et al. [9] proposed BodyEdge, which is an edge based novel
architecture for human-centric applications in the healthcare
industry 4.0. Wang and Zhang [10] proposed homomorphic
encryption based data division scheme on the data generated
by wireless sensor nodes.
We propose an IoT-MEC-Cloud based architecture as illus-
trated in Figure 1, linked to the blockchain and Inter Planetary
File System (IPFS) to achieve end to end security, scalable data
storage, high throughput, and efficient operational capability in
the resource restricted computational infrastructure. We utilize
the lightweight ECQV certificate based mechanisms to ensure
the lightweight cryptographic overheads in the operations.
In the proposed system, we envision seven parties: patient,
hospital, device, MEC node, blockchain service layer (BSL),
cloud server and trusted third party (TTP). The BSL can be
seen as a trusted security utility application, which separates
some services from the off-chain invocation. Note that we will
further assume that the hospital is responsible for the correct
access control to the doctor(s) related to it.
The following nine main phases in the system are distin-
guished and are illustrated on Figure 2.
1) System initialization: Without loss of generality, we
can assume the existence of one TTP, who decides on
the EC, generator G, hash function H(), symmetric key
encryption function EK(.)with symmetric key K, and
chooses a random value dT T P as private key. The param-
eters EC, G, QT T P , H (.), EK(.)with public key of TTP,
QT T P =dTT P G, are publicly available.
Fig. 1: The System Architecture
Storage offloading
Secured data sharing
Data computation
Privacy enforcement
IoT nodes Inter Planetary File System Blockchain Service
Cloud Service
MEC Nodes
Storage of Session Objects Secure Ledger Smart Contracts
Regulated access control
Transparency and data provenence
Security policy validation
Access control
Handling rewards to patients/ hospitals
Certificate management
Signature verification
Connectivity Security
Data aggregation
Secure Multi-party Computation
Basic analytics
Signature verification
Node authentication
Threat revocation with fog deployed IDS
Medical Staff/
Third parties
Registration of patient/hospital on blockchain
Registration of devices in blockchain
(IDp, Qp) : Patient's key pair
(IDh, Qh) : Hospital's key pair
(IDd, Qd) : Device's key pair
End entities
Qp/Qd /
Registration smart contract
Blockchain service
Key Establishment and
Operation Registration
Request from device
Access control to data
Defines EC, G and holds QTTP
Trusted Third Party
Verification smart contract
Verify device/patient/hospital identity
on the BC
Receive response and send back to
IoT node
Joint public key retrieved {IDm, Certm, Rm, H(Kdm
Response from BC to compute
device public key
Secret session key Kdm
Received from blockchain
Data encrypted from K
Send aggregrated analysis to the
blockchain, periodically
Diffie Hellman keys generated for
each hospital
Signed messages sent to BSL
Messages published in
the global database for
Compute K=dhRa
Doctor retrieves information
Smart contract invocation from cloud
Access count recorded for
Patient revokes access
Update blockchain service
Data migration and
symmetric key generation
[8] Data access to the third
Fig. 2: The message flow
The MEC nodes with identity IDmobtain a private and
public key pair (dm, Qm)through ECQV from the TTP
with Qm=H(IDm, Qm) + QT T P .
2) Registration phase of patient, hospital and de-
vice: In this phase, the patient and hospital first ob-
tain a private-public key pair (IDp, Qp)and (IDh, Qh)
through ECQV via the TTP. The hospital publishes
on the BC its identity, public key, time of registra-
tion, and certificate (IDh, Qh, Th, Certh)with Qh=
H(IDh, Th, Certh)Certh+QTTP invoking the smart con-
tract via blockchain API.
The patient publishes on the BC its identity, identity of the
hospital to who (s)he trusts his/her data, public key, time of
registration, and certificate IDp, I Dh, Qp, Td, Certpwith
Qp=H(IDp, IDh, Tp, C ertp)Certp+QT T P .
Next, a joint public key pair (dj, Qj)for patient and
hospital is constructed. For that, using the public key based
mechanisms to establish a secret channel, the random num-
bers r1, r2, selected by patient and hospital respectively,
are securely shared among them in order to derive the new
private key djand corresponding public key Qj=djG.
This joint public key is added to the BC related information
of the patient.
Also the devices going to monitor the patient will
be registered by the hospital on the BC at time
Td. Therefore, the device receives a private-public key
pair (IDd, Qd)with certificate Certdwhere Qd=
H(IDd, IDp, I Dh, Qj, Td, Certd)C ertd+QT T P through
ECQV via the TTP. On the BC, the information
IDd, IDp, Qd, Td, C ertd, Qjis published.
3) Key establishment between IoT and MEC node: The
IoT device sends a request message containing the signed
message {IDd, Rd}ddof its identity IDdand random value
Rd=rdG. Upon arrival of this message, the MEC node
verifies the existence of IDdon the BC, and looks up its
corresponding public key Qdand identifiers IDp, I Dh, Qj.
It verifies also on the BC if the patient has IDhas its
preferred hospital and corresponding joint public key Qj.
If then also the signature is correct, it verifies in its local
database if an analysis of IDpis already ongoing. If so, it
adds the current request to this analysis with identifier IDa.
If not, it creates a new analysis identifier IDa. Next, the
response containing (IDm, Certm, Rm, H(Kdm )) with
Kdm = (rm+dmH(Rd, Rm))(Rd+H(Rm, Rd)Qd)is
sent to the IoT node and IDd, Kdm, Qjis securely stored
in the database, containing the information related to IDa.
Based on the received response, the IoT device can first
compute with ECQV the public key Qmand verify the
correctness of the hash by also computing the secret session
key Kdm = (rd+ddH(Rm, Rd))(Rm+H(Rd, Rm)Qm).
If so, both are mutually authenticated and share the same
session key Kdm, which is due to construction, secure for
perfect forward secrecy and session information leakage
4) IoT-Cloud data sharing operation: Using the session key
Kdm, all data Mfrom the IoT node can now be securely
sent to the MEC node as (IDd, EKdm (M)).
The MEC node first checks the existence of IDdin its
database to find the session key and the corresponding
analysis IDato which it belongs. Based on that info, it can
decrypt the message and use it for the complete analysis
profile of the patient.
5) Active patient cloud update: After a fixed period, the
different aggregated analyses of all IDaare sent to the
BSL for publication on the BC. For each analysis, it
computes a random point Ra=raGand Diffie Hell-
man key Ka=raQjwith the corresponding hospital
and patient (using the joint public key). This key is
used to encrypt the analysis data Ma, to obtain Ca=
EKa(Ma). Next, the message is signed by sa=ra
hadmwith ha=H(IDm, IDp, Ra, Ca). The message
IDm,(IDp, Ca, Ra, sa)ais sent to the BSL.
Upon arrival of all these messages, coming from dif-
ferent MEC nodes, the BSL combines the messages
of each MEC and performs an aggregated signature
verification by computing (Pasa)G=PaRa
(PaH(IDm, IDp, Ra, Ca))Qm. If it is correct, the au-
thentication of all messages related to the same MEC is
verified at once and each message (IDp, Ca, Ra, sa)is
stored in the global database, together with a link on the
BC by invoking the smart contracts.
6) Active patient information retrieval: The doctor and
patient are now able to retrieve at any moment the data re-
lated to the patient by taking the records (IDp, Ca, Ra, sa)
stored in the cloud server and computing K=djRato
obtain Ma=DKa(Ca). The access query is validated by
the smart contracts.
7) Data sharing activation: When the patient-hospital rela-
tionship has been detached, for instance, 3 months after
discharging, the patient data will be transformed to a
shareable state by revoking the previously joint key pair
within the system. Patient data will be decrypted and re-
encrypted with a new random generated session key. This
session key is shared with the patient by encrypting it with
the patient’s public key.
8) Data sharing with third party: When the third party needs
to get the patient’s data, the hospital will be contacted and
the hospital triggers the patient about the request. When the
patient accepts the sharing request, the patient shares the
session key and the data can be decrypted. The data will be
shared in plain form without revealing the patient’s identity
to the third party. The key recycling policy is established in
the smart contract, for instance by expiring the session key
after 1 month or recycle the key after single data sharing
operation. Each key expiry will require encryption of the
data again from the newly generated session key.
9) Access revocation: Suppose the patient wants to change its
preferred hospital, then it registers again with the TTP and
receives a new private-public key pair, containing the new
hospital identifier in its calculation of the public key. It need
to create a new joint key and also all devices used by the
patient need to renew their certificate. Via a smart contract,
the MEC node is made aware of an update of hospital and
all ongoing analyses with IDpinvolved, change the stored
joint public key to which the patient is linked. The smart
contract is invoked to flag the access revocation on data.
The required security features of the proposed system are
obtained using well established and secure building blocks like
ECQV, Shnorr signature and Diffie DH key, who rely on the
security of the ECDLP and ECDHP. A focus on the security
features is as follows.
1) Authentication: The authentication of IoT nodes, MEC
nodes and cloud is guaranteed by the usage of the Schnorr
signature in each message. Through the signature verification,
the exchanged messages are secured against impersonations and
man-in-the-middle attacks since forging the messages is difficult
according to hardness of the Elliptic Curve Diffie Hellman
problem (ECDLP).
2) Integrity: The integrity is obtained in the same way as
the authenticity since the signature is generated on the complete
content of the message by means of a hash operation satisfying
protection against collisions, pre-image and second pre-image
attacks. Consequently, signatures cannot be forged by any other
3) Confidentiality: Thanks to the key establishment between
IoT node and MEC node, the data sent from IoT to the MEC
is encrypted using the symmetric encryption algorithm such as
AES-256. The aggregated data is sent from MEC to cloud,
encrypted by means of Diffie Hellman key, constructed using
the joint public key of hospital and patient.
4) Anonymity in shared patient data: The patient data is
accessible to the third parties after expiring the continuation
of the patient with the hospital, for instance 3 months after dis-
charging from the treatments. However, the patient and hospital
will be assigned with a new joint key pair and the patients are
only mapped in the hospital for the older data to trigger access
requests and reward them for the data sharing. The patient’s data
is migrated to a shareable storage upon permission of the patient
through the hospital and no patient identity will be revealed.
5) Access control: The access control to the patient data is
enforced with smart contracts. The patient contains ownership
of the data and the access to the data is granted to the attached
hospital, using the joint key pair, by default in the treatment
process. The access of the hospital is revoked automatically
using the smart contracts after a predefined period of time
when the patient is detached from the treatment. The patient
needs to authorize data access after the data converted into a
shareable state. The data shared anonymously with the other
organizations and the patient and hospital will be rewarded
for the number of access attempts and volume of the shared
data. The evaluation of the reward is performed through the
smart contracts, which handle the access control operation. The
explicit access revocation operation is also handled through the
invocation of the smart contract.
Fig. 3: The implementation setup
A. Experimental setup
The computing infrastructure utilized for the implementation
consists of a few virtual Machines(VM) and one host machine.
The virtual machine operates Ubuntu 18.10 64 bit with 13.2GB
RAM and single core allocation. The host machine consists of
Intel(R) Core i5 -8250 CPU with four cores and eight logical
processors. Figure 3 provides an overview of the implementation
setup. The proposed architecture integrates the MEC computing
module as an intermediary to establish the connectivity between
IoT nodes and the cloud. The IoT-MEC connectivity is estab-
lished in the implementation with Message Queuing Teleme-
try Transport (MQTT) and Constrained Application Protocol
(CoAP) protocols, which are widely used application protocols
in the IoT context. We used Raspberry Pi as IoT nodes which
represent the medical sensors and actuators in the experimental
The experimental environment with Hyperledger Fabric
blockchain platform, is connected to the Inter Planetary File
System (IPFS) as the extension of storage. The Hyperledger
Fabric blockchain platform is connected to the MEC nodes and
cloud service via REST API connectivity to submit and retrieve
transactions. The IPFS is connected to the smart contracts via
REST API in order to store the objects in the distributed storage.
The smart contracts are deployed in the Hyperledger blockchain
to operate on different steps of intervention of the blockchain.
The blockchain service is connected to the BSL which is being
invoked by the smart contract via REST API to offload a few
cryptographic transactions.
B. Transaction generation
A near realistic transaction generation implemented by mak-
ing the transactions follow a Poisson arrival with defined mean
values corresponding to the transactions per second (tps). The
rate parameter λis defined for the generated tps.
The simulation of transaction traffic is performed by the
multi-threaded software codes. The rate parameter is config-
urable when the test is conducted. The transaction generation
follows a Poisson distribution with rate parameter λand the
probability of observing kevents in the time period is denoted
P(X=k) = eλλk
The average of the measuring parameter Rfor Nrequests
triggered on each λis denoted as
C. Experiment
5 10 50 75 100 125
Mean transactions per second( )
Average latency on IoT-Fog-Cloud hop (ms)
Average latency on CoAP IoT-Fog-Cloud connectivity
Average latency on MQTT IoT-Fog-Cloud connectivity
Fig. 4: The IoT registration delay over MQTT and CoAP
1) IoT Device/Hospital/Patient registration delay: The regis-
tration process corresponding to IoT device, hospital, and patient
registration processes are the same in terms of interaction.
Through the MEC based architecture we facilitated MQTT and
CoAP IoT protocols. We observed that, there is a performance
bottleneck which hinders the scalability due to the smart contract
invocation as illustrated in Figure 4. However, the blockchain
service scalability is feasible with different specialized ap-
proaches. The healthcare use cases usually do not expect to
perform a higher number of registrations in realtime.
10 50 100 250 500 750 1000
Mean transactions per second( )
Average latency in ms
Average latency on Cloud-MEC-IoT : Implementation
Average latency on MEC signature verification : Implementation
Fig. 5: The IoT to Cloud data upload via MQTT
2) IoT to Cloud Data Upload delay: We evaluated the IoT-
Cloud data upload latency over the MEC connectivity. Previ-
ously we observed that MQTT is better in terms of latency
and therefore we run this experiment through MQTT. We used
to publish the data from the IoT nodes into different topics
while the subscribing cloud listens to each and every topic with
individual thread assigned for processing. The results prove as
in Figure 5, that even in 1000 transaction per second, the latency
does not increase drastically indicating the performance optimal
design of the proposed architecture.
1000 5000 10000 15000 20000 25000 30000
Number of dynamic ID registration
Ledger storage in MB
Memory utilization of blockchain for dynamic identity registration
Fig. 6: The blockchain storage utilization for registrations
3) Scalability Analysis Storage utilization in Blockchain :
We offload the storage overheads to a manageable distributed
storage to improve the scalability of solution. We focused the
storage utilization of the Hyperledger fabric blockchain by
retrieving the growth of the actual ledger using CouchDB ad-
ministration tools in Hyperledger. In the evaluation, we observed
that around 4MB is utilized for dynamic registration of 5000
objects which is relatively low and makes the system scalable
with minimal storage overhead. The results are displayed in
Figure 6.
D. Comparison with Related Work
TABLE I: Features Comparison with Key Related Works
[7] [8] [9] [11] [12]
ECQV certificates No No No No No Yes
Remote patient connec-
No Yes Yes Yes Yes Yes
Scalable storage No No No No No Yes
Patient anonymity Yes No No No Yes Yes
Operating on realtime
No Yes Yes Yes Yes Yes
Decentralization Yes No No Yes Yes Yes
Data sharing rewarding No No No No No Yes
Blockchain fees N/A No N/A No No No
A global interest towards telehealth systems surged with the
pandemics to keep the patients home and connect to the hospital
persistently on the treatment process. The growth of global
elderly population is also a significant reason for the researchers
to investigate on the telehealth systems in depth. We proposed
a MEC and blockchain based secure service architecture which
provides data privacy, integrity, authentication, and anonymous
data sharing capability for the future research using lightweight
ECQV mechanisms. We compared our work with a few existing
solutions in Table I We incorporated the smart contracts to exe-
cute different actions of the proposed systems such as signature
verification, access revocation and so on. We performed a near
realistic performance evaluation and validated that our system
can tolerate high transaction volumes through the MEC nodes,
with minimal latency. Furthermore, we optimized the blockchain
storage by offloading to the IPFS storage which is extensible.
Overall, our solution was designed targeting the lightweight
computing nodes and we observed the benefits for performance
in the evaluation.
We expect to develop the proposed system towards more scal-
ability by evaluating with other blockchain platform. We further
expect to enable on-chain secure multi-party computations on
the healthcare data.
This work is party supported by European Union in RE-
SPONSE 5G (Grant No: 789658) and Academy of Finland in
6Genesis (grant no. 318927) and SecureConnect projects.
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Science and Ubiquitous Computing. Springer, 2018, pp. 550–556.
... Studies in, [47], VOLUME 10, 2022 [48], [63], [64], proposed AuthN solutions, while studies in [42], [43], [50], [54], [55], [58], [59], [65] proposed AuthZ solutions. The rest of the studies [44]- [46], [49], [51]- [53], [56], [57], [60]- [62] covered both AuthN and AuthZ solutions. A complete IAM system should involve AuthN and AuthZ operations, as well as covering the identity management (IdM) for the whole life-cycle of identities, from registration to deleting identity data when it is no longer belongs to a system. ...
... As shown in Table 8 there are sixteen studies among the reviewed studies that included IdM in the IAM system. Although, they covered registration and verification processes, only two studies [57], [60] covered the revocation process. The lack of the revocation process causes functional and security issues. ...
... IAM for MIoT device security and management were proposed in [42]- [48]. In [57], an IAM solution was proposed for a telehealth system, and in [63], an IAM solution was proposed for a m-Health system. In [52] and [61], IAM solutions were proposed for PHR and in [51], [54], [58], and [65], IAM solutions were proposed in DSMS. ...
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Identity and Access Management (IAM) systems are crucial for any information system, such as healthcare information systems. Health IoT (HIoT) applications are targeted by attackers due to the high-volume and sensitivity of health data. Thus, IAM systems for HIoT need to be built with high standards and based on reliable frameworks. Blockchain (BC) is an emerging technology widely used for developing decentralized IAM solutions. Although, the integration of BC in HIoT for proposing IAM solutions has gained recent attention, BC is an evolving technology and needs to be studied carefully before using it for IAM solutions in HIoT applications. A systematic literature review was conducted on the BC-based IAM systems in HIoT applications to investigate the security aspect. Twenty-four studies that satisfied the inclusion criteria and passed the quality assessment were included in this review. We studied BC-based solutions in HIoT applications to explore the IAM system architecture, security requirements and threats. We summarized the main components and technologies in typical BC-based IAM systems and the layered architecture of the BC-based IAM system in HIoT. Accordingly, the security threats and requirements were summarized. Our systematic review shows that there is a lack of a comprehensive security framework, risk assessments, and security and functional performance evaluation metrics in BC-based IAM in HIoT applications.
... Smart-contract handles access-control mechanisms. 44 The present trend in data communication is the blockchain based-approach for authentication. This blockchain network could leverage to offer transparent data communication, [45][46][47] such that these data permit the secure level of recording in a wider diversified hospital network. ...
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Sharing a file that contains multimedia data among the different peers of wireless Internet of Things (IoT) networks has several challenges. One of the main challenges is their centralized system, which leads to high‐security risk and low user reachability. One solution could be to simply change the system to a decentralized network by using the blockchain network to store these files. However, it may solve the low user reachability and security problem at the cost of low latency, longer response time, scalability and privacy issues. Therefore, this article uses the advanced blockchain scheme and distributes InterPlanetary File System. We also presented the system framework and its working. Finally, we do the security analysis of our proposed system and found that it has strong potential to solve most of the security challenges that traditional system faces. Moreover, our proposed approach can be applied to any file‐changing wireless IoT network that needs to exchange multimedia data such as healthcare data, IoT data in wearable devices, traffic data in smart cities, etc.
... However, mobile clients such as laptops and smartphones will lose protection when they leave the network due to using intermediaries. Hewa et al. [33] proposed the application of an Elliptic Curve Qu-Vanstone (ECQV) certificate, which is lightweight for resource-constrained IoT devices. Additionally, they integrate blockchain-based smart contracts to handle certificate-related operations. ...
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With the development of blockchain, many studies apply blockchain to certificate revocation. However, existing blockchain-based certificate revocation schemes have two shortcomings. First, the storage overhead on the blockchain is relatively large. Second, as the number of revoked certificates increases, the misjudgment rate of certificate status will increase accordingly, so a public key infrastructure implementation certificate revocation scheme based on blockchain and accumulators, called CR-BA, is proposed. First, CR-BA expands the certificate structure, adding a revocation factor and a smart contract account for accessing the blockchain in the certificate extension, which is filled by the CA when the certificate is generated. Then, when the certificate is to be revoked, CA generates the revocation fingerprint through the revocation factor and publishes it to the blockchain. Finally, when the user needs to verify the status of the certificate, CA calculates the revocation fingerprint according to the revocation factor on the certificate, then compares it with the existing revocation fingerprint on the blockchain, and returns the comparison result to the user. The experimental results show that this scheme can effectively overcome the storage and misjudgment problems caused by existing blockchain-based certificate revocation schemes and improve the query efficiency of certificate revocation information.
... This approach has been proven in a telerehabilitation system for individuals with balance issues that uses augmented reality to provide a surrogate physiotherapist as a superimposed hologram, along with easyto-use wearable sensors. Hewa et al. [168] employed many decentralized applications to transmit cryptocurrency to patients' wallets effectively and reliably as an incentive for providing their health information utilizing edge computing and blockchain technology. To provide scalability with a large number of connected biomedical sensors to the cloud, the system included memory offloading functionality into the blockchain. ...
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Nowadays, the Internet has spread to practically every country around the world and is having unprecedented effects on people's lives. The Internet of Things (IoT) is getting more popular and has a high level of interest in both practitioners and academicians in the age of wireless communication due to its diverse applications. The IoT is a technology that enables everyday things to become savvier, everyday computation towards becoming intellectual, and everyday communication to become a little more insightful. In this paper, the most common and popular IoT device capabilities, architectures, and protocols are demonstrated in brief to provide a clear overview of the IoT technology to the researchers in this area. The common IoT device capabilities including hardware (Raspberry Pi, Arduino, and ESP8266) and software (operating systems, and built-in tools) platforms are described in detail. The widely used architectures that have been recently evolved and used are the three-layer architecture, SOA-based architecture, and middleware-based architecture. The popular protocols for IoT are demonstrated which include CoAP, MQTT, XMPP, AMQP, DDS, LoWPAN, BLE, and Zigbee that are frequently utilized to develop smart IoT applications. Additionally, this research provides an in-depth overview of the potential healthcare applications based on IoT technologies in the context of addressing various healthcare concerns. Finally, this paper summarizes state-of-the-art knowledge, highlights open issues and shortcomings, and provides recommendations for further studies which would be quite beneficial to anyone with a desire to work in this field and make breakthroughs to get expertise in this area.
With the advent of the Internet of Things (IoT), its uses have been growing enormously in the smart healthcare sector. IoT devices in smart healthcare range from simple wristband devices capable of monitoring the heart rate, sleep pattern, and blood pressure to connected inhalers, ingestible sensors, glucose monitoring, and remote patient monitoring systems. To fulfill the requirements of smart healthcare, these devices need to have reliable connectivity and must comply with the security and privacy regulations. To resolve the connectivity issue in healthcare, 5G networks are being deployed. Implementation of 5G in blockchain-based smart healthcare will bring many advantages such as faster and seamless data transfer of patients’ medical records, real-time monitoring of patients at remote locations, 5G-powered telesurgery, and monitoring of smart IoT devices. This chapter provides a detailed literature review in this field to understand concepts of blockchain in the healthcare industry. Blockchain technology, its mechanisms, 5G technology, and Blockchain 5.0-based smart healthcare are described. Applications of Blockchain 5.0 in healthcare informatics are discussed in detail. A comparative analysis of existing models on electronic health records is performed and one case study is considered to understand the application of blockchain in healthcare. The challenges for 5G-enabled blockchain technology in the healthcare sector are also addressed, which provides an opportunity for researchers to resolve these issues for better performance of the system.
Multiaccess edge computing (MEC) is creating a technology impact in distributing the computation to the edge of the radio access network (RAN). The MEC offers large bandwidth, low latency, highly efficient network operation and host of services to the end user. This property of MEC is going to benefit in handling the computation of voluminous data traffic created in fifth-generation (5G) network. However, MEC acts as a key technology to provide the concept of architecture of evolution of 5G from previous generation by transforming the mobile broadband network into advanced programmable and computational infrastructure for satisfying the requirements of 5G such as throughput, scalability, latency and automation. Although Internet of Thing (IoT) is an integral part of 5G, Reduced Functional Devices have limited storage and processing capabilities. Therefore, to fulfill the requirements of new compute-intense applications of 5G IoT, it is intuitive to converge with MEC for smooth processing of large data traffic to offload the cloud especially in the era of artificial intelligence and machine learning. This chapter deals with an overview of the convergence of 5G with MEC technology and enlightens technical aspects, which are crucial for any IoT-based smart healthcare system with a special focus on Convergence between 5G and MEC in the purview of IoT applications.
Brain-Computer Interface (BCI) also referred to as Brain-Machine Interface (BMI) is a buzzword in the world of neuroscience, translating the human brain's thoughts into a chip. These devices may be surgically implanted or placed externally. Such components allow the user to control the actuators or sense the input data through bilateral communication to achieve the task. Most of the current applications focus on neural prosthetics, artificial limbs, cochlear implants, and assistive devices for people affected by neurological disorders such as Alzheimer’s, Parkinson’s and more. Initially, It was developed to assist persons with neurological disorders. Due to the evolution of non-invasive imaging components, BCI is being extended for public communication like the brain- brain interfacing. The implementation of BCI on neuromorphic hardware components would further improve the computational complexity, execution speed, energy efficiency, and robustness against local failure. Machine learning and deep learning algorithms are contributing to computer vision, speech recognition, game control, autonomous vehicle systems, disease classification/prediction, and many more. Though BCI has improved the lifestyle of the end-users, the responsiveness of those devices is not alike natural elements. Hence, to create an effective pathway from a brain to the external world through mapping, augmenting, assisting and troubleshooting, many computational intelligence methods have been proposed. In this chapter, the translation of brain waves into features and further classified to control any applications in an open/closed environment with a secure mechanism along with adaptive learning algorithms will be discussed.
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Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints. By means of deep reinforcement learning to estimate future rewards of actions based on the reported information from the users served by the networks, we introduce single-layer MRA algorithms based on deep Q-learning (DQN) and deep deterministic policy gradient (DDPG), respectively, as the basis for the downlink wireless transmissions. Moreover, by incorporating the capability of data-driven DQN technique and the strength of noncooperative game theory model, we propose a two-layer iterative approach to resolve the NP-hard MRA problem, which can further improve the communication performance in terms of data rate, energy harvesting, and power consumption. For the two-layer approach, we also introduce a pricing strategy for BSs or APs to determine their power costs on the basis of social utility maximization to control the transmit power. Finally, with the simulated environment based on realistic wireless networks, our numerical results show that the two-layer MRA algorithm proposed can achieve up to 2.3 times higher value than the single-layer counterparts which represent the data-driven deep reinforcement learning-based algorithms extended to resolve the problem, in terms of the utilities designed to reflect the trade-off among the performance metrics considered.
Conference Paper
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Blockchain is so far well-known for its potential applications in financial and banking sector. However, blockchain as a decentralized and distributed technology can be utilized as a powerful tool for immense daily life applications. Healthcare is one of the prominent applications area among others where blockchain is supposed to make a strong impact. It is generating wide range of opportunities and possibilities in current healthcare systems. Therefore, this paper is all about exploring the potential applications of blockchain technology in current healthcare systems and highlights the most important requirements to fulfill the need of such systems such as trustless and transparent healthcare systems. In addition, this work also presents the challenges and obstacles needed to resolve before the successful adoption of blockchain technology in healthcare systems. Furthermore, we introduce the smart contract for blockchain based healthcare systems which is key for defining the pre-defined agreements among various involved stakeholders
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The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.
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Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized approach for resource and transaction management. Although blockchain has been widely adopted in many applications, e.g., finance, healthcare, and logistics, its application in mobile environments is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new transactions to the blockchain. Solving the proof-of-work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious option to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then, we introduce an economic approach for edge computing resource management. Moreover, a demonstrative prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.
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The use of wireless sensor networks for wearable computing in health care is growing quickly. Numerous applications are already in use, such as blood pressure monitors and heart rate monitors. As such, it is very important for system designers to consider how to protect patient privacy, especially in wireless sensor networks. After studying and analyzing the features of wireless sensor networks in medical systems, a data division scheme was proposed in this paper, provided the advantages of homomorphic encryption. In the proposed scheme, even if a forwarding node is compromised, the attacker will not be able to eavesdrop on the data, resulting in much stronger privacy than existing schemes. Experimental results shows that the scheme provides a good trade off in resources consumed and system security, and is efficient for encrypting or decrypting sensitive medical data.
The rapid growth of mobile services and Internet of Things (IoT) has caused severe demands of a management system for Mobile Edge Computing (MEC) where User Equipments (UEs) benefit from high computational power, capacity and communication as well as the offered services by MEC. However, a comprehensive management is required to orchestrate the services and resource in MEC to fairly distribute to UEs with the aim of ensuring the Quality of Experience (QoE). In this paper, we propose a decentralization concensus secure and authentication framework for MEC management/orchestration system with crucial security and authentication components by which it ensures the delivery of users’ quality of experience.
Conference Paper
Unmanned aerial vehicle (UAV) is an emerging technology that becomes popular not only in military operation but also in civil applications. Internet of things (IoT) is another popular technology which brings automation in our daily life. Like other areas, IoT also exposes its potential in healthcare. Using IoT sensors, it becomes easy to monitor the health of a user remotely. UAV consolidated with mobile edge computing (MEC) can provide real-time services in outdoor health monitoring. However, communication among them surrounds with cyber threats and data integrity issue. Blockchain is a data structure in which data are shared among peers. In this paper, a blockchain based secure outdoor health monitoring scheme using UAV is proposed for a smart city. In the proposed scheme, health data (HD) are accumulated from users wearable sensors and these HD are transmitted to the nearest MEC server via UAV. Prior to transmitting to MEC, HD experience encryption in order to provide protection against cyber threats. Moreover, after arriving at MEC, HD are diagnosed and if any abnormalities are found in the user’s health, MEC server notifies the user and the nearest hospitals. When the processing is completed, HD are stored in blockchain with the consent of validators. Finally, simulation results and experimental set up are discussed in order to manifest the feasibility of the proposed scheme.
The first comprehensive guide to the design and implementation of security in 5G wireless networks and devices. Security models for 3G and 4G networks based on Universal SIM cards worked very well. But they are not fully applicable to the unique security requirements of 5G networks. 5G will face additional challenges due to increased user privacy concerns, new trust and service models and requirements to support IoT and mission-critical applications. While multiple books already exist on 5G, this is the first to focus exclusively on security for the emerging 5G ecosystem. 5G networks are not only expected to be faster, but provide a backbone for many new services, such as IoT and the Industrial Internet. Those services will provide connectivity for everything from autonomous cars and UAVs to remote health monitoring through body-attached sensors, smart logistics through item tracking to remote diagnostics and preventive maintenance of equipment. Most services will be integrated with Cloud computing and novel concepts, such as mobile edge computing, which will require smooth and transparent communications between user devices, data centers and operator networks. Featuring contributions from an international team of experts at the forefront of 5G system design and security, this book: Provides priceless insights into the current and future threats to mobile networks and mechanisms to protect it. Covers critical lifecycle functions and stages of 5G security and how to build an effective security architecture for 5G based mobile networks. Addresses mobile network security based on network-centricity, device-centricity, information-centricity and people-centricity views. Explores security considerations for all relative stakeholders of mobile networks, including mobile network operators, mobile network virtual operators, mobile users, wireless users, Internet-of things, and cybersecurity experts. Providing a comprehensive guide to state-of-the-art in 5G security theory and practice, A Comprehensive Guide to 5G Security is an important working resource for researchers, engineers and business professionals working on 5G development and deployment.
Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and the huge amount of data coming from IoT devices toward the Internet. In the next future, edge-based approaches will be essential to support time-dependent applications in the Industry 4.0 context; thus the paper proposes ${BodyEdge}$ , a novel architecture well suited for human-centric applications in the context of the emerging healthcare industry. It consists of a tiny mobile client module and a performing gateway supporting multi-radio and multi-technology communication to collect and locally process data coming from different scenarios; moreover, it also exploits the facilities made available from both private and public cloud platforms to guarantee a high flexibility, robustness and adaptive level of service. The advantages of the designed software platform have been evaluated in terms of reduced bandwidth and processing time through a real implementation on different hardware platforms. The conducted study also highlighted the network conditions (data load and processing delay) in which ${BodyEdge}$ is a valid and inexpensive solution for healthcare application scenarios.
With the rapid development of medical and computer technologies, the healthcare system has seen a surge of interest from both the academia and industry. However, most healthcare systems fail to consider the emergency situations of patients, and are unable to provide a personalized resource service for special users. To address this issue, in this paper, we propose the Edge-Cognitive-Computing-based (ECC-based) smart-healthcare system. This system is able to monitor and analyze the physical health of users using cognitive computing. It also adjusts the computing resource allocation of the whole edge computing network comprehensively according to the health-risk grade of each user. The experiments show that the ECC-based healthcare system provides a better user experience and optimizes the computing resources reasonably, as well as significantly improving in the survival rates of patients in a sudden emergency.