1. Introduction

2. SYSTEMATIC LITERATURE REVIEW

2.5. OBJECTIVE BASED FILTERING FOR COMPLEXTY LEVEL TASKS

After title and abstract-based clustering, find the objectives of research papers and make filtering of the objectives as shown in Table 2. The objectives of the papers are as follows: Reputation score, Incentive, Privacy, and Complexity level.

Table 1. Objective-based filtering.

Table 1. Objective-based filtering.

2.6. RELEVANCE ASSESSMENT

The relevance evaluation (RA) criteria of chosen research are created utilizing the Kitchehem and Charters standards [14].

The following Quality questions (QQ) are used to determine RA:

QQ1: 

Is the study's goal/objectives described clearly?

QQ2: 

Is the study intended to attain certain goals?

Q3: 

Are the study's findings and outcomes well described?

QQ4: 

Is the research adequately referenced?

Individual research RA is done using quality points that are based on the following rules:

• RULE1: If the selected research responds to the quality inquiry, its score
• RULE2: If a research only partially addresses the quality question, its score is 0.5.
• RULE3: If the chosen research does not respond to the quality question, its score is 0.

Table 3 shows the assessed relevance of each selected study.

Because the RA score is acceptable for all investigations, none of the studies were rejected because of it.

• Low: If total points <=1
• Medium: If total points=>1<=2
• Good: If total points=>2<=3
• Excellent: If total points=>3=4

Table 2. Assessed relevancy of each selected study.

Table 2. Assessed relevancy of each selected study.

2.7. DETAILED LITERATURE

In the literature, data was gathered to get appropriate results. Calculations in certain study methodologies were based on the average of the stated task, with no adequate credibility and participation. Rewards were granted but not on the contribution level (sensors). In [5, 9], the authors proposed reputation quality aware recruitment (RQRP) as a framework for enabling high-quality reporting in mobile crowd sensing. They structured the plan into two parts: pre-quality measures and post-quality measures. Incentives are offered to mobile workers (MW) to maximize profit in IoT based on mobile quality reporting. According to [4,[4,] RQRP is broken into two major stages: filtering MW selection and checking the accuracy of reported tasks. The emphasis in the former is on selecting the best MWs based on a variety of parameters (such as reputation, a bid, anticipated quality, and forecast platform usefulness), whereas the emphasis in the latter is on incentives, sensing quality verification, and reputation score evaluation. Sensors for mobile apps collect data in an inefficient and costly manner in the crowd sensing network. Data is gathered from all devices and members, such as consumers, service providers, and data-gathering devices. To deal with all CNS members, centralized procedures are employed, which poses a significant security risk and makes privacy vulnerable to attack. In [6] and [10], the approach is decentralized and relies on block chain. The incentives are used to encourage mobile employees and data consumers to participate actively in network usage. They employed the advanced encryption standard AES (28) method to address privacy leakage concerns. A reputation framework was also implemented to assess misleading data and resolve other problems. The reputation system was built to solve issues such as the accuracy of data, fake reviews, and entity conflicts [5]. The method encourages data use by delivering accurate, consistent, and reliable data through the registration of reviews. Gas usage and string length are used to evaluate reputation. For network users and mobile workers, there is no suitable standard mechanism for incentive distribution. According to [24], the following MCS characteristics prevent existing incentive mechanisms from effectively encouraging users' active involvement and service contributions in multi-service exchange: lots of heterogeneous users have asymmetric service requirements; workers can choose their participation levels and sensing tasks; and several sensing tasks have heterogeneous values that may be falsely announced by the that corresponds requesters. To overcome this challenge and provide selected fairness, honesty, and limited efficiency while minimizing platform load, a green Stackelberg-game incentive system is created. To begin, the multi-service exchange issue is modelled as a Stackelberg multi-service exchange game with multiple leaders and many followers. In this game, every requester takes the role of a leader and chooses the reward announcement approach, which determines the payout for each sensing job.

Following that, each worker operates as a follower and chooses the sensing plan strategy that would maximize her utility. Current content-sharing methods are ineffective when it comes to personalized data sharing. On the basis of mobile crowd sensing, they devised a socially aware personalized content-sharing method. The first objective was to implement [11], which was to implement a two-stage pricing mechanism between the user and the service provider. Subgame perfect Nash equilibrium NE [10] and [12] of suggested game effective pricing method were solved. Two algorithms, Pareto optimal NE and Are developed, were utilized. They employ stochastic network models as well as real-world data sets. The downside of this strategy is that a third party is engaged in the uploading of personal content. It is difficult to avoid malicious individuals and untrustworthy task requesters in monetary incentive structures. Quality-aware incentive systems are unable to protect participants' anonymity, and data quality is not assessed. We evaluated PACE using a real-world dataset to demonstrate its usefulness and efficiency in [13]. The assessment and analysis outcomes show that PACE can prevent task respondents' and task requesters' aggressive conduct while also preserving privacy and measuring task participant data quality. They also suggested a zero-knowledge methodology for estimating data dependability [14], and real-world data sets were employed. The monetary rewards are the same as the data quality is the same.

Mobile crowd sensing is frequently employed in data collection and analysis. To address this issue, the authors of suggested a quality-based truth estimation approach and a surplus sharing mechanism in [15]. The unsupervised learning approach [16] is used to assess data quality and reputation by removing anomalies. Consider excess as a sharing game and suggest a shapely value best approach for each user's payment. In mobile crowd sensing MCS devices are equipped with sensors for data collection but ambiguities are seen in the data. Action-based techniques are widely used in incentive mechanisms but they have lots of challenges. In [17], addresses the effective task participant matching by, Completed task maximization, clearance rate (CR). They proposed a new bidding procedure for task allocation based on reputation aware auctioning [18]. For budget transfer, an intuitive look back method was used. Tasks with few bids should be given higher priority for completion. Introduced task returned and examined elements to complete further duplicate task assignments. It increases the clearance rate but decreases bidders for task accomplishment. According to research [19], Incentive-G is a game-based incentive system designed to efficiently attract mobile users while also boosting the reliability and quality of sensing data against untrustworthy or malicious users. The Incentive-G system comprises numerous design steps, including sensing data analysis, defining mobile user reputations, and assuring data quality and reliability through task group voting. This method employs a two-stage Stackelberg game to analyses the reciprocal relationship between service providers and mobile consumers, followed by backward induction to optimize incentive benefits. Our study demonstrates that determining the optimum data-provision techniques for mobile users helps prove the existence and uniqueness of the Stackelberg equilibrium. The results show that the Incentive-G mechanism may greatly incentivize mobile users to donate their efforts and optimize income for game-based crowd-sensing services. Uses social network influence propagation to aid in the recruitment of MCS workers. We begin by selecting a group of social network participants as initial seeds and assigning MCS tasks to them. Then, affected users who accept assignments are hired as workers, with the ultimate objective of increasing coverage. To be more specific, we present two methods, Basic-Selector and Fast-Selector, to pick a near-optimal collection of seeds. Basic-Selector uses an iterative greedy approach based on projected mobility, which has high performance but is inefficient. Fast-Selector, which is based on the interdependence of geographical placements among friends, is proposed to speed up the selection process[20]. However,[21] most quality-aware reward systems do not adequately secure task participants' privacy. Furthermore, these methods are intended for common MCS application situations in which data is collected by internal sensors integrated into participants' cellphones and are not appropriate for situations in which data is collected by additional sensors other than internal sensors (e.g., household medical devices).

Malicious players might In situations with several sensors, false sensing data is used instead of gathering data from additional sensors, indicating that the reliability of sensing data cannot be guaranteed. We propose P² SIM, a privacy-preserving and source-reliable incentive mechanism scheme for MCS with extra sensors, to overcome these challenges. To provide source reliability verification of sensing data while maintaining participants' anonymity, we utilize a reachable signature with a private hash function. Furthermore, to increase the flexibility of incentive distribution, awards are separated into two categories: fixed rewards and floating rewards. In this research [22], we analyses a scenario If worker arrivals are unevenly distributed and job quality rises in line with the rule of decreasing margins of employee effort. We propose a quality-based online task-bundling reward system (QOTB). The purpose of the design is to increase social welfare while achieving work quality criteria to the maximum degree feasible. We apply Mental Accounting Theory at QOTB to build accounts for job execution profitability and reward, which are then used to determine workers' willingness to participate. We utilize task bundling to encourage employees to modify their initial travel plans in order to balance task participation depending on the popularity of task locations as well as the expense of travel. Furthermore, current systems tend to assign more work to people with high reputations, leaving fewer tasks for new users with poor reputations. To overcome these deficiencies, a restricted multi-objective optimization model of variable speed multi-task allocation is designed to maximize user incentives while reducing work completion time. Meanwhile, each user has a limited amount of fully compensated activities that are favorably connected with reputation. To solve the constructed model, a three-stage multi-objective shuffled frog leaping algorithm is proposed, which includes an objective defined a hybrid initialization operator is based on heuristic information, a region-based mining approach for archiving individuals and others, a discrete establishing rule to improve the interacting between individual details, and constraints performing operators for minimizing personal data loss. The performance of the proposed algorithm is evaluated by comparing it to five state-of-the-art approaches in both real-world and synthetic circumstances. The bulk of contemporary mobile crowd-sensing solutions rely on centralized systems, which have significant practical restrictions. The storage of data is overly reliant on third-party platforms, resulting in single-point failures. Furthermore, trust issues have a major influence on both user willingness to interact and data reliability [23]. To overcome these two challenges, this work presents a credible and distributed incentive system built on Hyper Ledger Fabric (HF-CDIM). The HF-CDIM, in instance, incorporates methodologies from auction, reputation, and data detection [24]. Based on the DRL and Stackelberg game models, we search to create a privacy-preserving incentive structure for MCS in this study. The suggested incentive system is built around a two phases Stackelberg game in which the service provider takes the lead and the user devices follow. We characterize the user device connection as a non-cooperative game and demonstrate that Nash equilibrium (NE) occurs and is unique in this game. We use the reputation constraint mechanism as the data quality evaluation standard, and we add sensing cost as an indicator, taking into consideration the cost and quality of sensing data. [25]. The Mobile Crowd Sensing (MCS) architecture, which leverages mobile devices as sensing systems, is a viable option. A promising option is the Mobile Crowd Sensing (MCS) architecture, which uses mobile devices as sensing platforms. A few field tests proved the technique's effectiveness as a feasible method of supporting municipal authorities and broadening the possibilities of collaborative urban noise monitoring [26]. MCS uses in industry and people's personal lives [27].

Developed the quality-aware incentive mechanism (QAIM) technique to cope with variable quality needs from task to task, which should be efficient enough to accurately quantify report quality [28]. Many strategies for ensuring sensing quality in MCS have been proposed. The list below includes some of them. MCS has a knowledgeable audience with low-cost services, but it may also be a major drawback (for example, when jobs might be submitted with fake or unsatisfactory quality). Participants who are selfish or strategically minded may act maliciously to improve utility by delaying or changing job completion features. Sensing reports may be malicious or supplied for the purpose of "free-riding." In any event, platforms must pay to obtain reports from MWs [29]. Data from the literature has been collected and established to generate approximated right findings. Aggregation was just the average of the reported tasks in certain ways, with no concern for reporter credibility or involvement level, and incentives were awarded regardless of contribution. A weighted reputation technique was then proposed [30]. Two models are proposed [31] (IMC-Z and IMC-G): incentive approaches for crowd sensing systems with zero and general instances. The zero model was formed when the departure and arrival timings were not taken into account. In contrast, the generic model, in which MW may report on in-out time, was described. Observations were made to set the norm for future recruitments; the strategy is purely focused on inexpensive costs, and honesty is considered. Another approach for assuring the honesty of declared bids under the restriction of a reasonable budget is presented in paper [32]. This study proposes Experience-Reputation (E-R) as a mechanism for analyzing trust connections between any two mobile device users in an MCS platform [33]. This study describes a unique technique for the VCS system called Security Protection Incentive Mechanism with Data Quality Assurance (SPIM-DQA). The goal is to solve the security problems connected with user data while also improving overall data quality. To do this, we use a block chain-enabled VCS framework and provide a set of smart contracts that allow the incentive mechanism to be executed automatically. We effectively overcome the user data security vulnerabilities commonly observed in traditional incentive methods by using this architecture and utilizing smart contracts. Furthermore [35], we present a data quality-aware incentive system aimed at improving the overall quality of the obtained data. This technique selects individuals based on factors such as cheap cost and good quality, guaranteeing that only trustworthy users engage in the crowd sensing job. We examine the quality of the data given by each user to maintain and update user reputation. We can identify and reward people who consistently give high-quality data using this review procedure. In the literature, [36] did a survey in which they classified several forms of crowd sensing based on numerous parameters. The amount of human engagement necessary in the crowd sensing system is one of the categorization criteria they mentioned. As a result, current studies may be divided into two types: participatory sensing and opportunistic sensing. In the literature, [37] presented a block chain-based solution to improve the security of the automotive ecosystem. Several research in this sector have offered block chain-based architectures and smart contracts to enable secure and automated crowd sensing operations. However, further study is needed to properly characterize the workflow of the block chain-based incentive system and the smart contract's triggering mechanism, as both features are vague in present studies. Furthermore, when it comes to data quality, many current studies focus exclusively on measuring the temporal and spatial coverage of the data, while ignoring the evaluation on data quality supplied by users. In order to solve this issue thoroughly, greater emphasis must be placed on analyzing the quality of user-provided data. For example, in this study [38], we offer a technique for assessing data quality based on temporal and geographical coverage. They also allow for user selection by defining minimal criteria for sensing task quality. [39] offer an unsupervised learning approach for evaluating and enhancing the quality of users' data through the merging of quality estimates and monetary incentives. While these techniques have improved data quality, their quantitative procedures include drawbacks such as high computing complexity and extended operation cycles. As a result, they are unsuitable for a reverse auction-based incentive system. In the rapidly evolving landscape of computer science and cybersecurity [40-52], the convergence of cutting-edge technologies such as deep learning and machine learning has become pivotal in fortifying digital systems against emerging threats.

• 2.7. CRITICAL ANALYSIS

In Table 3, a summary of the critical analysis of Reputation and Incentive based schemes is discussed

Table 3. Summary of critical analysis of Reputation and incentive based schemes.

Table 3. Summary of critical analysis of Reputation and incentive based schemes.

Year	Schemes	Objectives	Advantages	Limitation
2018	RQRP Reputation, Quality aware Recruitment for Platform [1]	On new requests, data connected to MW's prior history is supplied to improve the quality of sensing requests based on reputation.	The advantage of the proposed scheme is that it improves the truthfulness and quality of sensing data while maximizing platform profit in the Internet of Things (IoT) scenario	The paper's drawback is that it concentrates on the single-task reputation updation situation, but more general scenarios with numerous requesters might be difficult to manage [4]
2021	Decentralized incentive and reputation mechanism [2]	To increase participation and data dependability in crowd sensing networks with a decentralised reward and reputation system.	improved data reliability, increased participation, and enhanced privacy protection in crowd sensing networks	The main drawback of the method suggested in the study is the usage of an unsecured communication platform, which could risk the workers' privacy [5]
2023	Data quality assurance and security protection incentive mechanism (SPIM-DQA) [3]	This paper's goal is to suggest a security protection incentive system for vehicle crowd sensing that includes data quality assurance.	Enhancement of vehicular crowd sensing user engagement, job completion quality, data quality assurance, and security protection.	The proposed incentive structure assumes rationality and rewards as motivations but ignores factors like privacy, trust, and social conventions [35], impacting user engagement with VCS.
2021	PACE privacy-preserving and quality and data quality-aware incentive scheme [4]	A privacy-preserving and data-quality-aware incentive system for mobile crowd sensing is being developed to assure privacy preservation, avoid suspicious behaviour, and assess data quality.	PACE method: A privacy-preserving, data-quality-conscious incentive system for mobile crowd sensing that ensures accuracy, participant privacy, and fraud avoidance..	The PACE scheme's drawback is its reliance on a trusted third party to estimate data dependability, which might be impracticable in some cases [10]
2021	Green Stackelberg-game incentive mechanism [5]	Develop an efficient, fair, and honest incentive system for mobile crowd sensing using a green Stackelberg game strategy, while minimizing platform burden.	Obtaining selected fairness, honesty, and restricted efficiency, reducing the stress on the platform, and balancing service requests and service provisions amongst users through the utilization of virtual currency.	They did not manage the procedure of converting virtual cash to actual money [7]

3. METHODOLOGY AND SETUP

Figure 4, shows the architecture of proposed scheme. Firstly, platform announce task and requester request for task. If the task is less than miles threshold score, then the task is simple task and if the task is not less than miles threshold score then task is complex task. By using mobile worker detail check the feedback if the feedback is greater than 1 then select worker, calculate their reputation score and give incentive according to their complexity level.

Table 4. NOTATION TABLE.

Table 4. NOTATION TABLE.

Acronyms	Definition
CR, SR	Complex Reputation, Simple Reputation
CFS, SFS	Complex Feedback subset, Simple Feedback Subset
DRS	Default reputation score
W-F	Weightage Feedback
DT	Default Threshold
B_MW’s	Best _Mobile Worker’s
R-score	Reputation score
MW’s	Mobile Workers
CW, SW	Complex Weightage, Simple Weightage
FR_ Score	Final Reputation Score
l(group)	length (group)
MT	Miles Threshold
MW_Details	Mobile Worker Details
CT,ST	Complex Task ,Simple Task
CRS,SRS	Complex Reputation Score ,Simple Reputation Score
ACRS,ASRS	Average of Complex Reputation Score , Average of Simple Reputation Score
RDF	Reputation Driver Feedback
D_F	Driver _ Feedback
QS	Quality of Simple
QC	Quality of Complex

4. RESULTS ANALYSIS

5. Conclusion

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