An Incentive and Reputation Mechanism Based on Blockchain for Crowd Sensing Network

Abstract

Nowadays, sensors inserted in mobile applications are used for gathering data for an explicit assignment that can effectively save cost and time in crowd sensing networks (CSNs). The true value and essence of gathered statistics depend on the participation level from all the members of a CSN, i.e., service providers, data collectors, and service consumers. In comparison with the centralized conventional mechanisms that are susceptible to privacy invasion, attacks, and manipulation, this article proposes a decentralized incentive and reputation mechanism for CSN. The monetary rewards are used to motivate the data collectors and to encourage the participants to take part in the network activities. Whereas the issue of privacy leakage is dealt with using Advanced Encryption Standard (AES128) technique. Additionally, a reputation system is implemented to tackle issues like data integrity, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent, and reliable data. Furthermore, simulations are performed for analyzing the gas consumed by smart contracts. Similarly, the encryption technique is ratified by comparing its execution time with other techniques that are previously used in literature. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

The rapid expansion and revolutionary developments in technology, such as smartwatches, smart glasses, wearable devices, and smartphones that have embedded sensors, provide data collection opportunities to organizations. These opportunities give access to multiple organizations and companies to raw data that can be sensed from a particular environment. This forthcoming process is the new direction in the market [1]. The progression in the technology has given access to so many applications to gather data through the mobile crowd sensing network (MCSN). This mechanism operates by contracting out the sensing task to a voluntary crowd known as workers or data collectors [2]. The key objective of these workers is to finish their designated tasks for which they are compensated through a variety of incentives. The type of incentive depends on the service provider also known as task requester. The inspiration for this approach is taken from a conventional process known as a win-win situation. Here, all the parties involved, i.e., server and a client, are provided a chance to cooperate and work together with each other for cultivating a mutually beneficial resolution.

The smart contracts are incorporated as a secure transmission medium by imposing the defined criteria autonomously. The purpose of incorporating the reputation system in the proposed scenario is to preserve the integrity and reliability of data for promoting trust among the service consumers. The acronyms are listed in Table 1.

Table 1

List of acronyms.

Acronyms	Full form
AES	Advanced encryption standard
CSN	Crowd sensing network
DES	Data encryption standard
DTRPP	Dynamic trust relationships aware data privacy protection
GPS	Global positioning system
IoT	Internet of things
MCSN	Mobile crowd sensing network
MT	Merkle tree
QUOIN	Quality and usability of information
RAF	Review automatic filtering
RSA	Rivest-Shamir-Adleman

From a commercial perspective, many scholars have explored this new trend to attain maximum advantage from crowd sensing network (CSN). Thus, a service-based approach is another point of view that has been researched by authors [3]. Furthermore, a new entity [4], i.e., the service consumer is led in this picture for gaining profit by the sensed data acquired by workers. Otherwise, if the third entity is not considered, the massive amount of data collected goes into waste. Additionally, the significant time spent, efforts, and resources on this procedure also go in vain. To improve, achieve organizational goals, and drive towards success, it is a necessity for businesses to process and analyze the data [5]. Therefore, the raw data acquired by the data collectors are sold that can help out organizations to fill up the loopholes and produce radical and dynamic leads for projects.

The multifarious issues have become apparent with the CSN based platforms, which have provided an effective mechanism for sensing data at a cheaper rate with many advantages. Numerous researchers have indicated that CSN has become a constructive tool for obtaining quality data, which was previously difficult to obtain [4]. An incentive mechanism has been devised, which compensates the workers for spending their resources and collaterals. Moreover, CSN platforms’ strength is ensuring trustworthiness in their efficient requisite services. For optimum service utilization, the customer of such a facility must know the kind of data he wants to acquire, and then the transaction should be made. Furthermore, CSN is branched into two more domains, i.e., in-volunteer (opportunistic) and volunteer (participatory) [6–8]. Such category arrangements assist beneficiaries in their decision for resource allocation, tasks, and resources to be utilized.

Several incentives have been devised, including socially aware incentive mechanisms for the MCSN [9] to resolve issues faced by CSN, like quality and information usability (QUOIN) [10], and numerous compensation (monetary) approaches. A central authority is established for these mechanisms [11]; thus, a single point of failure may occur. Moreover, the participation of malicious users can expose these systems to Sybil and Distributed Denial of Service (DDoS) attacks. The blockchain has shown its tendency to be the best claim for centrist approaches of the CSN technology. Figure 1 is taken from [11]. Blockchain has also a lot of applications in energy trading in smart grids, like [12–14], in food supply management, like [15], and in data sharing, like [16].

[figure omitted; refer to PDF]

In this article, we have proposed a decentralized incentive and reputation mechanism for CSN with two communication paradigms. The system is further divided based on these two paradigms, i.e., incentive mechanism between service providers and data collectors, and a reputation system between service providers and consumers. Further, the Advanced Encryption Standard (AES128) is also implemented for retaining the privacy of data collectors.

1.1. Contributions

This work is an extension of [17]. The contributions of this paper are summarized as follows.

(i) A blockchain-based incentive and reputation mechanism is proposed for CSN

(ii) To ensure data quality, a rating mechanism is implemented where requesters rate the acquired data from service providers

(iii) The issue of privacy of data collectors is tackled using the AES128 encryption technique

(iv) Furthermore, the performance of the proposed system is evaluated by three widely used performance measures for blockchain, which are stated below

(a) Gas consumption of incentive smart contracts

(b) Mining time against different string input lengths of the reputation system

(c) Gas consumption against different input string values of the reputation system

(d) The comparison of execution time between different cryptographic techniques

1.2. Organization

The paper is further organized as follows. Section 2 provides the literature review, which is further divided into three categories, i.e., blockchain-based CSN, incentive mechanism-based CSN, and privacy mechanism-based CSN. Section 3 describes the motivation behind the proposed system and the identified problem. Section 4 presents the explanation of the proposed system model. In Section 5, an analysis of security, privacy, and robustness is discussed and Section 6 presents the details of experimental results, whereas the paper is concluded in Section 7.

2. Related Work

The related work for CSN is divided into three categories: blockchain, incentive, and privacy mechanisms.

2.1. Blockchain-Based CSN

CSNs are categorized as generally large groups of people that possess mobile devices for different purposes. These devices can sense and process the shared data that can be utilized to measure, map, analyze, and extract important information. Most smart mobile devices, e.g., phones and tablets can sense several inputs, such as ambient light, location, noise, and movement. In [18], a blockchain-based incentive system for CSN is proposed. It works on the principle of motivating the participants through retaining their privacy. Mainly, the system takes into account the truthfulness by introducing a cryptocurrency token as a premium to the participants. With this system, high-quality users get a reward and it is stored in the blockchain. The process integrates a server that publishes a sensing task, users who complete and upload a task assignment, and the miners that verify the quality of data. Once the verification is done, the transactions must be validated, and the rewards are distributed by the server to the participants. While in [19], the authors propose a blockchain-based mechanism that uses location privacy preservation as an incentive in CSNs. The mechanism emphasizes to protect any information and provides rewards to participants that increase the users’ participation. The experimental tests were conducted with a total of 10 participants in a campus environment, and the obtained results are effective in encouraging the participation of users. In [20], the authors have introduced a crowdsensing blockchain-based system where both the miners and workers that are involved in sensing task are rewarded via a predefined incentive system, which incorporates authentic anonymity and robustness. The related work is summarized in Table 2.

Table 2

Summary of related work.

Schemes	Contributions	Limitations
Blockchain-based CSN
Blockchain-based incentive mechanism for CSNs [18]	Ensures data quality, increases participation level, and preserves the privacy of the workers	Collusion attacks are ignored between anonymity groups and miners
A decentralized privacy-preserving incitement mechanism for CSNs [19]	Ensures privacy by using affine cipher and stimulates user involvement	Insecure communication platform and does not consider service consumers
Blockchain-based crowd sensing system (BCS) [20]	Provides authentic anonymity of the workers and system robustness	Possibility of privacy leakage of workers while submitting location for efficient job allocation
Incentive mechanism-based CSN
A social incentive mechanism for CSN [9]	Promotes global cooperation	Structure of participant’s social relationship is not considered
Incentive mechanism for CSN named as QUOIN [10]	Provides quality and usability of information and stimulates participation rate	Single point of failure and mutability
Incentive mechanism based on contract theory for mobile CSNs [21]	Increases participation rate and maximizes platform’s profitability	Centralized server causes delay in performance
Incentive mechanism involving ParticipAct platform for CSNs [22]	Promotes user active collaboration	Single point of failure and no transparency
Privacy mechanism-based CSN
CSN as a service and contribution [23]	Preserved privacy with AES256	No traceability mechanism
DTRPP [24]	Combined public key with trust management mechanism for tackling the issue of privacy	Ineffective in terms of cost
Location preserving mechanism of mobile users by combining $k$-anonymity and differential privacy [25]	Protects location of mobile users	Ineffective in terms of cost

2.2. Incentive Mechanism-Based CSN

The system proposed in [10] is known as QUOIN. It ensures the usability and quality of the information for CSN applications. The theory of the Stackelberg game is implemented so that every participant is benefited by an equal and sufficient part of incentives. This system is evaluated by conducting a case study. The obtained results show the efficacy of the system for motivating the workers to participate.

The authors of [21] have proposed a monetary encouragement mechanism for CSN. The system is based on contract theory. The process includes a trust plan implemented between the platform and mobile network users. The trust scheme includes direct and indirect trust patterns. A contract is established that has incentive allotment criteria outline. Along with the platform’s profitability, this contract appeases the customer’s incentive agreeableness. Furthermore, in [9], the authors have suggested a new scheme that is called social incentive mechanism. As the name suggests, it is based on providing incentives to the friends of the participants in the network. It intensifies the social bonds among network users, which in turn promotes global ties. The incitement helps to promote participation as when a user motivates its friends to participate, it is rewarded with an increased payback. The networks based on the interdependent relationship are highly benefited from this kind of incentive approach.

The authors in [22] have presented a case study with the ParticipAct platform and living lab. The experiment is conducted at a University located in Bologna. The experiment involves a total of 170 students, and the duration of the experiment is 365 days. The crowd users participated in several crowdsensing tasks and campaigns. Moreover, mobile phones were accessed passively, and the user’s active cooperation and collaboration are provoked. The article outlines the platform’s architecture, design, features, and reports with integral results.

2.3. Privacy Mechanism-Based CSN

The encryption techniques play a vital role in preserving the privacy of any participant. The process of encryption and decryption is compared by the authors of [26, 27] such as Rivest-Shamir-Adleman (RSA), AES, Blowfish, and Data Encryption Standard (DES). The features considered for comparing the techniques are time, avalanche effect, entropy, and memory used. Similarly, in [23], the authors proposed a platform where CSN is promoted as a contribution. Nonetheless, whenever there are people entangled, there is a possibility of exploitation of privacy. For CSN, privacy leakage is a loose end because this platform purely relies on participant’s in-volunteering or volunteering actions. This kind of problem is tackled using AES256 for preventing the exploitation of the user’s privacy. Likewise, in [19], affine cipher is implemented for a similar issue as mentioned above.

Additionally, for conventional CSNs [24], Dynamic Trust Relationships Aware Data Privacy Protection (DTRPP) mechanism is used for preserving the confidentiality of the participants. The platform integrates the trust management mechanism with the public key. The results display the improved performance of the system in terms of delivery, load rate, and average delay as compared to traditional mechanisms. Similarly, in [25], the authors have suggested a system to shield and safeguard the location of the users participating in the network by integrating differential privacy-preserving and $k$-anonymity.

3. Motivation and Problem Statement

In this section, the motivation behind the proposed system is discussed along with the problem statement.

3.1. Motivation

In [20], the authors have proposed a quality-driven auction-based incentive mechanism for MCSN. Similarly, in [28], the authors have introduced TaskMe. It is also based on a cross-community and quality-enhanced incentive mechanism for MCSNs. These incentive-based mechanisms motivate the participants to take part in the task sensing and consequently enhance the quality of data. The higher quality of data provided by users, the more reward a server returns to users. In [4], a Stackelberg game theory model-based incentive mechanism for CSN is proposed where the authors have considered three entities instead of two, i.e., service providers, service consumers, and data collectors. Furthermore, in order to recruit mobile workers, the authors of [7] have proposed reputation-aware recruitment and credible reporting for platform utility in MCSN. The mechanism is aimed at hiring mobile workers based on the reputation for quality reporting with the intention of platform profit maximization for an Internet of Things (IoTs) scenario. By taking the motivation from the above work, in this paper, we have proposed a blockchain-based decentralized system with seven groups having distinct roles, i.e., service providers, service consumers, data collectors, blockchain, communication platform, arbitrator, and a reputation system for ensuring the integrity and immutability of data through registered reviews.

3.2. Problem Identification

In [19], a decentralized virtual credit incentive mechanism is proposed while providing privacy protection for CSN entities. The main objective is to tackle two problems, i.e., stimulating user participation and privacy exposure. Affine cipher is used for privacy protection, and the other issue is tackled by giving the guarantee of preserving the participant’s privacy. However, it is affiliated with the class of classical monoalphabetic substitution schemes. It is also liable to all the cipher attacks. Furthermore, the medium used for communication is not a smart contract, and the technique used for encryption is implemented separately. Also, the third entity used for utilizing the data, i.e., service consumers is not considered in the proposed system.

Moreover, to build trust between the service providers and consumers, it is necessary to build a system, which assures the integrity and reliability of data being sold out to consumers. To tackle such a challenge, a reputation system is introduced for the proposed scenario and the motivation is taken from [7, 29]. Another issue is raised during the system development, i.e., no stimulus is provided for the consumers to register a review. There is no incentive for consumers for contributing their time and computational resources for registering a review. This problem can damage the system’s performance.

To confront the aforementioned limitations, we have recommended a scenario, which is then divided into two units of communication that are explained below.

3.2.1. Communication between Data Collectors and Service Providers

In the suggested scenario, the service provider establishes a smart contract. AES128 encryption technique is applied to guarantee that workers’ identities are preserved while surrendering their location for task assignment; hence, guaranteeing the privacy of data collectors. Furthermore, incentives are allotted to all the data collectors immediately to motivate user participation.

3.2.2. Communication between Service Consumers and Service Providers

To initiate communication between a consumer and service provider, a smart contract is deployed that triggers the function of the service request and its response, accordingly. Further, to check the integrity of data, a decentralized reputation mechanism is implemented between them. To solve the problem of motivating consumers for registering a review, an incentive mechanism is also introduced for service consumers. The reward is issued only to those consumers, who wish to register their reviews and contribute to enhancing the performance of the whole blockchain-based reputation mechanism for CSN. Furthermore, a fake review is another major challenge in the reputation system. To eliminate the fake reviews, which could be done by any consumer or an opponent company/organization, the Revain platform is used to make sure that the module identifies fake reviews. Moreover, in case of a dispute between a service consumer and a provider, the arbitrator steps in to resolve the clash and restores all the collaterals. The proposed system is compared with the existing systems in Table 3.

Table 3

Comparison with existing work.

References	Smart contracts	Service consumers	Encryption	Reputation system	Identification of fake reviews	Dispute resolution
Reputation aware recruitment platform for CSN [7]		✓		✓
Blockchain-based incentive mechanism for CSN [19]			✓
Smart contract-based review system [29]	✓	✓		✓
Proposed mechanism: blockchain-based incentive and reputation mechanism	✓	✓	✓	✓	✓	✓

4. System Model

The system model of the proposed mechanism is further divided into two modules, i.e., incentive system and reputation system. They are elaborated in the subsections below.

4.1. Incentive System

This system is developed for providing an incentive mechanism based on blockchain for CSN. Four entities are participating in the proposed scenario, i.e., service provider, arbitrator, service consumers, and data collectors. The following words, i.e., requester and service provider, data collector, and worker are being used alternatively throughout the paper. The aspects of each role are defined in Table 4.

Table 4

Roles of entities of a CSN.

Participants	Roles
Requester/service provider	Publishes an assignment in the network and accommodates services for consumers
Service consumers	Request and inquire data. Then, utilize the data obtained by a data collector
Worker/data collectors	Measures the necessary data of interest in accordance with the defined criteria using smart gadgets
Arbitrator	It is a trusted entity by the requester, consumers, and workers. The role of the arbitrator is to resolve disputes between the requester and the consumer. It also settles the quarrel by downloading the same item requested by the consumer and decides whether the complaint filed is valid or not.

The smart contract is initially called by a requester, and it sets the demands of the data sensing task. Service providers deposit some amount that is later established as a monetary reward for the workers. The task assignment is finally authorized and broadcasted in the network. To preserve the privacy of the workers for promoting and motivating their participation in tasks, AES128 is used to encrypt private information, therefore, preventing the exploitation of privacy. Thereafter, when the data collectors submit their assigned task is verified by the miners. After verification, the rewards are immediately allotted that are set aside in the smart contract’s protocol. The prompt incentives build up the repute of the system. Also, it encourages both miners and data collectors for their devotion. Similarly, trust is already established between service providers and other participants because of the rules set in the smart contract. As a result, a requester is considered a reliable entity. Additionally, data collectors are charged with a definite aggregate of gas while posting the sensed data. The cumulative gas serves as a security deposit by the data collectors and guarantees the authenticity of the participant. This process aids in avoiding various kinds of attacks. The motivation for the proposed system model is taken from [19, 29, 30], as shown in Figure 2. The interaction between service consumers and provider takes place through a separate smart contract as shown in Figure 2. If a consumer requires service from a requester, it inquires for a review before sending the request. This mechanism is shown in Figure 3. A specific request is sent by the consumer for establishing a smart contract to the requester. The payment is made immediately in exchange of the requested data. Also, the usage of smart contracts aids in getting the job done efficiently and effectively. Additionally, it also dismisses the possibility of any blunder that may occur in traditional agreements or contracts.

[figure omitted; refer to PDF]

The CreateTask() function is used to create a task along with the decided monetary reward for each sensing task. A small amount is deposited by a service provider while initiating a smart contract. The deposited amount defines the reward for the published task. Because of this, CreateTask() has used the greatest quantity of gas in comparison with the other operations. Whereas, Abort() is called, when it is believed that data collectors have gathered enough data by examining the number of data through calling the CheckData() function.

Figure 5 displays the gas consumption of the functions that are triggered by data collectors. In a classic CSN, the data collectors are provided with a choice regarding the selection of task. However, in the proposed scenario, it is presupposed that the worker is looking forward for the broadcasted task only. There are two functions executed by data collectors, i.e., getTask() and commitTask(), respectively. To read the informational aspects of the task prescribed by the service provider, the getTask() function is used. It includes monetary reward and amount of data. Also, it is vital for workers to first look at the assignment and inspect the criteria. Otherwise, if the entered data is not according to the defined criteria, the worker is considered ineligible for incentive. Further, the commitTask() is called to acknowledge the gathered data that demands more computational efficiency in comparison to view the assignment; therefore, the consumed gas of former function is less in comparison to the latter function.

[figure omitted; refer to PDF]

Figure 6 shows the gas consumption of the functions executed by the service consumer. The functions executed by smart contract are ServiceRequest(), Payment(), and ServiceResponse(). The first two functions consume almost the same amount of gas; however, Payment() demands more execution and transaction gas. This function triggers the smart contract’s payment protocol. The monetary transaction takes place and later on added in the blockchain. This action justifies the increased consumption of gas.

[figure omitted; refer to PDF]

Figure 7 depicts the gas consumption by four functions of the review system. SetReviews() consumes a noticeably greater amount of transaction and execution gas as compared to others. Whereas, GetReviews(), GetRatings(), and IsReviewExist() functions have lesser gas cost. Execution cost depends on the computational operation that is executed as an outcome of the transaction. The operations performed for SetReviews() are more logically complex because when this function is called, it registers the reviews of the user and saves them in blockchain for future use. However, transaction gas is the cost of sending the contract code to the Ethereum blockchain in addition to the execution cost, which validates the high transaction cost of SetReviews() function in comparison to others.

[figure omitted; refer to PDF]

Figure 8 shows the mining time against each input string value of the reviews. The data is processed as inputs string for the specified fields. To investigate the effect of the size of input over the mining time, we take data in all three fields of the review system and mine to check the effect of input string size over the mining time. The result is different for all of the input strings; therefore, it is concluded that there is no explicit relationship between string size and mining time. However, the mining time depends on the network parameters of the system, as miners calculate hash, which should be below the target. Target is computed from the difficulty, which is a value set by the network to regulate it that how much it is difficult to mine a block of transactions in the blockchain. Hence, this proves that mining time is determined by network conditions.

[figure omitted; refer to PDF]

Figure 9 demonstrates the gas consumption against the input string length. As it can be seen that on the $x$-axis, we have taken the input string, and on the $y$-axis, we have plotted gas consumption. To check whether increasing the input string increases the gas consumption, we took four input values with different string lengths. The plotted results show that they have a direct relationship as the gas consumption increases with the increase in string length.

[figure omitted; refer to PDF]

Figure 10 shows the comparison of cryptographic techniques based on the execution time of encryption and decryption in milliseconds (ms). The process of transforming normal text into ciphertext is called encryption; whereas the process of converting the ciphertext into normal text is called decryption. To produce a more quicker and responsive system, both of the abovementioned processes are required to take less time for execution. Similarly, they also affect the performance of the system. Therefore, for this scenario, affine cipher, 3DES, AES128, and AES256 are compared based on execution time. Affine cipher is used in [2] for protecting the location information of workers; however, it is affiliated with the class of classical monoalphabetic substitution schemes. The mentioned class can be easily interpreted by solving a set of concurrent equations. Additionally, it is liable to all the cipher attacks; as a consequence, it is not considered to be a strong and secure technique for encryption in comparison to the modern symmetric key block cipher approaches. From the literature review of [22, 26, 27], we executed three more encryption techniques. The execution time noted for affine cipher, AES256, AES128, and 3DES is $9.07$, $10.29$, $7.43$, and $30.81$, respectively. By acquiring the time, it is found that AES128 performs better with the least execution time in comparison to AES256, affine cipher, and 3DES. AES256 is considered to be more secure when compared with AES128 having more rounds and lengthy key size, i.e., 256 bits. However, AES128 beats the AES256 in terms of execution time. Hence, to have a more responsive mechanism, we preferred AES128 over AES256 according to the suitability of the proposed scenario.

[figure omitted; refer to PDF]

7. Conclusion and Future Work

With the evolution and expansion of technology on a huge scale, blockchain has come forth as the most suitable solution for providing a distributed yet shared environment while preserving the privacy of all the participants in the applications. In this article, a decentralized incentive and reputation system is proposed for a CSN. It is aimed at persuading workers and at captivating expert user’s attention. The process of encryption is incorporated to protect the private information of data collectors. Smart contracts are used as a reliable transmission medium. The proposed system caters to the requirements of all entities in a decentralized manner; consequently achieving consistent data, secure communication, increased cooperation rate, and authentic reviews. The incentive mechanism is evaluated by inspecting the gas utilization of all the functions, whereas the reputation mechanism is inspected through studying the gas consumption and mining time against input string length. Furthermore, based on encryption’s execution time, i.e., $7.43$ ms for AES128 and $9.07$ ms for affine cipher, the former technique is selected for the proposed scenario. Although AES256 provides a high level of security as compared to AES128; however, AES256 takes more time to encrypt. Therefore, the trade-off between time and security level is also considered for the proposed scenario, and it is concluded that AES128 is an appropriate technique for the system.

For the future, our goal is to measure the trustworthiness of the data, which is submitted by the participants. The objective is to compare the user’s trust attributes and the application of nonparametric statistic methods and analyze the outcome. The results will be examined based on data subjectivity for the proposed scenario.

Acknowledgments

This research was supported by the MSIT (Ministry of Science and ICT, South Korea), under the ITRC (Information Technology Research Center) support program (IITP-2021-2016-0-00313) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).

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