This paper considers a market for trading Internet of Things (IoT) data that is used to train machine learning models. The data, either raw or processed, is supplied to the market platform through a network and the price of such data is controlled based on the value it brings to the machine learning model. We explore the correlation property of data in a game-theoretical setting to eventually derive a simplified distributed solution for a data trading mechanism that emphasizes the mutual benefit of devices and the market. The key proposal is an efficient algorithm for markets that jointly addresses the challenges of availability and heterogeneity in participation, as well as the transfer of trust and the economic value of data exchange in IoT networks. The proposed approach establishes the data market by reinforcing collaboration opportunities between device with correlated data to avoid information leakage. Therein, we develop a network-wide optimization problem that maximizes the social value of coalition among the IoT devices of similar data types; at the same time, it minimizes the cost due to network externalities, i.e., the impact of information leakage due to data correlation, as well as the opportunity costs. Finally, we reveal the structure of the formulated problem as a distributed coalition game and solve it following the simplified split-and-merge algorithm. Simulation results show the efficacy of our proposed mechanism design toward a trusted IoT data market, with up to 32.72% gain in the average payoff for each seller.

翻译：本文考虑一个用于交易物联网数据的市场，这些数据被用于训练机器学习模型。数据（原始或经过处理）通过网络供应给市场平台，其价格根据对机器学习模型的价值进行控制。我们在博弈论框架下探究数据的相关性属性，最终推导出一种简化的分布式数据交易机制，该机制强调设备与市场的互利性。核心方案是一种高效的市场算法，它联合解决了参与方的可用性与异质性挑战，以及物联网网络中信任传递和数据交换的经济价值问题。所提出的方法通过加强具有相关数据的设备之间的协作机会来建立数据市场，从而避免信息泄露。在此过程中，我们构建了一个网络级优化问题，在最大化同类数据类型的物联网设备联盟社会价值的同时，最小化由网络外部性（即数据相关性导致的信息泄露影响）以及机会成本所引发的成本。最后，我们揭示了所构建问题的分布式联盟博弈结构，并采用简化的分裂-合并算法进行求解。仿真结果表明，我们提出的机制设计能够有效构建可信的物联网数据市场，每个卖家的平均收益提升高达32.72%。