# ExCrowd: A Blockchain Framework for Exploration-Based Crowdsourcing
## 7 Implementation and Performance Analysis
### Time Complexity  
Caltech-101 is an object recognition dataset provided by [PERSON] et al. in Learning generative visual models from few training instances ([[https://data.caltech.edu/records/20086](https://data.caltech.edu/records/20086)]([https://data.caltech.edu/records/20086](https://data.caltech.edu/records/20086)), accessed on 11 March 2022). It features images from 101 distinct object categories with varying sizes and spans. For each object category, there are about 40 to 800 images, but then most classes contain about 50 images. The images have a resolution of around 300 \(\times\) 200 pixels. We ran 25 sets of trials to process picture tagging tasks using the CALTECH-101 dataset. We chose 10 categories of images such as elephants, bicycles, soccer balls, human brains, crocodiles, and airplanes to analyze the utility, security, and efficiency of the system. Each of the 800 images in the CALTECH-101 dataset is split into five training batches and one test batch.For each set of experiments, we choose images at random from the training batch. Furthermore, five employees and one requester have been added to the system, with each obtaining 20 ETH coins. Three workers complete each activity. We chose 500, 1000, 1500, 2000, and 2500 photos randomly from the training samples based on task 500, 1000, 1500, 2000, and 2500. The solutions are also secured with the public key of the requester and preserved in data storage, making them inaccessible to malevolent users. We deployed the UserContract, JobContract, AgreementContract, TanderingContract, and AssessmentContract in the Ropsten Network. In Ropsten, the average mining block time is 10 s. A total of 800 transactions were used in the experiment. Each transaction is mined together into single blocks with a block of numbers, which ranges from 4,324,649 to 4,330,703, as depicted in Figures 5-7. On task publishing, receiving of tasks, as well as submission of solutions, the average transaction confirmation time was about 39.24 s, 34.17 s, and 38.71 s, respectively. Each transaction was confirmed in around 35 block time. Furthermore, we provide a network performance metric in Table 3, gas consumption in Table 4 and feature comparison in Table 5.  
Figure 4: User registration delay. Figure 5: Time complexity of task posting in crowdsourcing. Figure 6: Time complexity of task receiving in crowdsourcing. Figure 7: Time complexity of answer submission in crowdsourcing. To have a clearer perspective of the transaction costs associated with implementing the primary functions of our application, we first gather gas needs before calculating the cost per gas at 1 Giga-Wei. It should be emphasized that one Gwei equals 0.000000001 ETH, and the value of one ether during this experimentation (February 2022) is USD 2935.89. Observing the information in Table 4, we determine the entire task cost from the uploaded time to the time it undergoes system review. The entire amount of the needed gas is 2,531,339, which equates to USD 0.3731. Since the expenses of registering as a worker or posting a job are only incurred once, they have not been included in this calculation. Various ways of testing the smart contracts were investigated throughout the design of our smart contracts. One approach is to utilize software such as ganache-cli, which provides a localized replica of the Ethereum blockchain for testing purposes. A common alternative is to employ test networks, which include Ropsten or rinkeby. The test networks function similarly to the Ethereum main net, with the exception that ether in these networks has no value and is primarily used for testing. We conducted a segment of a range of image tagging investigations and documented the image tagging accuracy by the workers in each task using varied image quantities. On 8500 images, the average accuracy was 96.52%; amongst the erroneous findings, some images were extremely difficult to deduce. This demonstrated ExCrowd's effectiveness.