Blockchain and IPFS based Service Model for the
Internet of Things
Hajra Zareen1, Saba Awan1, Maimoona Bint E Sajid1, Shakira Musa Baig1, Muhammad Faisal2, Nadeem Javaid1,∗
1Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
2Iqra National University, Peshawar, Hayatabad Peshawar 25000, Pakistan
Email:firstname.lastname@example.org ,email@example.com, firstname.lastname@example.org,
∗Corresponding author: email@example.com; www.njavaid.com
Abstract—In this paper, blockchain and InterPlanetary File
System (IPFS) based service model is proposed for Internet
of Things (IoT). In the IoT, nodes’ credentials and generated
data are stored on the IPFS in a hashed format. In order to
ensure the security of data, encrypted hash is stored on the
blockchain. However, blockchain is very expensive for storing
the large amount of data. While, in the case of centralized
database, there is a possibility of data tampering and information
leakage. Moreover, a service model is proposed for sharing the
services from the service providers to the consumers. In addition,
a product consensus mechanism is performed between admin and
user which is replaced with a blockchain service model. Due to
this, consumers send the request to the service provider through
blockchain for the required services. Though, peer and minor
nodes’ involvement in consensus mechanisms, hinder in ﬁnding
the service provider. Moreover, existing scheme is computationaly
expensive and delay occurs due to the lengthy procedure of
veriﬁcation and consensus. Here, blockchain is used to record
the evidence of the services. Also, a service veriﬁcation scheme
is designed using the Secure Hash Algorithm-256 (SHA-256).
Furthermore, the smart contract is utilized to settle disputes
between consumers and service providers. The simulation results
show that Proof of Authority consumes less gas and has low
latency as compared to Proof of Work, which represents the
efﬁciency and effectiveness of the proposed solution.
Index Terms—Internet of Things, Blockchain, InterPlanetary
File System, Smart Contract, Consensus Mechansism, Service
Internet of Things (IoT) plays a signiﬁcant role in today’s
world through promoting social and economic development.
A Wireless Sensor Network (WSN) is considered the key
technology in IoT architecture, which plays a signiﬁcant
role in promoting IoT. The IoT is now extensively used
in various ﬁelds like smart cities, healthcare, smart power
grids, etc. As IoT consists of many devices, so the security
of IoT devices is compromised because of different kinds
of cyberattack. Therefore, several centralized and distributed
architecture have been proposed to tackle these attacks. How-
ever, the existing solutions are not suitable due to single point
of failure, high latency, storage constraints, high computa-
tional cost, big data collection and lack of privacy which
leads to inaccurate decision making . The edge, cloud
and transparent computing have been developed to extend
terminals devices’ functionality with the on demand service
provisioning scheme . The existing service provisioning
schemes face security challenges like a service provider may
provide nonconformance or malicious services. In addition,
the dishonest clients may deny from the correct services.
The traditional nonrepudiation mechanisms can be classiﬁed
into two categories: Trusted Third Party (TTP) and Non TTP
schemes. In the former scheme, single point of failure and
performance bottlenecks are the major issues. While in a later
scheme, high performance cost, no nonrepudiation evidence
and weak evidence are notable issues .
Nakamoto introduced the blockchain in 2008 which consists
of blocks that are in sequences and chained together. Each
block contains the hash of the previous block, current block
and data. Data of each block is stored in the form of a
Merkle Tree (MT). If any data tamper is performed, it can
be easily identiﬁed while comparing the data with the root
hash . Blockchain is utilized to overcome the limitations
of a centralized system and involvement of a third party.
Blockchain consists of a Peer-to-Peer (P2P) network and
distributed ledger, which offers tamper resistant, secure and
immutable services. Blockchain’s ﬁrst implementation was
bitcoin. Later various domains adopted it, such as the Internet
of Vehicles, IoTs, the energy sector, etc., .
However, blockchain is very expensive for storing a large
amount of data. As two databases (Blockchain and local
database) are considered to store each nodes’ data that require
extra maintenance cost and storage capacity. Moreover, de-
registration and re-registration of the nodes require extra
storage space. Also, there is a possibility of data tampering and
information leakage. Furthermore, product consensus among
users and admin involves minor nodes and peer nodes. In-
addition, computational cost and delay increases due to the
lengthy procedure of veriﬁcation and consensus mechanism.
To overcome the issues addressed above, blockchain and
InterPlanetary File System (IPFS) based service model for IoT
The contributions of the paper are as follows:
•the blockchain and IPFS based service model is proposed.
The blockchain is utilized to store the evidence of the
services while IPFS is used to store and share data
•the service veriﬁcation scheme is designed which is based
on the Secure Hashing Algorithm (SHA-256) and
•the smart contract is utilized to settle service disputes
between consumers and service providers.
The remainder of the paper is organized as follows: related
studies are presented in Section II. Proposed system model is
demonstrated in Section III. Simulation Results are discussed
in Section IV and Section V presents the conclusion of this
II. RE LATE D WOR K
Due to many IoT devices, security is a major concern
in IoT devices, so it is urgent to protect them from cyber
attacks. Several centralized and distributed architecture has
been proposed to tackle these attacks. Existing solutions are
not suitable due to single point of failure, high latency, storage
constraints and high computational cost. Besides this, the
traditional systems face issues like big data collection and
lack of privacy. A sufﬁcient collection of data is not possible,
leading to inaccurate decision-making  .
The Industrial IoT (IIoT) is referred to as smart sensors
to enhance manufacturing and industrial processes. Network
computing technologies increase the functionalities of IIoT.
Almost all existing service-provisioning schemes face new se-
curity challenges, which causes the anxiety of stakeholders and
concern about using such provisioning technologies. A service
provider may provide nonconformance or malicious services.
In comparison, a dishonest client may deny getting the correct
services. The traditional Nonrepudiation mechanisms can be
classiﬁed into two categories: TTP and Non-TTP schemes. In
the former scheme, single point of failures and performance
bottlenecks are the major issues in centralized TTP. However,
in the later scheme high-performance cost, no nonrepudiation
evidence and provides weak evidence for any party for self-
WSNs consist of issues related to the privacy and security
of the data due to constraints. Besides this, authentication
of every node and managing trust are necessary. However,
previous work either handles security, privacy, or authen-
tication and trust management, but none of them handles
the trust management and authentication in WSN and IoT
. The traditional IoT identity authentication protocols uses
centralized authentication methods and mostly rely on the
trusted third parties. As a result, it causes single point of failure
The WSN has a self-organizing structure where various
sensor nodes are being deployed at a random positions to
collect data from the physical environment. There are two
types of sensor nodes named beacon and unknown nodes.
Beacon nodes know their location through Global Positioning
System(GPS) or manually assigned them. At the same time,
Unknown nodes get their position through localization. As
localization has a signiﬁcant impact in many WSNs applica-
tions but accurate location estimation is still a big challenge.
Localization has many problems like location estimation due
to which accuracy is affected. In contrast, energy conservation
is another issue that decreases the lifetime of a WSN. Another
concern is malicious attack or activity. In this case, false
location information is broadcasted in the network . In the
routing algorithms, malicious nodes attack and compromise
the routing nodes thus they sends the wrong queue length
information to maximize the chances of getting the packets.
When it receives the packets, it does not forward them to
neighbor nodes and discard them, making the black hole in
the network. This phenomenon is called a black hole attack.
So, for trust management among routing nodes, a third party is
utilized, but it does not tackle the multi-hop distributed WSN.
Besides this, there is a possibility of attack and compromised
by malicious nodes, so the security and fairness cannot be
All IoT devices contain personal information and are con-
nected to the internet. So, for this reason, it requires cer-
tiﬁcation. The lightweight IoT devices that perform simple
tasks have low-performance chipsets or no operating system
running. Most IoT devices do not have authentication methods
due to the limited resources. It does not support encryption
protocol or certiﬁcate and then it is vulnerable . The rout-
ing protocols require CA to authenticate, identify or remove
IoT devices. There is a trust issue between the IoT vendors
in central management due to disagreement in a centralized
system. Besides this, it needs a secret key sharing mechanism,
which requires high cost for implementation .
In a dynamic WSNs, key management security is compro-
mised by BS, which is untrusted and easily compromised.
Also, it causes additional overhead on sensors in the key
distribution scheme. Besides this, it consists of a complex
design protocol . The main issue of hierarchical sensor
networks is data transmission’s security and privacy without
high computational cost . As the mobile phones consist
of sensor nodes that have limited resources. So, utilizing
blockchain requires abundant resources to perform Proof of
Work(PoW). Besides this, WSN has limited battery power, so
it drained quickly and thus the whole network is affected .
The authenticity of the data and user privacy in a vehicular
sensor network is the major issue .
Mobile devices consist of limited resources, so applying
blockchain on the wireless mobile network requires abundant
resources (computational and storage) to solve the PoW puzzle
for the mining process . No secure caching scheme can be
tackled in the Information-Centric Network (ICN) that based
on WSNs . Nodes in a WSN have limited energy and
storage capacity. Due to this reason, many network nodes
prefer to preserve their resources. If most nodes act selﬁshly
and do not forward the packets, then the whole network
will not work properly . Crowdsensing networks utilized
mobile phone sensors for the collection of data to reduce
the cost. However, there is a threat of privacy leakage. Due
to this reason, users do not trust on it, upload incorrect
information and also their involvement is low . Now a
day’s WSN plays a signiﬁcant role. However, sensors nodes
have constraints like limited energy and transmission range.
Due to this reason, they faces several threats. These threats
can be divided into two parts data and routing. A data attack
malicious node changes the data in the payload for learning
the transmission details or performing the variation. While in
routing attacks, malicious nodes choose the wrong path by
WSN has a self-organizing structure where various sensor
nodes are being deployed at random positions to collect data
from the physical environment. There are two types of sensor
nodes named beacon and unknown nodes. Beacon nodes
know their location through GPS or manually assigned them.
In comparison, Unknown nodes get their position through
localization. There are two types of localization algorithms
known as range based and range-free. The former approach
requires special hardware, so its procedure needs high cost
for localization. While in the case of range free approach, the
mobility of sensor nodes can change the framework’s network
topology. Besides this, constant alarming situation because of
malicious nodes make the framework overprotective .
Different types of threats occur in Industrial IoT. Workers
gain access to the restricted area of the industry, steal, or mis-
place the products, information, or important records through
compromising the sensor nodes, moreover, by getting open
access to the information. Furthermore, to register the IoT
devices, collect and verify the registration every time. So that
no one can be able to modify or change it . WSNs have
a signiﬁcant impact on IoT development. There are mainly
two types of threats in WSNs, external and internal attacks.
In the external attacker attacks from outside the network,
while an internal attack node is compromised, it attacks from
within the network. So, the detection of a malicious node
within the network is very important. Also, there is no way to
record the original data and detection process to trackback later
. Various challenges occur when blockchain is deployed
in IIoT. Blockchain requires massive computing power in
deployment. While IIoT demands maximum storage capacity
and bandwidth to handle a huge amount of data. For these
reasons, resource-constrained devices do not support certain
operations because of their conﬂicts between heterogeneous
network resources and IIoT devices .
The Central Processing Unit usage rate of the mining pro-
cess, hash operations, hash quality, self-proposed block num-
ber, system throughput, remaining blocks ﬁltered by UBOF in
different scale of accounts, remaining blocks in different scale
of throughput and storage cost are the key parameters that
are considered for the validation of the proposed scheme .
Network latency and data delivery issues occur due to mobile
sensors. Mobile WSNs sensor nodes cannot send information
to their mobile CH until the mobile CH comes to its cluster
boundary. Besides this, mobile CHs rises the possibility of
malicious nodes to join the network and compromise its data
Shifting the global population in the urban area can maxi-
mize the burden on smart city network architecture in struc-
tural scalability, bandwidth constraints, low latency, high mo-
bility, single points of failure, security and privacy of data
. As IoT devices contain a huge amount of data to access
data, it takes a lot of time as IoT sensors have limited
computing power and storage capacity. Due to this reason,
it quickly runs out of battery when process the transactions
faster. Limited bandwidth and slow update rate can cause
major issues in time-critical applications as it requires faster
information updates .
III. SYS TE M MOD EL
In this paper, blockchain and IPFS based Service Model is
proposed which is motivated from , . It consists of the
following three entities.
Service Provider: Any person or organization which owns the
data and provide the services to the consumers (C).
Consumer: Make a request for the service and execute the
Arbitrator: It is from the minor nodes which uses the smart
contract for abitration. Arbitrator (A) responsibility is to settle
disputes between C and service provider (SP). A is also
utilized to maintain the distributed ledger.
The proposed system consists of two phases, namely registra-
tion and service model.
The registration of every node is compulsory before pro-
viding any services. So, all the nodes are registered in the
blockchain in the registration phase and contain a unique
account address. Also, the node comprises other relevant
information like services name, services data and hash value.
After successful registration of the nodes, they can provide the
desired services. The service record of every node is stored on
B. Service Model
This section contains the description of Elliptic Curve
Integrated Encryption Scheme (ECIES) and IPFS. Moreover,
blockchain and IPFS based service model is described.
1) Elliptic Curve Integrated Encryption Scheme: ECIES is
the enhancement of Elliptic Curve Cryptography (ECC) which
is considered as the public key-based encryption scheme. It
gives the semantic security against the intruders . As,
the intruder can utilizes plaintext and ciphertext attacks. Our
service model uses ECIES to encrypt the services data.
2) InterPlanetary File System: Our service model utilizes
a distributed ﬁle storage system, IPFS. As IPFS is content-
addressable, so if any modiﬁcation is done on the data, its
content address also changes. After encryption of data through
ECIES, it is uploaded on the IPFS. Hash is generated against
each data which is later stored on the blockchain.
3) Description of the Service Model: In traditional scheme
product consensus between admin and user is replaced through
the service model. The user sends a request to the SP for
the required services so no Cental Authority (CA) is used to
Data Storage and Encryption
Client Service Provider Registration
Phase1: Node Registration in blockchain
2.3 Deliver S2 through IPFS
2.5 Publish S1
2.2.Publish Hash (S2)
2.4 Confirm Hash (S2)
2.6 Confirm Hash (S)
2.1 Request Service S
Phase2: Service Model
Fig. 1. Proposed system model
ﬁnd a respective SP. Here, blockchain is utilized to record
the evidence of the services. The whole service is split into
two nonexecutable fragments. The major off-chain part is sent
through IPFS and the tiny part is delivered via on-chain, which
can reduce the burden on blockchain and enforce the C to
submit evidence of the off-chain part (IPFS) on the blockchain.
So, through these steps fairness of the mechanism is ensured.
Also, a service veriﬁcation scheme is designed based on SHA-
256 that only validates the on-chain evidence instead of the
whole service program. The smart contract based technique is
utilized to settle service disputes between C and SP. It provides
fair and effective dispute resolution.
The whole service scheme process is demonstrated as follows
(also see the Fig.1)
Step 1: First, C sends the request of the transaction to SP for
the desired service. This request consists of the service name
and crypto token.
Step 2: SP calculates the hash of S2, which is a major part
of service S2, by using the hash function SHA-256. After
calculating the hash, it is encapsulated with the token within
the transaction and then sent to the respective C for the
Step 3: As Hash of S2 is published on the blockchain
successfully. Now SP sends the S2 part through IPFS to C.
As the major part is sent through IPFS and the tiny part is
delivered via on-chain, it reduces the burden from blockchain
and enforces the client to submit evidence of the off-chain part
(IPFS) on the blockchain. So, these steps ensure the fairness
of the repudiation mechanism.
Step 4: C compute the hash value of the obtained S2 through
SHA-256 and then compare it with the value stored in
blockchain. If both values match, then C conﬁrms it by sending
the transaction conﬁrmation with the SP account’s token.
Whereas, if its value does not match, then C invokes smart
contract for the termination of service program. In this step,
SP can also terminate the service program if no response is
received from the C within a speciﬁed time. As in Step 4, C is
bound to conﬁrm the off-chain part S2 for complete executable
service program. Otherwise, both cannot obtain any beneﬁt in
case of termination of the service program. This demonstrates
the true fairness of the scheme.
Step 5: Afterwards, conﬁrmation of the previous transaction
from C is accessible on blockchain. The SP sends the trans-
action as evidence that comprises a small part S1 and token
to the C account.
Step 6: After getting the S1, C restore the whole service. C has
two options: either conﬁrm the service or call for arbitration
through a smart contract. Likewise in step 4, SP has a right to
arbitrate if no response is received within speciﬁed time. Note
that in step 5, hash of S1 is already published as evidence on
the blockchain, so the smart contract can easily resolve the
dispute based on previous evidence without ﬁnal conﬁrmation
MAP PIN G TABL E
Limitations identiﬁed Solutions proposed Validations
L1: Extra maintenance
cost and storage capacity.
Data tampering and
information leakage in
local database .
S1: IPFS and encryp-
V1: Average gas
L2: Computational cost
increases and delay occurs
C. Mapping of Limitations to the Solutions
In ﬁrst limitation (L1), two databases (blockchain and local
database) are considered that require extra maintenance cost
and storage capacity. Also, due to the local database, there is
a possibility of data tampering and information leakage. To
overcome the L1, IPFS is utilized as the solution one (S1) of
the proposed model. In the registration phase, all the nodes
are registered in the blockchain before providing any services.
After successful registration of the nodes, they can provide the
desired services. Services record of every node is stored on the
IPFS. IPFS then generates the hash of the data. This process
lacks the security of data because its hash is shared with every
network node. The security of data is ensured by encryption
after that encrypted hash is stored on the blockchain.
In second limitation (L2), product consensus among user
and admin involves minor nodes and peer nodes which cause
increased computational cost and delay because of lengthy
procedure of veriﬁcation and consensus among nodes. To over-
come the L2, service model is proposed which is the second
solution (S2) . Product consensus between admin and user is
replaced by the blockchain service model. Users directly send
the request to the service provider for the required services
so that no CA is used to ﬁnd the respective service provider.
Average gas consumption and average transaction latency are
the key parameters considered for the validation (V1,V2) of
the proposed service model.
IV. SIMULATION RESULTS AN D DISCUSSION
This section provides the simulation results of the proposed
system model. Average gas consumption of ﬁve steps is
illustrated for both PoW and PoA consensus mechanisms.
In PoW all nodes participate in consensus, and it utilizes
maximum resources for mining. Whereas, in PoA, only pre-
selected nodes participate in consensus so it requires less
resources. According to Fig. 2, step 1 requires more gas in
case of PoA as compared to PoW due to pre-selection of minor
nodes. Step 2 and step 5 consumed maximum gas because
of publishing the hashes (s1, s2). In Fig. 3, the transaction
latency illustrates the efﬁciency of handling the transactions
in blockchain system. Two consensus mechanism are utilized
to check the average transaction latency. PoA has low latency
as compared to PoW due to the pre selected nodes that take
part in mining.
Step 1 Step 2 Step 4 Step 5 Step 6
Average Gas Consumption(gas)
Fig. 2. Average gas consumption
25 50 75 100 125 150 175 200 225
Number of Transactions
Average Transaction Latency (s)
Fig. 3. Average transaction latency
In this paper, we have proposed blockchain and IPFS based
service model for IoT. The proposed system consists of two
phases, namely registration and service model. In the registra-
tion phase, all the nodes are registered in the blockchain before
service provisioning. After successful registration of the nodes,
they can provide the desired services. The record of every
node is stored on the IPFS. The second phase consists of a
service model. In this model, the consumers send the request
to the service provider blockchain account for the required
services. Therefore, no middleman is used to ﬁnd a respective
service provider. Here, blockchain is utilized to record the
evidence of the services. Also, a service veriﬁcation scheme
is designed which is based on the SHA-256. Beside this, the
smart contract is utilized to settle service disputes between
clients and service providers. In case of conﬂict between both
parties, this mechanism provides fair and effective dispute
resolution. So, anyone can easily trust on it. The simulation
results shows that PoA consumes less gas as compared to
PoW. In addition, PoA has low latency as compared to PoW,
which shows the efﬁciency and effectiveness of the proposed
 Rathore, S., Kwon, B. W., Park, J. H. (2019). BlockSecIoTNet:
Blockchain-based decentralized security architecture for IoT network.
Journal of Network and Computer Applications, 143, 167-177.
 Alghamdi, T. A., Ali, I., Javaid, N., & Shaﬁq, M. (2019). Secure service
provisioning scheme for lightweight IoT devices with a fair payment
system and an incentive mechanism based on blockchain. IEEE Access,
 Xu, Y., Ren, J., Wang, G., Zhang, C., Yang, J., Zhang, Y. (2019). A
blockchain-based nonrepudiation network computing service scheme for
industrial IoT. IEEE Transactions on Industrial Informatics, 15(6), 3632-
 Moinet, A., Darties, B., Baril, J. L. (2017). Blockchain based trust
and authentication for decentralized sensor networks. arXiv preprint
 Sultana, T., Almogren, A., Akbar, M., Zuair, M., Ullah, I., & Javaid,
N. (2020). Data sharing system integrating access control mechanism
using blockchain-based smart contracts for IoT devices. Applied Sciences,
 Javed, M. U., Javaid, N., Aldegheishem, A., Alrajeh, N., Tahir, M.,
& Ramzan, M. (2020). Scheduling Charging of Electric Vehicles in a
Secured Manner by Emphasizing Cost Minimization Using Blockchain
Technology and IPFS. Sustainability, 12(12), 5151.
 Cui, Z., Fei, X. U. E., Zhang, S., Cai, X., Cao, Y., Zhang, W., Chen,
J. (2020). A hybrid BlockChain-based identity authentication scheme for
multi-WSN. IEEE Transactions on Services Computing, 13(2), 241-251.
 Kim, T. H., Goyat, R., Rai, M. K., Kumar, G., Buchanan, W. J.,
Saha, R.,Thomas, R. (2019). A Novel Trust Evaluation Process for
Secure Localization Using a Decentralized Blockchain in Wireless Sensor
Networks. IEEE Access, 7, 184133-184144.
 Yang, J., He, S., Xu, Y., Chen, L., Ren, J. (2019). A trusted routing
scheme using blockchain and reinforcement learning for wireless sensor
networks. Sensors, 19(4), 970.
 Hong, S. (2020). P2P networking based internet of things (IoT) sensor
node authentication by Blockchain. Peer-to-Peer Networking and Appli-
cations, 13(2), 579-589.
 Ramezan, G., Leung, C. (2018). A blockchain-based contractual rout-
ing protocol for the internet of things using smart contracts. Wireless
Communications and Mobile Computing, 2018.
 Tian, Y., Wang, Z., Xiong, J., Ma, J. (2020). A blockchain-based
secure key management scheme with trustworthiness in DWSNs. IEEE
Transactions on Industrial Informatics, 16(9), 6193-6202.
 Uddin, M. A., Stranieri, A., Gondal, I., Balasurbramanian, V. (2019). A
lightweight blockchain based framework for underwater iot. Electronics,
 Sergii, K., Prieto-Castrillo, F. (2018). A rolling blockchain for a dynamic
WSNs in a smart city. arXiv preprint arXiv:1806.11399.
 Kolumban-Antal, G., Lasak, V., Bogdan, R., Groza, B. (2020). A
Secure and Portable Multi-Sensor Module for Distributed Air Pollution
Monitoring. Sensors, 20(2), 403.
 Liu, M., Yu, F. R., Teng, Y., Leung, V. C., Song, M. (2018). Computation
ofﬂoading and content caching in wireless blockchain networks with
mobile edge computing. IEEE Transactions on Vehicular Technology,
 Mori, S. (2018). Secure caching scheme by using blockchain for
information-centric network-based wireless sensor networks. Journal of
Signal Processing, 22(3), 97-108.
 Ren, Y., Liu, Y., Ji, S., Sangaiah, A. K., Wang, J. (2018). Incentive
mechanism of data storage based on blockchain for wireless sensor
networks. Mobile Information Systems, 2018.
 Jia, B., Zhou, T., Li, W., Liu, Z., Zhang, J. (2018). A blockchain-
based location privacy protection incentive mechanism in crowd sensing
networks. Sensors, 18(11), 3894.
 Kuma r, M. H., Mohanraj, V., Suresh, Y., Senthilkumar, J., Nagalalli, G.
(2020). Trust aware localized routing and class based dynamic block chain
encryption scheme for improved security in WSN. Journal of Ambient
Intelligence and Humanized Computing, 1-9.
 Goyat, R., Kumar, G., Rai, M. K., Saha, R., Thomas, R., Kim, T. H.
(2020). Blockchain Powered Secure Range-Free Localization in Wireless
Sensor Networks. Arabian Journal for Science and Engineering, 45(8),
 Rathee, G., Balasaraswathi, M., Chandran, K. P., Gupta, S. D., Boopathi,
C. S. (2020). A secure IoT sensors communication in industry 4.0 using
blockchain technology. Journal of Ambient Intelligence and Humanized
 She, W., Liu, Q., Tian, Z., Chen, J. S., Wang, B., Liu, W. (2019).
Blockchain trust model for malicious node detection in wireless sensor
networks. IEEE Access, 7, 38947-38956.
 Liu, Y., Wang, K., Lin, Y., Xu, W. (2019). LightChain: A Lightweight
Blockchain System for Industrial Internet of Things. IEEE Transactions
on Industrial Informatics, 15(6), 3571-3581.
 Feng, H., Wang, W., Chen, B., Zhang, X. (2020). Evaluation on frozen
shellﬁsh quality by blockchain based multi-sensors monitoring and SVM
algorithm during cold storage. IEEE Access, 8, 54361-54370.
 Haseeb, K., Islam, N., Almogren, A., Din, I. U. (2019). Intrusion
prevention framework for secure routing in WSN-based mobile Internet
of Things. Ieee Access, 7, 185496-185505.
 Sharma, Pradip Kumar and Jong Hyuk Park. ”Blockchain based hybrid
network architecture for the s mart city.” Future Generation Computer
System s 86 (2018): 650 655.
 Rovira-Sugranes, A., Razi, A. (2019). Optimizing the Age of Informa-
tion for Blockchain Technology With Applications to IoT Sensors. IEEE
Communications Letters, 24(1), 183-187.
 Gayoso Mart´
ınez, V., Hern´
andez Encinas, L., and S´
(2010). A survey of the elliptic curve integrated encryption scheme.