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A Blockchain based Incentive Mechanism for Crowd Sensing Network

Abstract and Figures

Crowd Sensing Network (CSN) uses sensor embedded mobile phones for the collection of data for some specific task which can effectively save cost and time. 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 mechanisms devised for CSN, we have proposed a decentralized system model where incentives are used to stimulate the involvement among data collectors and motivate the participants to join the network. Moreover, the issue of privacy leakage is tackled by using AES128 technique. Furthermore , the system 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 methods.
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A Blockchain Based Incentive Mechanism
for Crowd Sensing Network
Zainib Noshad, Atia Javaid, Maheen Zahid, Ishtiaq Ali,
Raja Jalees ul Hussen Khan, and Nadeem Javaid(B
)
COMSATS University Islamabad, Islamabad, Pakistan
zainabnoshad@yahoo.com, atiajavaid477@gmail.com,
maheen.zahid2017@gmail.com, ishiali503@gmail.com,
jalees106@gmail.com, nadeemjavaidqau@gmail.com
Abstract. Crowd Sensing Network (CSN) uses sensor embedded mobile
phones for the collection of data for some specific task which can effec-
tively save cost and time. The quality of collected data depends on the
participation level from all entities of CSN, i.e., service provider, ser-
vice consumers and data collectors. In comparison with the centralized
traditional incentive mechanisms devised for CSN, we have proposed a
decentralized system model where incentives are used to stimulate the
involvement among data collectors and motivate the participants to join
the network. Moreover, the issue of privacy leakage is tackled by using
AES128 technique. Furthermore, the system is evaluated through analyz-
ing the gas consumption of all the smart contracts, whereas, the encryp-
tion technique is validated through comparing the execution time with
base paper methods.
Keywords: Crowd Sensing Network ·Blockchain ·Encryption ·
Service consumer ·Gas consumption
1 Introduction
With the proliferation of emerging technology naming smartphones, smart-
watches, wearable devices, and smart-glasses with sensors embedded has given
the opportunity to sense raw data from the environment at a very high rate.
It is apparently the new trend in the market [1]. This advent in technology has
enabled many applications to collect data from a crowd through Mobile Crowd
Sensing Network (MCSN). The dawn of this sensing paradigm works through
outsourcing the acquiring of sensory data to a crowd of volunteering users, called
data collectors or crowd workers [2]. Their aim is to complete a task which is
broadcast-ed by any service provider or a requester and in return, they are com-
pensated for their efforts and spent resources with some fair share. This approach
is adapted from the traditional mechanism which is referred as a win win situ-
ation. It provides the opportunity to both the parties i.e., client and server, a
chance to collaborate. All the involved participants work together for a conflict
and comes up with a mutually beneficial solution.
c
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L. Barolli et al. (Eds.): 3PGCIC 2019, LNNS 96, pp. 568–578, 2020.
https://doi.org/10.1007/978-3-030-33509-0_53
A Blockchain Based Incentive Mechanism for Crowd Sensing Network 569
From the commercial point of view, many researchers have exploited this
trend to gain the maximum benefit from Crowd Sensing Network (CSN). There-
fore, service oriented approach is another perspective of a CSN that has been
addressed by multiple authors [3]. Moving on, another entity [4] i.e., service con-
sumers is brought into the scenario for sole purpose of utilizing the data that
has been acquired by the data collectors. Otherwise, without the third entity,
there is no point of collecting this massive amount of data and letting the crowd
workers to spend their efforts, time, and resources for no good reason at all. For
businesses to improve, drive towards success, and achieve organizational goals,
there is a dire need [5] to analyze and process data which is not generated by
themselves. Rather, the raw data purchased from service providers of a CSN
where you can fill up gaps and generate leads.
Although CSN is serving the purpose of sensing data at a very cheaper rate
and providing a handful of advantages however, there are multiple issues which
arise with such kind of mechanisms and the major issue that has been addressed
by multiple researchers, is attaining the data quality [4] and engaging skilled
users. Since smart phone users are participating and spending their resources like
battery, storage and computation power, with that they are also exposing them-
selves with the potential privacy leakage. There should be some kind of reward
or incentive mechanism to compensate for their privacy leakage and resource
consumption. Therefore, a truthful and secure incentive mechanism has an utter
importance for CSNs. Furthermore, the sensing domain is divided into two cate-
gories i.e., opportunistic (in-volunteer) and participatory (volunteer) [68]. This
categorization help organizations to decide their task allocation, resources, and
the measures to take regarding the challenges mentioned above.
In order to encounter the issues faced by a CSN, many incentive mechanisms
have been proposed such as socially aware incentive mechanism for MCSN [9],
Quality and Usability Of INformation (QUOIN) [10] and many more monetary
approaches. However, these mechanisms posses a central authority [11]which
can lead to a single point failure. With the advent of technology and emerging
trends in application centric approaches, blockchain has proved to be the opti-
mal solution for the problems faced by CSNs. The Fig. 1is taken from [12]to
elaborate the structure of blockchain.
In this paper, we have proposed a blockchain based data sharing mechanism
for CSN with two communication paradigms. In addition to that, AES128, an
encryption technique is also applied for preserving privacy of data collectors.
Smart contracts are used for communication purposes and enforcing the defined
criteria autonomously.
The paper is further divided as follows; Sect. 2describes the motivation
and problem statement. Whereas, Sect. 3provides the related work. Section 4
presents the proposed system model and explanation. Moving on, Sect. 5has the
details of experimental results while the paper is concluded in Sect. 6.
570 Z. Noshad et al.
Fig. 1. Blockchain structure
2 Motivation and Problem Statement
CSN use sensors carried by mobile phones to collect sensed data and can effec-
tively save cost. In [13], authors have proposed a blockchain based privacy pro-
tection and virtual credit incentive mechanism for CSN. They have targeted
two issues of CSN, i.e., privacy leakage and low user involvement respectively.
Former issue is resolved by using encryption technique i.e., Affine Cipher. The
latter issue is resolved through tackling the issue of privacy leakage. However,
the Affine Cipher used for encryption is rather a less secure substitution cipher
and it is vulnerable to all the attacks of cipher class. Also, they have not used
smart contracts as a secure communication platform rather, encryption tech-
niques are applied separately when data is submitted by the sensors and Merkle
Tree (MT) root is used for data validation. Furthermore, the communication
paradigm between service provider and consumers is not considered.
Taking the motivation from the paper mentioned above and to tackle the
following limitations, we have proposed a system which is further divided into
two communication paradigms;
communication between service provider and data collectors,
communication between service provider and service consumers.
In the proposed scenario, where AES128 is used for encryption and a smart
contract is initiated by the service provider who acts as a requester in the net-
work for a specific task. Encryption is used to make sure that worker’s identity
is kept safe while submitting their committed task and incentive is distributed
among all the data collectors/workers immediately to stimulate user participa-
tion. Moreover, another smart contract is deployed for service consumers that
can request service and get a response from the service provider. Following that,
payment will be made to the service provider for sending the requested data.
A Blockchain Based Incentive Mechanism for Crowd Sensing Network 571
3 Related Work
CSNs are where a large group of individuals have mobile devices consisting of
sensors. These devices are capable of sensing and computing shared data which
can be used to extract useful information for measuring, mapping, analyzing, or
estimating any process of common interest. Most mobile devices (smart phones,
tablet computers, wearables) can sense ambient light, noise (through the micro-
phone), location (through the GPS), movement (through the accelerometer),
and much more. In [14], authors have proposed a blockchain based incentive
mechanism for CSNs to increase the participation rate of the active users with
preserving their privacy. This mechanism mainly considers the truthfulness of
the mechanism by introducing a cryptocurrency as a secure reward to com-
pensate the participant. In this way, high quality users will be rewarded with
cryptocurrency that is built on blockchain and is recorded in transaction blocks.
The server will publish a sensing task with deposit, users will upload sensing
data, miners will verify the quality of data and transactions and in the end,
server will distribute the reward to all the participants. Whereas in [13]the
authors have proposed a blockchain based location privacy preserving incentive
mechanism in CSNs. The emphasis of this mechanism is to protect the pro-
vided information and sanctions reward for participation which will increase the
involvement of the users. The experiments are conducted in a campus environ-
ment with a total of 10 nodes (participants) and the results obtained are effective
in terms of encouraging user participation. In [12] the authors have introduced
blockchain based crowd sensing system where the miners and sensing-task work-
ers are rewarded through some pre-defined incentives which provides authentic
anonymity and system robustness. In [10], the authors have proposed QUOIN
which concurrently provides quality and usability of information for crowd sens-
ing applications. Stackelberg game model is applied in QUOIN to ensure that
every participant attains a sufficient level of financial gain. The authors have
evaluated this mechanism through a case study and the results show their effi-
ciency and effectiveness for the purpose of stimulating participation rate.
In [15], the authors have suggested an incentive mechanism which is based on
contract theory for mobile CSNs. A trust scheme has been introduced between
crowd sensing platform and mobile users which is based on direct and indirect
trust. Following that, an optimal contract is laid out that is based on incen-
tive scheme to encourage mobile users for participation in CSN. This contract,
together with maximizing the platform’s profitability, satisfies individual incen-
tive compatibility. The authors in [9] have proposed a novel technique, called
the social incentive mechanism where the social friends of the participants are
incentivized which strengthens the social ties among participators to promote
global cooperation. The incentive allotment depends on the participants social
circle therefore they get motivated to impact their friend’s behavior in order to
secure an increased payback. This kind of approach is useful where the quality of
the sensed data depends on the interdependent relationship among participants
or users.
In [16], a case study has been conducted by the authors which involves Partic-
ipAct platform and ParticipAct living lab. This experiment has been conducted
572 Z. Noshad et al.
in University of Bolonga that involves 170 students, for a whole year, invested
in multiple crowd sensing campaigns who can access the smartphone sensors
passively and also provoke for user active collaboration. This paper presents the
outline of ParticipAct’s design, architecture, its feature, and report with the
quantitative results.
Cryptographic techniques play an important role when the information is
exchanged between users. In [17] and [18], the authors have compared encryp-
tion and decryption techniques for multimedia and guessing attacks prevention
such as RSA, Blowfish, AES, 3DES, and many more. The analysis is based on
comparing the encryption, decryption time, memory used, avalanche effect, and
entropy. Likewise in 2016, the authors of [19] designed a platform to promote
CSN not just as a service but as a contribution for the society too. However,
where there are people involved, there is always a risk of privacy leakage. This
issue causes a huge set back to CSNs as they are purely based on user’s vol-
unteering or in-volunteering involvement towards the network. To tackle this
issue, the authors used AES256 encryption technique for preserving the privacy
of users that attracted skillful participants for the platform. Moreover, in [13],
the authors have used Affine Cipher for the same issues mentioned above.
Furthermore, in conventional CSNs, there are many other techniques which
are used for preserving privacy. In [20], the authors have used Dynamic Trust
Relationships Aware Data Privacy Protection (DTRPP) mechanism for achiev-
ing privacy. The system is devised to combine public key with trust management
mechanism. The extensive simulation analysis showed that the system performed
better in-terms of average delay, delivery and loading rate when measured against
traditional systems. Likewise, [21], authors have proposed a mechanism to pro-
tect the location of the mobile users through combining k-anonymity and differ-
ential privacy-preserving.
4SystemModel
The proposed system is an incentive mechanism which is based on blockchain
for CSN. In the suggested scenario, there are three entities which are partici-
pating in the CSN, i.e., the service provider, the service consumers, and data
collectors. The terms requester and service provider will be used alternatively
throughout the paper likewise, the terms data collector and worker will be used
interchangeably. The roles of the entities are defined in Table1.
Table 1. Roles of entities of a CSN
Participants Roles
Service provider Broadcasting a task in the network and provide services to consumers
Service consumers Requesting data and utilizing that information which is acquired by a
data collector
Data collectors Measuring the required data about a subject of interest using mobiles
which is stated in the task broadcast-ed by service provider
A Blockchain Based Incentive Mechanism for Crowd Sensing Network 573
Fig. 2. System model of CSN
After entering the system, service provider will initiate a smart contract by
setting the demands of task for sensing data and stores a definite amount of
deposit for establishing an incentive for the worker. Then, the task is published
in the network. Following that, worker’s identity is kept safe through encryption
AES128 which tackles with the issue of privacy leakage.
Once the workers submit their sensed data and it has been validated by the
miners, they will get the incentive immediately which is reserved in the smart
contract protocol. This helps in building the reputation of the system and boosts
up the enthusiasm of both miners and data collectors because to the instant
receival of incentives. In addition to that, since the rules are already established
in the smart contract initiated by service provider, both data collector and miners
can put their trust in service provider as a reliable administrator. Also, the
posting of gathered data by data collectors will cost them a definite aggregate of
gas in smart contract. It is the equivalent to security deposits by data collectors
before participating in the CSN, this process will help in preventing various
attacks. The motivation for proposed system model as shown in Fig. 2is taken
from [13] and [22].
574 Z. Noshad et al.
Service consumers separately interacts with service provider as shown in
Fig. 2. They will send a request to the provider for that specific service. Service
provider will initiate a contract with specified payment for the data requested
and the response is sent to the server. In addition to that, the payment is sent
to the server in exchange of the requested data. Since the interaction between
service provider and consumer involves smart contract, it gets the job done effec-
tively. It removes the probability of any error that may transpire in traditional
contracts and agreements.
5 Experimental Results
To assess the performance of a blockchain based data sharing mechanism for
CSN with two communication paradigms, we have developed our application
in VS code with the help of ganache, truffle framework and metamask. The
language used is solidity. Ganache is used for deploying our smart contracts
whereas metamask is used as a bridge that permits us to run Ethereum decen-
tralized applications in the browser without executing a full Ethereum node. The
specifications of the system are as follows; it is an Intel core i3, 7th generation,
with 4 GB RAM, and 500 GB of storage. The performance parameters for the
proposed scenario are as follow;
gas consumption of smart contracts, and
execution time of different cryptographic techniques.
5.1 Gas Consumption Analysis
The detail of gas consumption of each contract with functions are described
below. In Fig. 3, the gas consumption of data collectors are plotted against each
function executed by the smart contract. There are total three functions that a
server performs, i.e., initTask(),Abort() and Checkdata(). Service provider acts
Cre a t e Ta sk Abor t Task Che ck Da t a
0
200000
400000
600000
800000
Gas consumption (gas)
Transaction cost
Ex ec u t i on c os t
Fig. 3. Gas consumption of requester
A Blockchain Based Incentive Mechanism for Crowd Sensing Network 575
as a requester in CSN as an initiator of task. Therefore, it has the authority
to write the digital contract and commence the task. It is evident from the
graphs that transaction gas is more than the execution gas of all the functions
executed in both smart contracts. Transaction gas is basically the gas used when
the transactions are validated and stored in the blockchain which requires more
computational power then execution gas, which is the cost for execution of each
line of code. The function initTask() is used by the service provider to create
a task and decide the incentive for each processed sensing data. The service
provider saves a definite range of deposits and define the reward with number of
data required in the criteria before creating the task. This is why, it has consumed
the greatest amount of gas as compared to the rest of functions whereas, Abort()
is called when service provider believes that they have collected sufficient amount
of data through viewing the number of data in Checkdata(). The deposits will
be returned to the requester by this function.
View Task Com m i t Task
0
25000
50000
75000
100000
125000
150000
175000
Gas consumption (gas)
Tra nsa cti on co st
Ex ec u t i on c os t
Fig. 4. Gas consumption of worker
Figure 4is demonstrating the consumption of gas of two functions which are
executed by the data collectors. In CSN, the data collectors have the choice
to select a task to perform they are interested in. However in our scenario, we
have assumed that the task, worker is interested in, is the task broadcast-ed by
our requester. The functions performed by the worker are following; getTask()
and commitTask(). The former function is used to view the information required
by the service provider with defined criteria which is the reward and number
of data. It is necessary for workers to first view the task otherwise if the data
entered is incorrect, not a single penny will be given as an incentive to the worker.
The transaction and execution gas of view task is less as compared to the latter
function. Because, the commitTask() is called to submit the collected data which
requires more computational energy as compared to viewing the task.
Figure 5represents the gas consumed by the service consumer. This smart
contract, when initiated performs the following functions, i.e., ServiceRequest(),
ServiceResponse(),andPayment(). The transaction cost of request and response
is almost the same while Payment() requires more transaction and execution
576 Z. Noshad et al.
Ser v ic e Req ue st Se rv ic e Res po n se Pay m en t
0
10000
20000
30000
40000
50000
60000
Gas consumption (gas)
Transaction cost
Execution cost
Fig. 5. Gas consumption of service consumer
cost. The reason behind increase in gas consumption is because some amount
is edited from the smart contract’s account while added in service provider’s
account. This transaction is then added in the blockchain.
5.2 Execution Time Analysis
Figure 6shows the comparison of cryptographic techniques on the basis of exe-
cution time of encryption and decryption. Encryption time is the time required
to convert the normal text into cipher text whereas, decryption time is the
time required for converting the cipher text into normal text. Both of them are
desired to be less for the system to be quick and more responsive. Also, both
have an affect on the performance of the system due to which four techniques
are compared i.e., Affine Cipher, AES128, AES256, and 3DES respectively for
the proposed scenario. Affine Cipher is used in [2] for preserving the privacy of
the user however, it belongs to the class of classical mono-alphabetic substitu-
tion techniques which can be easily comprehended by solving a simultaneous
equation. In addition to that, it is vulnerable to all the cipher attacks and it is
Fig. 6. Execution time comparison for Affine Cipher, AES128, AES256, and 3DES
A Blockchain Based Incentive Mechanism for Crowd Sensing Network 577
not considered to be a strong secure method for encryption as compared to the
modern symmetric key block cipher techniques. From the literature review [18
20], we implemented three more encryption techniques and found that AES256
and AES128 performs with less execution time as compared to 3DES. Although,
AES256 is more secure as compared to AES128 because of 256 bit key size
and increased number of rounds however, there is the trade-off of execution
time. Therefore, in order to have a fast and secure algorithm for encryption and
decryption for the system to be more responsive, we deployed AES128 instead
of AES256 which is suitable for the proposed scenario.
6 Conclusion
With the advent of technology, blockchain has emerged to be the optimal solution
for providing a decentralized environment to applications and helping out in
terms of security, privacy and single point of failure. In this paper, a blockchain
based incentive mechanism is devised for CSN which aims to motivate data
collectors and attract highly skilled users. Encryption is used to preserve the
privacy of participants and a communication platform i.e., smart contract is
provided for secure reporting. The objective of the proposed model is to cater the
needs of all the entities of CSN in a decentralized manner which in return achieve
integral and reliable data, high participation rate, and secure communication.
The system is evaluated by analyzing gas consumption of all smart contracts
deployed whereas, the encryption technique is validated through examining the
execution time and comparing it with other techniques.
In future, the objective is to calculate trustworthiness of the data contributed
by the users. Through comparing the user’s trust attitudes and applying non-
parametric statistic methods, we can examine the subjectivity in contributed
data for the proposed scenario.
References
1. He, D., Chan, S., Guizani, M.: User privacy and data trustworthiness in mobile
crowd sensing. IEEE Wirel. Commun. 22(1), 28–34 (2015)
2. Jin, H., Su, L., Xiao, H., Nahrstedt, K.: Incentive mechanism for privacy-aware data
aggregation in mobile crowd sensing systems. IEEE/ACM Trans. Netw. (TON)
26(5), 2019–2032 (2018)
3. Merlino, G., Arkoulis, S., Distefano, S., Papagianni, C., Puliafito, A., Papavassiliou,
S.: Mobile crowdsensing as a service: a platform for applications on top of sensing
clouds. Future Gener. Comput. Syst. 56, 623–639 (2016)
4. Nie, J., Luo, J., Xiong, Z., Niyato, D., Wang, P.: A stackelberg game approach
toward socially-aware incentive mechanisms for mobile crowdsensing. IEEE Trans.
Wirel. Commun. 18(1), 724–738 (2019)
5. Gisdakis, S., Giannetsos, T., Papadimitratos, P.: Security, privacy, and incentive
provision for mobile crowd sensing systems. IEEE Internet Things J. 3(5), 839–853
(2016)
578 Z. Noshad et al.
6. Luo, C., Liu, X., Xue, W., Shen, Y., Li, J., Hu, W., Liu, A.X.: Predictable privacy-
preserving mobile crowd sensing: a tale of two roles. IEEE/ACM Trans. Netw.
(TON) 27(1), 361–374 (2019)
7. Ahmad, W., Wang, S., Ullah, A., Yasir Shabir, M.: Reputation-aware recruitment
and credible reporting for platform utility in mobile crowd sensing with smart
devices in IoT. Sensors 18(10), 3305 (2018)
8. Lane, N.D., Eisenman, S.B., Musolesi, M., Miluzzo, E., Campbell, A.T.: Urban
sensing systems: opportunistic or participatory? In: Proceedings of the 9th Work-
shop on Mobile Computing Systems and Applications, Napa Valley, CA, USA, pp.
11–16 (February 2008)
9. Yang, G., He, S., Shi, Z., Chen, J.: Promoting cooperation by the social incentive
mechanism in mobile crowdsensing. IEEE Commun. Mag. 55(3), 86–92 (2017)
10. Ota, K., Dong, M., Gui, J., Liu, A.: QUOIN: incentive mechanisms for crowd
sensing networks. IEEE Netw. 32(2), 114–119 (2018)
11. Jaimes, L.G., Vergara-Laurens, I.J., Raij, A.: A survey of incentive techniques for
mobile crowd sensing. IEEE Internet Things J. 2, 370–380 (2015)
12. Huang, J., Kong, L., Kong, L., Liu, Z., Liu, Z., Chen, G.: Blockchain-based crowd-
sensing System. In: 2018 1st IEEE International Conference on Hot Information-
Centric Networking (HotICN), pp. 234–235. IEEE (August 2018)
13. Jia, B., Zhou, T., Li, W., Liu, Z., Zhang, J.: A blockchain-based location privacy
protection incentive mechanism in crowd sensing networks. Sensors 18(11), 3894
(2018)
14. Park, J.S., Youn, T.Y., Kim, H.B., Rhee, K.H., Shin, S.U.: Smart contract-based
review system for an IoT data marketplace. Sensors 18(10), 3577 (2018)
15. Wang, J., Li, M., He, Y., Li, H., Xiao, K., Wang, C.: A blockchain based privacy-
preserving incentive mechanism in crowdsensing applications. IEEE Access 6,
17545–17556 (2018)
16. Dai, M., Su, Z., Wang, Y., Xu, Q.: Contract Theory Based Incentive Scheme for
Mobile Crowd Sensing Networks. In: 2018 International Conference on Selected
Topics in Mobile and Wireless Networking (MoWNeT), pp. 1–5. IEEE (June 2018)
17. Cardone, G., Corradi, A., Foschini, L., Ianniello, R.: Participact: a large-scale
crowdsensing platform. IEEE Trans. Emerg. Topics Comput. 4(1), 21–32 (2016)
18. Ahamad, M.M., Abdullah, M.I.: Comparison of encryption algorithms for multi-
media. Rajshahi Univ. J. Sci. Eng. 44, 131–139 (2016)
19. Wahid, M.N.A., Ali, A., Esparham, B., Marwan, M.: A comparison of crypto-
graphic algorithms: DES, 3DES, AES, RSA and blowfish for guessing attacks pre-
vention (2018)
20. Mottur, P.A., Whittaker, N.R.: Vizsafe: the decentralized crowdsourcing safety
network. In: 2018 IEEE International Smart Cities Conference (ISC2), pp. 1–6
(September 2018)
21. Wu, D., Si, S., Wu, S., Wang, R.: Dynamic trust relationships aware data pri-
vacy protection in mobile crowd-sensing. IEEE Internet Things J. 5(4), 2958–2970
(2017)
22. Chi, Z., Wang, Y., Huang, Y., Tong, X.: The novel location privacy-preserving
CKD for mobile crowdsourcing systems. IEEE Access 6, 5678–5687 (2017)
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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
Full-text available
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
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
Full-text available
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
Full-text available
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
Full-text available
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
Full-text available
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.