# Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)

arXiv: https://arxiv.org/abs/1602.03202 · HTML: https://mlsanity.com/paper/1602.03202/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2016.

## Built on by (most cited first, 4 of 4)

- [FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning](https://mlsanity.com/paper/2106.02310/) (2021)
- [Strategic Coalition for Data Pricing in IoT Data Markets](https://mlsanity.com/paper/2206.07785/) (2022)
- [Privacy-Aware Data Acquisition under Data Similarity in Regression Markets](https://mlsanity.com/paper/2312.02611/) (2023)
- [How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?](https://mlsanity.com/paper/2412.07813/) (2024)

## Abstract (arXiv)

Big data has been emerging as a new approach in utilizing large datasets to optimize complex system operations. Big data is fueled with Internet-of-Things (IoT) services that generate immense sensory data from numerous sensors and devices. While most current research focus of big data is on machine learning and resource management design, the economic modeling and analysis have been largely overlooked. This paper thus investigates the big data market model and optimal pricing scheme. We first study the utility of data from the data science perspective, i.e., using the machine learning methods. We then introduce the market model and develop an optimal pricing scheme afterward. The case study shows clearly the suitability of the proposed data utility functions. The numerical examples demonstrate that big data and IoT service provider can achieve the maximum profit through the proposed market model.