# ExCrowd: A Blockchain Framework for Exploration-Based Crowdsourcing
## 7 Implementation and Performance Analysis
### Comparison  
Figures 12 and 13 illustrate a comparison of the proposed system in terms of registration time and processing overhead with three existing ones, namely PrivCrowd: A Secure Blockchain-Based Crowdsourcing Framework with Fine-Grained Worker Selection [40], CrowdR-FBC: A Distributed Fog-Blockchains for Mobile Crowdsourcing Reputation Management [41] and An Incentive and Reputation Mechanism Based on Blockchain for Crowd Sensing Network [35]. For starters, the proposed system is analyzed for its typical registration times. By altering the number of blocks, it has been shown that the proposed task takes less time to execute than the existing systems. Thus, the performance is evaluated depending on the amount of time it takes to process the data.  
Figure 11: Processing time for image evaluation with \(e=5\) and varied worker sizes (WITHOUT BLOCKCHAIN).  
Figure 10: Analysis of latency in high-quality submission of 50 images with varied worker sizes and different \(e\) values of repeated assessment submission (WITHOUT BLOCKCHAIN).  
It has been possible to use a variety of crowdsourcing systems up to this point. Table 5 compares these systems on the basis of a variety of important factors.The qualities of privacy, decentralization, smart contracts, encryption, exploration based, and Shapley value are all taken into account in this comparison. The first factor is privacy. As a result of the sensitive data stored in these systems, it is essential to protect the privacy of users.  
Certain crowdsourcing systems do not require participants to use their actual names, which helps protect their anonymity. The most critical aspect affecting a crowdsourcing system's acceptability is its ability to boost both the quality and quantity of user participation. The second parameter considered is decentralization, where the system is not controlled by a central authority, thereby lowering the risk of system failure. The third parameter used in this comparison is the adoption of smart contracts, which helps to check suspicious behaviors of users in the system, especially with requesters who refuse to pay their incentives.  
Furthermore, encryption is performed to secure any form of private information, eliminating the exploitation of privacy. The fifth factor used in this comparison is the exploration-based approach, which ensures that the system is not biased towards high-reputation workers as is conducted in reputation-based systems, where certain systems take workers' reputations into consideration when allocating tasks. This kind of approach plays  
Figure 12: Proposed system overhead comparison with [35,40,41]  
Figure 13: Proposed system’s execution time comparison with [35,40,41].  
a critical role in reputation-based threats as a result of some unscrupulous workers engaging in requester-worker collusion, resulting in the system storing false data impressions.  
Finally, we adopt the \"Shapley value\" approach in our reward distribution. Workers will only stay engaged in crowdsourcing if they feel that their efforts are being compensated fairly. As a cooperative game, crowdsourcing may be modeled using Shapley values, one of which is the best approach for distributing prizes fairly. Each worker's participation may be evaluated via the smart contract and their incentives distributed evenly using our recommended Shapley value-based distribution method. There is no waste in the distribution of advantages from collaboration. Equal payoffs are given to those who contribute equally.