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Reputation System for IoT Data Monetization using Blockchain

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Abstract and Figures

Internet of Things (IoT) is growing exponentially and bringing revolution in today's modern society. IoT based smart devices are source of convenience to human life and producing huge amount of data on daily basis. This data is useful for consumers like industries, marketplaces, and researchers to extract valuable and functional data from raw data generated by these devices. This data is used by industries and developers to provide more efficient devices and services to users. Owner of the IoT device can generate revenue by selling IoT device data to interested consumers. However, on the other hand consumers do not trust the owner of IoT device for data trading and are not confident about the quality of data. Traditional systems for data trading have many limitations, such as they are centralized, lack reputation system, security and involve third party. Therefore in this paper, we have leveraged the IoT with blockchain technology to provide a trustful trading through automatic review system for monetizing IoT data. We have developed blockchain based review system for IoT data monetization using Ethereum smart contracts. All transactions are secure and payments are automated without any human intervention. Data quality is ensured to consumer through reviews and ratings about the data. Additionally, Ethereum blockchain system requires gas for every transaction. We have used 2 parameters: gas consumption, string input length and in terms of time and cost, and examined our model.
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Reputation System for IoT Data
Monetization Using Blockchain
Atia Javaid, Maheen Zahid, Ishtiaq Ali, Raja Jalees Ul Hussen Khan,
Zainib Noshad, and Nadeem Javaid(B
)
COMSATS University, Islamabad 44000, Pakistan
nadeemjavaidqau@gmail.com,
http://www.njavaid.com/
Abstract. Internet of Things (IoT) is growing exponentially and bring-
ing revolution in today’s modern society. IoT based smart devices are
source of convenience to human life and producing huge amount of data
on daily basis. This data is useful for consumers like industries, mar-
ketplaces, and researchers to extract valuable and functional data from
raw data generated by these devices. This data is used by industries
and developers to provide more efficient devices and services to users.
Owner of the IoT device can generate revenue by selling IoT device
data to interested consumers. However, on the other hand consumers
do not trust the owner of IoT device for data trading and are not con-
fident about the quality of data. Traditional systems for data trading
have many limitations, such as they are centralized, lack reputation sys-
tem, security and involve third party. Therefore in this paper, we have
leveraged the IoT with blockchain technology to provide a trustful trad-
ing through automatic review system for monetizing IoT data. We have
developed blockchain based review system for IoT data monetization
using Ethereum smart contracts. All transactions are secure and pay-
ments are automated without any human intervention. Data quality is
ensured to consumer through reviews and ratings about the data. Addi-
tionally, Ethereum blockchain system requires gas for every transaction.
We have used 2 parameters: gas consumption, string input length and in
terms of time and cost, and examined our model.
Keywords: Message querring telemetry transport ·Blockchain ·IoT ·
Review system ·Metadata
1 Introduction
The Internet of Things (IoT) is a world wide network where humans, devices
and objects are connected with unique addresses. These devices have the capabil-
ity to communicate, share and transfer data through centralized servers. IoT is
growing exponentially year by year and gaining more attraction for all academic
fields and industries. More than 8.5 billion devices are connected to the IoT and
will increase up to 20.4 billion connected devices in 2020. The applications of
c
Springer Nature Switzerland AG 2020
L. Barolli et al. (Eds.): BWCCA 2019, LNNS 97, pp. 173–184, 2020.
https://doi.org/10.1007/978-3-030-33506-9_16
174 A. Javaid et al.
IoT in daily life makes its importance more evident. These devices produce data
of different types depending on the type of network. This data is used by pub-
lic users, industries and many other technologies like smart home automation,
health care, data trading market places, etc. This data is used to make human
life more convenient and advance. Using IoT devices for the purpose of sensing
as a service, huge amount of revenue could be generated.
As number of connecting devices are increasing, there is huge threat to net-
work security and data integrity. Traditional centralized systems because of secu-
rity, automation, scalability and third party issues are not feasible for IoT devices
to share or trade data and assets over the network. Centralized systems are costly,
involve hacking and trust issues along with the threat of single point of failures
[1]. Improving security and privacy regarding IoT is challenging because IoT
devices are energy, memory and resource constrained [2]. The available amount
of energy is utilized to perform needed application functions, because of which
security and other important features are compromised. Many researchers con-
tributed to handle such security issues like transmission field security [3], cloud
storage field [4], digital signature and permission identification [5,6]. In the field
of IoT and healthcare, researchers have worked on the IoT to collect data ubiq-
uitously [7], to provide processed data in time for efficient healthcare purposes.
The resource based IoT access methods are used to collect heterogenous data
ubiquitously from cloud and mobile computing platforms. On the other hand,
these devices are generating huge amount of data. Scalable data storage solution
is necessary to handle large amount of data efficiently.
Researchers have worked on such data storage issues to produce efficient
solutions for the structured and unstructured data [8]. Blockchain is new emerg-
ing technology with a lot of advantages of security, trust and immutability. It
is a decentralized ledger, previously only used for money transactions however,
it is now widely applied to diverse fields like smart grids, IoT [9], smart cities
[10], vehicle management [11] and many more. Once the transactions between
sender and receiver are recorded in the open and distributed ledger, then this
data cannot be tampered. In the mining process of blockchain, miner records
the transaction in the block and broadcast this block in the network, so that all
nodes have the same copy of transaction.
2 Related Work
Many studies have been done to leverage the advantages of blockchain in IoT.
Blockchain and IoT integration have achieved automation in industries and in
daily human life. Secure, scalable and adaptive industrial IoT platforms are
needed by the industries to achieve their goals. Existing industrial IoT platforms
are centralized systems having problems of single point failure. These systems
also face the problems like accessibility, confidentiality and integrity. Semantic
rules engine [12] and health based IoT systems [13] are facing the issue of sin-
gle point failure. In [14] authors have presented the industrial IoT system for
the smart factories to address the above mentioned problems. Directed Acyclic
Reputation System for IoT Data Monetization Using Blockchain 175
Graph (DAG) structured blockchain is used with efficient POW mechanism,
making DAG structured blockchains more efficient for industrial IoT systems.
Privacy and confidentiality are ensured through data authority management sys-
tem. However, they do not tackle the storage and data quality issues related to
sensor data. There is a major threat to traditional storage systems because of
centralized storage.
Traditional storage systems are vulnerable to single point of failures, and are
costly due to involvement of third party. In [15] a blockchain based storage sys-
tem named Sapphire is presented for analyses of data. For this storage system
smart contracts are created based on object storage device approach for interac-
tion of IoT devices with blockchain. Application specific operations are executed
only and results are provided to users instead of all the data which decreases
the storage and computational overhead of data analyses in IoT. However, this
system is not matured enough to deal with scalability and security issues. IoT
devices are used along with other electronic devices and protocols to perform
different tasks and operations without the human involvement. These electronic
and IoT devices and other network participants have to communicate with each
other to complete desired tasks. These devices must be authenticated before
entering the network and sharing data and resources with the other authenti-
cated entities. Authentication is essential, otherwise network will become target
for malicious users.
Conventional centralized authentication systems for IoT are not efficient. In
[16] authors have presented a decentralized system for authentication of devices.
Blockchain system provides security features and virtual zones provides trust-
ful environment where devices can authenticate each other. However, virtual
zones introduce the computational overhead and delay for the authentication
purpose. Different authors have used blockchain to tackle the several problems
such as: data trading, energy trading, node recovery, efficient energy routing,
edge servers participation, data rights management, healthcare issues, securing
data, fair sharing of data and under water routing problems. In [1727] authors,
have provided solutions for the above mentioned problems using blockchain.
2.1 Motivation
In [28], authors used review system for ensuring data quality and integrity of
data being traded through data marketplaces. Reviews are maintained by the
blockchain based system for the new users to analyze the reviews before using
the data.
2.2 Problem Statement
Earning revenue through the data produced by the IoT devices considering secu-
rity, cost efficiency, automatic monetization and centralized governance are the
challenging problems handled by the authors in [29].
176 A. Javaid et al.
Security: Previously, generating revenue from IoT does not consider the rules
and regulations provided from the owner of the IoT devices. Usage terms
about the data are not well defined.
Cost Efficiency: Automatic monetization of IoT data is costly due to the
involvement of third parties to exchange data and money.
Centralized Governance: Number of devices connected to the centralized plat-
form will create a bottleneck, and if centralized system is attacked or failed,
whole system will suffer the consequences.
Authors have implemented a blockchain using smart contracts to provide secure,
cost effective and decentralized solution for IoT data trading. However, they
did not consider the trustworthiness of data owner and quality of data. Data
consumers will purchase the data only if they have trust on the data owner.
Previous IoT and blockchain systems do not provide a review system for IoT
data to be traded. Review system is needed which holds the reviews from users
who have used the data, so that other data users can trust the data they are
using.
3 Proposed System Model
In this section, we present an Ethereum blockchain based solution to provide
an automated review system for monetization of IoT data using smart con-
tracts by taking motivation from [28] and [29]. Ethereum blockchain platform
is used because it is open source, and public users can access it easily. It is a
distributed virtual machine with crypto-currency payments and developers are
free to execute their smart contracts. Smart contracts eliminate the third party
risk associated with exchange of payments in IoT monetization. Proposed system
comprises of seven main entities interacting with each other. Main entities of the
system include: device owner having an IoT device, MQTT broker [28], review
system, smart contracts, arbitrator, users and blockchain database. The process
starts with creating a smart contract using solidity language where owner of the
IoT device defines set of rules for the sale of IoT device data. Previous customers
who had accessed the data, had provided reviews about the data used, which are
stored in the blockchain. New customers who want to use this data can interact
with the smart contract to get the reviews and ratings along with exchange of
payments. Review system is available for the users to analyze the quality of data
they want to use.
Metadata, reviews and ratings are stored in the blockchain however, because
of the security and storage purposes data is stored in MQTT broker. Owner
sends data to MQTT broker and making use of review system [29], when any
user request the data, reviews and ratings, smart contract is triggered and unique
token is provided to the user. Smart contract also provide the customer unique
token to the MQTT broker for authentication.
Reputation System for IoT Data Monetization Using Blockchain 177
3.1 System Model Components
The main components of the system model are discussed below. All main
components contain Ethereum addresses for identification of their accounts in
Ethereum blockchain. The interaction between the smart contracts and main
components is as follows:
Owner: At first, IoT device contract is created by the owner of the device.
Owner also specifies the rate for each topic in the contract depending on the
topic information and length. Owner of device data encrypts the data which
is to be stored in MQTT broker and sends private key to the smart contracts.
Customers must have to deposit money to smart contracts before making
transaction. Rates for the topics are decided in agreement with MQTT broker,
because broker has to store them. Successful data access triggers the deposit
function and payment is transferred to the ether wallet of the owner.
IoT Device Contract: All the logic and code of the system is written in the
device contract.
MQTT Broker: MQTT broker holds the data and provides this data to user
on demand. Smart contract provides the customer token to broker so that
broker can authenticate the user.
Review System: The javascript code is connected with the web interface using
the HTML code. Application binary interface (ABI) and bytecode values of
the smart contract are ascribed by the javascript code. Web3 application pro-
gramming interface is the communication medium between smart contracts
and mining server. Functions defined in the javascript makes call to smart
contract functions to execute the transactions. When user interact with the
web interface, values are passed to the javascript functions as transaction
input. Output is displayed when block is mined successfully. When user on
the web page calls the function, transaction occurs and added to the block
through mining. We have used geth interface to run the mining server. Remote
procedure call (RPC) protocol is used by the geth interface.
Web page and web browser are connected using Node.js and web page is
created using HTML and CSS. User will open the web browser and then con-
nected web page will also open. When a contract is deployed, user will choose
the account address and write the review. User who wants to check the exist-
ing review, register review or modify review, will trigger the javascript and
smart contract functions.
User: Users will interact with the smart contracts, broker and review system
and will pay for the data they want to use. User is allowed to subscribe,
review, access data, add review, modify review. User is provided with the
decryption key to decrypt and use data.
Arbitrator: Arbitrator is a trusted entity selected by the owner and system
administration. Arbitrator will get incentive, to act honestly and perform the
two specified tasks from the owner. Owner will provide some privileges to
arbitrator to download the data and provide reviews to handle dispute and
fake reviews (Fig. 1).
178 A. Javaid et al.
4 Smart Contract: Review System for IoT Data
Monetization
The proposed blockchain system focusses on keeping and managing reviews,
ratings, and metadata about the data stored in MQTT broker. These reviews
and ratings are stored in blockchain as a result of the user interactions. After
using data, user writes review and rating before leaving the system depending
upon his experience. In this Section, discussion about the functions and smart
contract is provided. Smart contract code is written, using solidity language.
Add Topic: Firstly, IoTContract is created by the device owner using the con-
structor of the contract. Owner then identifies the default topics and MQTT
broker’s address. Smart contract registers the MQTT broker and topic is
added using the Add Topic function.
Deposit: Interested customers who want to access this data will deposit ethers
using the Deposit function to the contract.
Subscribe: Before, accessing the topic customer has to subscribe the topic
which they want to access.
Is Review Exist: User triggers this function to check whether reviews exist
for the data he/she subscribed.
Heading
Review System for IoT Data Monezaon
MQTT Broker
Owner Side
Ethereum Blockchain
Review System
User
MQTT Connecon MQTT Connecon
Request and Submit
Reviews
Device Owner
IoT Device
JS Services Request Data
Register
Modify
Search
Owner defines rules
Fig. 1. Blockchain based patient driven interoperability.
Reputation System for IoT Data Monetization Using Blockchain 179
Get Reviews: Customer will make request to smart contract for the reviews of
the subscribed topic. Then this function returns the existing review for sub-
scribed data. Data structure which saves the reviews consists “dt contents
which is string value.
Get Ratings: Customer will make request to smart contract for the rating of
the subscribed topic. Then this function returns the existing rating for sub-
scribed data. Data structure which saves the reviews consists “dt ratings
which is a rating value for the string.
Acces s : After, analyzing the review and ratings about data, if user is satis-
fied he/she can access the topic by calling access function. Smart contract
then authenticates, if user has subscription and deposited money. Smart con-
tract will grant access usage time, and unique token to user. The message is
broadcasted and broker also have user unique token for user authentication.
Every user will have a unique token because contract hashes the concatena-
tion of several variables which include: owner and customer address, topic id,
and total number of accesses. User access time for the data depends on the
amount of money in his account. When account ethers are utilized, user is
not able to access data further.
Get Data Content: If user want to get a copy of data to be downloaded on
his system. User will invoke this function and get the data offchain. Smart
contract will send decryption key to user, after user has invoked this function.
User using this decryption key will decrypt the data. To download data, user
must have enough balance in his account.
Update Subscription: When user disconnects or account balance is used, bro-
ker will call this function and customer balance is updated according to the
usage time or downloaded data. Smart contract then sends the money to the
owner account.
Ref und : In case, if there are ethers left in the user’s account and user discon-
nects, then these ethers will be refunded to the user. This function will also
be triggered, when arbitrator finds problem in the user downloaded file.
Set Reviews: User and arbitrator will provide reviews after using and analyz-
ing data and is provided with incentive through incentive mechanism.
5 Results and Discussion
In Ethereum blockchain gas is of fundamental importance because it reflects
the computational complexity of the transactions having number of different
operations. As a result of any request when transaction occurs, smart contracts
are executed. In the network at every node instructions are executed and there is
a cost for every operation which is expressed as number of gas units. Transaction
is the operation which adds something to the blockchain or modifies its state.
Transaction cost or gas cost for a transaction depends on the size and complexity
of the smart contract. Transaction cost is the cost of sending data to blockchain
and is concerned with the transaction’s base cost and contract deployment cost
180 A. Javaid et al.
Access Get Data Set Revie w s Ref u n d
0
10000
20000
30000
40000
50000
60000
70000
Gas Consumption
Ex ec u t ion Ga s
Tra ns act ion Ga s
Fig. 2. Gas consumption for smart contract functions
Get Rat i ngs Up da t e Get De posi t Rev i e w Exi s t
0
5000
10000
15000
20000
Gas Consumption
Ex ec u t ion Ga s
Tra ns act ion Ga s
Fig. 3. Gas consumption for smart contract functions
which are 21000 and 32000 in our case. We can calculate transaction cost of any
transaction, if we know the gas cost using Eq.1[29].
TotalCost
gwei =GasUsed ×GasCost (1)
In Eq. 1gas used, is the amount of gas used by smart contract or any single
operation of smart contract. Gas cost represents the unit price of gas with cryp-
tocurrency unit of gwei.
In smart contract cost test we have set the default gas price as 10gwei. Dif-
ferent operations of the smart contract uses different gas amount. Some smart
contract function costs are given in Table 1. The data trading contract and data
review contract are created once and their costs are $989099 and $90898 respec-
tively. At first, the owner of the IoT device adds the topic to MQTT broker
and cost of this transaction is $0.15103. Then customer will perform initial
ether deposit and subscribe the topic. The cost for these two operations is
$0.03880732 and $0.21558128 respectively. Then customer can access to data
and make request for reviews about the data. These two operations have the
Reputation System for IoT Data Monetization Using Blockchain 181
String Length as Trx Input Value
43 820 37
60,000
80,000
100,000
120,000
140,000
Gas Amount
Fig. 4. Gas consumption for string input length
Fig. 5. Mining time for string input length
Table 1. Cost of functions in Ether and USD.
(Functions) Gas used Cost (Ethers) Cost (USD)
Topic added 92091 0.00092091 0.15103
Deposit 23663 0.00023663 0.03880732
Subscribe 131452 0.00131452 0.21558128
Access 70,000 0.0007 0.1148
Get reviews 24476 0.00024476 0.04014064
Get data content 23115 0.00023115 0.0379086
Refund 18352 0.00018352 0.03009728
Set reviews 59362 0.00059362 0.0973537
cost $0.1148 and $0.04014064 respectively. If user is satisfied about the data
he/she will invoke the get data content to download the data and this operation
have the cost of $0.0379086. If user did not want to download data money will be
refunded to that user when he invokes the refund function. This operation has
the cost of $0.03009728. At the end user will set reviews and this operation has
total consumption of 59362 gas and actual cost in ethers is $0.00059362 ether,
and USD (0.09735368).
182 A. Javaid et al.
Figure 2shows the result for access, get data content, refund and set review
functions. Access function has gained the highest execution and transaction costs
as compared to other smart contract functions. The user when invokes the access
function smart contracts check whether user has the subscription and provides
a unique token to the user. When this user makes other transactions this token
is used for authentication of that specific user. Because of these authentications
involved with these functions, they have more cost and gas consumption. Figure3
shows the result for get deposit, get ratings, is review exist and update subscrip-
tion time functions. Ethereum yellow paper is referred [30] for more informa-
tion regarding to transaction and execution gas. Figure 4shows the relationship
between review string length and gas used to submit that review. Amount of gas
used is increased when string contains more characters. 3200 gas is consumed
when review string length is 32 because gas consumption and review length are
directly proportional to each other. Figure 5shows the mining time for string
input length. Graph shows that there is no relationship between review length
and mining time. At smaller review string length mining time is greater and for
larger review string length mining time is smaller. We can conclude from the
results that mining time is not related to review string length, it depends on
the network conditions and miner’s choice. Customer who has given a detailed
review about the data, cost of the transaction gas will be increased, however
mining time depends upon the network conditions.
6 Conclusion
IoT devices are becoming everyday part of human life and data produced by
these devices is used to improve the modern civilization. In this paper, we have
presented blockchain based review system for trading data of IoT devices. This
system provides confidence to users, that quality of the data is satisfactory.
Trustful automated payments are done using smart contracts by eliminating
third party risk. In order to consider data integrity and security at first only
metadata is provided and reviews cannot be modified because it is a blockchain
based system. All transactions are done through Ethereum smart contracts and
there is log of every transaction in the blockchain. The security and immutability
of the system is ensured using blockchain based system. The smart contracts are
implemented in solidity language and the system is designed using Visual studio
code. User interface is developed using HTML and CSS.
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... Second, pub/sub systems normally assume that the brokers correctly execute matching and routing tasks. In an IoT data marketplace, the data producers and consumers should ideally be able to transfer data in a decentralized manner [11]- [13]. The involvement of any third party in the process should be transparent and require a minimal amount of trust. ...
... Many data monetization models have been studied and conceived depending on different requirements and use cases. In [13], the authors describe a reputation-based monetization system for IoT data using Ethereum blockchain and MQTT. According to their model, a data owner creates a smart contract containing information about the topics and the broker address. ...
... In [37], authors leveraged the IoT with Ethereum's Blockchain to provide a reputation-based monetization system for IoT data, whose quality is ensured for consumers through reviews and ratings. They proposed a publish-subscribe model based on smart contracts, whereby a data owner shares information about the topics and subscribers make deposits, consume data and rate the service quality. ...
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The emergence of blockchain technology and cryptocurrencies opened the possibility for building novel peer-to-peer(P2P) resource allocation and sharing models. However, the trustless nature of these P2P models creates the need for reliable and effective trust and reputation mechanisms to minimize the risk of accessing or interacting with malicious peers. Blockchain technology, which is renowned for ensuring trust in trustless environments, provides us with new mechanisms to overcome the weaknesses of the existing reputation and trust management protocols. This paper proposes BTrust, an innovative decentralized and modular trust management system based on blockchain technology for evaluating trust in large-scale P2P networks. To quantify and assess the trustworthiness of peers and identify malicious peers, BTrust introduces a multi-dimensional trust and reputation model to represent trust and reputation scores in a single value derived from multiple parameters with appropriate weightings. Other contributions of this paper include the combination of recommendation and evidence-based approaches into a single system to provide a reliable and versatile way to compute trust in the network, an optimized trustless bootstrapping process to select trustworthy peers among neighbour peers and an incentive mechanism to encourage truthful feedback. We implement and evaluate the BTrust protocol using simulations and show that BTrust is highly resilient to failures and robust against malicious nodes
... This study is an extension of the work done in [62]. This study is motivated by the studies in [59,60] to present a BC based automated systems maintaining reviews about data to gain the trust of data users. ...
Article
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In this paper, Internet of Things (IoTs) devices are used for sensing the data through which the device owners earn revenue. Interested users can purchase data from IoT device owners, according to their demands. However, users are not confident about the quality of data they are purchasing. Moreover , the users do not rely on the device owner and are not willing to initiate data trading. Currently, data trading systems have many drawbacks, as they involve a third party, security and reputation mechanisms. Therefore, in this paper, IoTs and BlockChain (BC) are integrated to monetize IoT's data and provide trustful data trading. A BC based review system to monetize IoT's data trading is developed through Ethereum smart contracts. The review system encourages the owners to provide authentic data and solves the issues regarding data integrity, fake reviews and conflicts between entities. Reviews and ratings are stored in the BC database for providing a guarantee about the data quality to users. To maintain data integrity, we use an Advanced Encryption Standard (AES)-256 encryption technique to encrypt data. Moreover , an arbitrator entity is responsible to resolve conflicts between data owner and users. The incentive is provided to the users and arbitrators to increase user participation and honesty. Simulations are performed for the validation of our system. We examine the proposed model using three parameters: gas consumption, mining time and encryption time.
... As in [32], [33], the master's reward represents a monetary value of a task, part of which can come from the consumers of mobile/IoT applications that need computation. For example, in the machine learning application for Internet of Vehicles (IoT), the reward can be paid by electric vehicles (EVs) and charging stations which use this application [34], [35]. Both masters and workers remain in the system indefinitelylongeven if the duration of their stay is finite, it is unknown. ...
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This paper proposes a novel framework based on Lagrange coded computing (LCC) for fast and secure offloading of computing tasks in the mobile edge computing (MEC) network. The network is formed by multiple base stations (BSs) acting as masters which offload their computations to edge devices acting as workers. The framework aims to ensure efficient allocation of computing loads and bandwidths to workers, and providing them with proper incentives to finish their tasks by the specified deadlines. Thus, each master must decide on the amounts of allocated load and bandwidth, and a service fee paid to each worker given that: i) other masters, i.e., BSs, can be privately-owned or controlled by different operators, i.e., they do not communicate/coordinate their decisions with the master; ii) workers are heterogeneous non-dedicated edge devices with constrained and nondeterministic computing resources. As such, masters compete for the best workers in a stochastic and partially-observable environment. To describe interactions between masters and workers, we formulate a new stochastic auction model with contingent values of bidders, i.e., masters and contingent payments to auctioneers, i.e., workers. To solve the auction, we represent it as a stochastic Bayesian game and develop machine learning algorithms to improve the auction solution.
... Proposed system model of blockchain is elaborated in the section below: This study is an extension of the work done in [93]. In this section, we present an Ethereum blockchain based solution to provide an automated review system for monetization of IoT data using smart contracts by taking motivation from [91] and [92]. ...
Thesis
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Decision fusion is used to fuse classification results and improve the classification accuracy in order to reduce the consumption of energy and bandwidth demand for data transmission. Decentralized classification fusion problem was the reason to use belief function based decision fusion approach in Wireless Sensor Networks (WSNs). With the consideration of improving the belief function fusion approach, we have proposed four classification techniques namely Enhanced K-Nearest Neighbor (EKNN), Enhanced Extreme Learning Machine (EELM), Enhanced Support Vector Machine (ESVM), and Enhanced Recurrent Extreme Learning Machine (ERELM). In addition, WSNs are fallible to errors and faults because of their different software, hardware failures, and their deployment in diverse fields. These challenges require efficient fault detection methods to be used to detect faults in WSNs in a timely manner. We induced four type of faults: offset fault, gain fault, stuck-at fault, and out of bounds fault and used enhanced classification methods to solve the sensor failure issues. Experimental results show that ERELM has given the first best result for the improvement of belief function fusion approach. The other three proposed techniques ESVM, EELM, and EKNN have provided the second, third, and fourth best results, respectively. Proposed enhanced classifiers are used for fault detection and are evaluated using three performance metrics ,i.e., Detection Accuracy (DA), True Positive Rate (TPR), and Error Rate (ER). In this thesis, the owner of the (Internet of Thing) IoT device can generate revenueby selling IoT device’s data to interested users. However, on the other hand, users do not trust the owner of IoT device for data trading and are not confident about the quality of data. Traditional data trading systems have many limitations, as they involve third party and lack: decentralization, security and reputation mechanisms. Therefore, in this thesis, we have leveraged the IoTs with blockchain technology to provide trustful data trading through automatic review system for monetizing IoT’s data. We have developed blockchain based review system for IoT data monetization using Ethereum smart contracts. Review system encourages the owner to provide authenticated data and solve the issues regarding data integrity, fake reviews and conflict between entities. Data quality is ensured to users through reviews and ratings about the data, stored in blockchain. To maintain the data integrity, we have used Advanced Encryption Standard (AES)-256 encryption technique to encrypt data. All transactions are secure and payments are automated without any human intervention. Arbitrator entity is responsible to resolve problems between data owner and users. Incentive is provided to users and arbitrator in order to maintain the user participation and honesty. Additionally, Ethereum blockchain system requires gas for every transaction. Simulations are performed for the validation of our system. We have examined our model using three parameters: gas consumption, mining time and encryption time. Simulations show that the proposed methods outperform the existing techniques and give better results for belief function and fault detection in datascience WSNs. Additionally, blockchain based data trading in IoT system requires gas for every transaction. We have examined our model using three parameters: gas consumption, mining time and encryption time.
Article
Multi-access Edge Computing relies on the concept of moving part of the cloud resources closer to the users to address limitations of the traditional cloud in order to reduce communication latency and increase security. This makes Multi-access Edge Computing (MEC) suitable for time-critical applications such as autonomous vehicles where they can support connectivity with a safe and more efficient experience. Nevertheless, MECs are generally deployed on constrained devices with limited resources, which may affect the infrastructure reliability and thus its trustworthiness. In this paper, we focus on a trust mechanism based on the interactions between MECs to increase reliability in the context of a service migration scenario, where MEC nodes can decide based on a trust score to which node to migrate their services and user information. Our proposed algorithm leverages ideas of the EigenTrust and RLIoT reputation system and combined with a novel correlation concept and a dynamic distance algorithm. From our simulations, our model shows better results in different scenarios and communication ranges. This work provides the core component of a complete trust management system for MECs and could be applied in various use cases.
Thesis
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Wireless Sensor Networks (WSNs) are vulnerable to faults because of their deployment in unpredictable and hazardous environments. This makes WSN prone to failure such as software, hardware, and communication failures. Due to the sensor’s limited resources and diverse deployment fields, fault detection in WSNs has become a daunting task. To solve this problem, Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Random Forest (RF), and Convolutional Neural Network (CNN) classifiers are used for classification of gain, offset, spike, data loss, out of bounds, and stuck-at faults at the sensor level. Out of six faults, two of them are induced in the datasets, i.e., spike and data loss faults. Likewise, sensors embedded mobile phones are used for the collection of data for some specific task which can effectively save cost and time in Crowd Sensing Network (CSN). The quality of collected data depends on the participation level from all entities of CSN, i.e., service provider, service consumers and data collectors. In comparison with the centralized traditional incentive and reputation mechanisms, we propose a blockchain based incentive and reputation mechanism for CSNs, which mainly consists of three smart contracts. The incentives are used to stimulate the involvement of data collectors and motivate the participants to join the network. Also, the issue of privacy leakage is tackled by using Advanced Encryption Standard (AES128) technique. In addition to that, a reputation system is implemented to tackle the issues like untrustworthiness, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent and reliable data. Furthermore, the results of first scenario are compared on the basis of their Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC), and F1-score. In this thesis, a comparative analysis is performed among the classifiers mentioned previously on real-world datasets and simulations demonstrate that the RF algorithm secures a better rate of fault detection than the rest of the classifiers. Similarly, the second scenario is evaluated through analyzing the gas consumption of all the smart contracts, whereas, the encryption technique is validated through comparing the execution time with base paper technique. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.
Thesis
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Underwater Wireless Sensor Network (UWSN) is quite useful in monitoring different tasks including: from instrument monitoring to the climate recording and from pollution control to the prediction of natural disasters, etc. Recently, different routing protocols have been proposed in UWSN to explore the underwater environment for military and scientific purposes. In this regard, traditional transmission approaches increase the transmission overhead, i.e., packets' collision and congestion, which affect reliable data delivery. In addition, replacement of the sensors' battery in the harsh aquatic environment is also a challenging task. Therefore, to avoid the drastic failure of the network and to prolong the lifespan of the network, efficient routing protocols are needed. However, there are some challenges which affect the performance of the network, i.e., high Energy Consumption (EC), high End to End (E2E) delay, low Packet Delivery Ratio (PDR), minimum network lifetime, high probability of void hole occurrence, limited bandwidth and high bit error rate.~Thus, fast, energy efficient, reliable, collision and interference free routing protocols are required to improve the throughput of a network. Therefore, in this thesis, firstly, two routing protocols are proposed namely: Improved GEogrphic Depth Adjustment Routing (Im-GEDAR) and Co-Improved GEographic Depth Adjustment Routing (Co-Im-GEDAR) to maximize the PDR by minimizing the probability of void hole occurrence (with minimum EC). This enhanced PDR is attained by prohibiting the immutable forwarder nodes selection using three parameters including energy, depth and number of neighbor nodes. Moreover, the probability of void hole occurrence is minimized up to 30\% using fixed nodes deployment at different strategic locations in the network. Secondly, two energy efficient routing protocols namely: Shortest Path-Collision avoidance Based Energy-Efficient Routing (SP-CBE2R) protocol and Improved-Collision avoidance Based Energy-Efficient Routing (Im-CBE2R) protocol are proposed. These routing protocols minimize the probability of void hole occurrence, which minimizes the EC and E2E delay. In addition, both proposed routing protocols enhance the PDR and throughput of the network. In both routing protocols, greedy forwarding is opted to forward the data packets. Moving towards Wireless Sensor Networks (WSNs), during the data transmission, maximum energy is consumed in void hole recovery. In addition, location error and nodes' battery consumption are inevitable. Meanwhile, the loss of data packets and more EC degrade the performance of the network, significantly. Thirdly, three energy conservation routing protocols are implemented. These routing protocols are proposed to maximize the network stability (by avoiding void hole). Fourthly, a Proactive routing Approach with Energy efficient Path Selection (PA-EPS-Case I) is proposed to provide interference free communication. The proposed protocol adaptively changes its communication strategy depending on the type of the network, i.e., dense network, partially dense network and sparse network. Similarly, Bellman-Ford Shortest Path-based Routing (BF-SPR-Three) and Energy-efficient Path-based Void hole and Interference-free Routing (EP-VIR-Three) protocols are proposed for an efficient, reliable, collision and interference free communication. Afterward, the algorithms for the proposed routing protocols are also presented. Feasible regions for proposed routing protocols using linear programming are also computed for optimal EC and maximum network throughput. Moreover, the scalability of the proposed routing protocols is also analyzed by varying the number of nodes. In the end, extensive simulations have been performed to authenticate the performance of the proposed routing protocol. Meanwhile, comparative analysis is performed with state-of-the-art reactive and proactive routing protocols. The comparative analysis clearly shows that proposed routing protocols namely: Im-GEDAR and Co-Im-GEDAR achieved 21\% higher PDR and minimized 7\% EC than GEographic and opportunistic routing with DA based topology control for communication Recovery (GEDAR). The proposed routing protocols outperformed Transmission Adjustment Neighbor-node Approaching Distinct Energy Efficient Mates (TA-NADEEM) and minimized the void hole occurrence up to 30\%. Meanwhile, Im-CBE2R, SP-CBE2R, HA-ECMAE, HA-ECMAE2H and GTBPS-3H outperformed the counterparts. Furthermore, in PA-EPS-Case I, comparative analysis is performed with two cutting edge routing protocols namely: Weighting Depth and Forwarding Area Division Depth Based Routing (WDFAD-DBR) and Cluster-based WDFAD-DBR (C-DBR). Results demonstrate that proposed protocol achieve 12.64\% higher PDR with 20\% decrease in E2E delay than C-DBR. Furthermore, the proposed routing protocol outperformed C-DBR in terms of packet drop ratio up to 14.29\% with an increase of EC up to 30\%. In the end, comparative analysis of BF-SPR-Three and EP-VIR with benchmarks disclose that the proposed routing protocols outperformed in order to provide efficient path selection and to minimize the void hole occurrence.
Thesis
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Underwater Wireless Sensor Networks (UWSNs) are promising and emerging frameworks having a wide range of applications. The underwater sensor deployment is beneficial; however, some factors limit the performance of the network, i.e., less reliability, high end to end delay and maximum energy dissipation. The provisioning of the aforementioned factors has become a challenging task for the research community. In UWSNs, battery consumption is inevitable and has a direct impact on the performance of the network. Most of the time energy dissipates due to the creation of void holes and imbalanced network deployment. In this work, two routing protocols are proposed to avoid the void hole and extra energy dissipation problems due to which lifespan of the network will increase. To show the efficacy of the proposed routing schemes, they are compared with the state of the art protocols. Simulation results show that the proposed schemes outperform their counterpart schemes. By keeping in mind the emerging security issues in sensor networks, we have proposed a blockchain based trust model for sensor networks to enrich the security of the network. Additionally, this model provides security along with data immutability. We have used a private blockchain because it has all the security features that are necessary for a private sensor network. Moreover, private blockchain cannot be accessed by using the Internet. In the proposed trust model, the Proof of Authority (PoA) consensus algorithm is used due to its low computational power requirement. In PoA consensus mechanism, a group of the validator is selected for adding and maintaining blocks. Moreover, smart contracts are used to validate and transfer cryptocurrency to service providers. In the end, transaction and execution costs are also calculated for each function to testify the network suitability.
Thesis
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In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to involvement of TTP, such systems lack trust, transparency, security and immutability. To over come these issues, this thesis proposed a blockchain based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). A meta data is uploaded to IPFS server by owner and then divided into n secret shares. The proposed scheme achieves security and access control by executing the access roles written in smart contract by owner. Users are first authenticated through RSA signatures and then submit the requested amount as a price of digital content. After the successful delivery of data, a user is encouraged to register reviews about data by announcing customer incentives. In this way, maximum reviews are submitted against every file. In this scenario, decentralized storage, Ethereum blockchain, encryption and decryption schemes and incentive mechanism are combined. To implement the proposed scenario, smart contracts are written in solidity and deployed on local Ethereum test network. The proposed scheme achieves transparency, security, access control, authenticity of owner and quality of data. In simulation results, an analysis is performed on gas consumption and actual cost required in terms of USD, so that a good price estimate can be done while deploying the implemented scenario in real setup. Moreover, computational time for different encryption schemes are plotted to represent the performance of implemented scheme, which is shamir secret sharing (SSS). Results show that SSS shows least computational time as compared to advanced encryption standard (AES) 128 and 256.
Thesis
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Decision fusion is used to fuse classification results and improve the classification accuracy in order to reduce the consumption of energy and bandwidth demand for data transmission. Decentralized classification fusion problem was the reason to use belief function based decision fusion approach in Wireless Sensor Networks (WSNs). With the consideration of improving the belief function fusion approach, we have proposed four classification techniques namely Enhanced K-Nearest Neighbor (EKNN), Enhanced Extreme Learning Machine (EELM), Enhanced Support Vector Machine (ESVM), and Enhanced Recurrent Extreme Learning Machine (ERELM). In addition, WSNs are fallible to errors and faults because of their different software, hardware failures, and their deployment in diverse fields. These challenges require efficient fault detection methods to be used to detect faults in WSNs in a timely manner. We induced four type of faults: offset fault, gain fault, stuck-at fault, and out of bounds fault and used enhanced classification methods to solve the sensor failure issues. Experimental results show that ERELM has given the first best result for the improvement of belief function fusion approach. The other three proposed techniques ESVM, EELM, and EKNN have provided the second, third, and fourth best results, respectively. Proposed enhanced classifiers are used for fault detection and are evaluated using three performance metrics ,i.e., Detection Accuracy (DA), True Positive Rate (TPR), and Error Rate (ER). In this thesis, the owner of the (Internet of Thing) IoT device can generate revenueby selling IoT device’s data to interested users. However, on the other hand, users do not trust the owner of IoT device for data trading and are not confident about the quality of data. Traditional data trading systems have many limitations, as they involve third party and lack: decentralization, security and reputation mechanisms. Therefore, in this thesis, we have leveraged the IoTs with blockchain technology to provide trustful data trading through automatic review system for monetizing IoT’s data. We have developed blockchain based review system for IoT data monetization using Ethereum smart contracts. Review system encourages the owner to provide authenticated data and solve the issues regarding data integrity, fake reviews and conflict between entities. Data quality is ensured to users through reviews and ratings about the data, stored in blockchain. To maintain the data integrity, we have used Advanced Encryption Standard (AES)-256 encryption technique to encrypt data. All transactions are secure and payments are automated without any human intervention. Arbitrator entity is responsible to resolve problems between data owner and users. Incentive is provided to users and arbitrator in order to maintain the user participation and honesty. Additionally, Ethereum blockchain system requires gas for every transaction. Simulations are performed for the validation of our system. We have examined our model using three parameters: gas consumption, mining time and encryption time. Simulations show that the proposed methods outperform the existing techniques and give better results for belief function and fault detection in datascience WSNs. Additionally, blockchain based data trading in IoT system requires gas for every transaction. We have examined our model using three parameters: gas consumption, mining time and encryption time.
Thesis
Full-text available
Wireless Sensor Networks (WSNs) are vulnerable to faults because of their deployment in unpredictable and hazardous environments. This makes WSN prone to failure such as software, hardware, and communication failures. Due to the sensor’s limited resources and diverse deployment fields, fault detection in WSNs has become a daunting task. To solve this problem, Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Random Forest (RF), and Convolutional Neural Network (CNN) classifiers are used for classification of gain, offset, spike, data loss, out of bounds, and stuck-at faults at the sensor level. Out of six faults, two of them are induced in the datasets, i.e., spike and data loss faults. Likewise, sensors embedded mobile phones are used for the collection of data for some specific task which can effectively save cost and time in Crowd Sensing Network (CSN). The quality of collected data depends on the participation level from all entities of CSN, i.e., service provider, service consumers and data collectors. In comparison with the centralized traditional incentive and reputation mechanisms, we propose a blockchain based incentive and reputation mechanism for CSNs, which mainly consists of three smart contracts. The incentives are used to stimulate the involvement of data collectors and motivate the participants to join the network. Also, the issue of privacy leakage is tackled by using Advanced Encryption Standard (AES128) technique. In addition to that, a reputation system is implemented to tackle the issues like untrustworthiness, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent and reliable data. Furthermore, the results of first scenario are compared on the basis of their Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC), and F1-score. In this thesis, a comparative analysis is performed among the classifiers mentioned previously on real-world datasets and simulations demonstrate that the RF algorithm secures a better rate of fault detection than the rest of the classifiers. Similarly, the second scenario is evaluated through analyzing the gas consumption of all the smart contracts, whereas, the encryption technique is validated through comparing the execution time with base paper technique. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.
Conference Paper
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Internet of Things (IoTs) is widely growing domain of the modern era. With the advancement in technologies, the use of IoTs devices also increases. However, security risks regarding service provisioning and data sharing also increases. There are many existing security approaches, although these approaches are not suitable for IoT devices due to their limited storage and limited computation resources. These secure approaches also require a specific hardware. With the invention of blockchain technologies, many security risks are eliminated. With the help of blockchain, data sharing mechanism is also possible. In this paper, we proposed a novel secure service providing mechanism for IoTs by using blockchain. We introduced cloud nodes for maintaining the validity states of edge service providers. The rating and cryptocurrency is given to edge servers. Given rating and incentive is stored in cloud node and updated with respect to time. The smart contract is proposed to check the validity state of the edge server as well as compare and verify the service provided by edge servers. In our proposed system we perform service authentication at cloud layer as well as edge server layer. Moreover, by using Proof of Authority (PoA) consensus mechanism overall performance of our proposed system also enhanced.By experimental analysis it is shown, our proposed model is suitable for resource constrained devices.