> For the complete documentation index, see [llms.txt](https://university.elumicate.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://university.elumicate.com/tokenomics/mining-rewards/worker-rewards.md). # Worker Rewards In the Elumicate Network, worker allocation plays a crucial role in ensuring the stability and efficiency of the network. The allocation of workers is based on specific tasks and the amount of work accomplished by each worker. The goal is to distribute rewards fairly among the workers based on their contributions. During the network's inception, each input stream or camera feed is initially allocated one worker. However, as the network progresses and evolves, additional workers can be acquired through various means such as data mining, ownership of NFTs, and token staking. The allocation of workers may change over time, driven by advancements in data mining efficiency and decreasing hardware costs. For example, as hardware miners become more powerful and cost-effective, they may be able to handle multiple camera streams simultaneously, leading to the allocation of multiple workers to a single miner. Worker allocation can be determined by specific tasks or computational power. For instance, when a camera feed is on standby and ready to be mined, it may initially be allocated one worker. However, when the camera feed becomes actively mined for multiple tasks, such as firearm detection, wildlife tracking, traffic counting, speed detection, and parking space availability, it may receive additional workers proportional to the complexity and demand of the tasks being performed. On the other hand, a different camera feed that is mined for a single task, such as vehicle count, may only be awarded one extra worker. This dynamic allocation of workers ensures that the network optimizes its resources based on the workload and requirements of each camera feed, allowing for efficient data capture and processing while fairly rewarding the workers for their contributions. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://university.elumicate.com/tokenomics/mining-rewards/worker-rewards.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.