This paper considers a market for Internet of Things (IoT) data that is used to train machine learning models. The data is supplied to the market platform through a network and the price of the 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 devices 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：本文探讨了用于培训机器学习模型的Tings(IoT)互联网数据市场。数据通过网络提供给市场平台,数据价格以机器学习模型带来的价值为基础加以控制。我们探讨了游戏理论环境中数据的相关性属性,以便最终为强调装置和市场互利的数据交易机制找到一个简化的分布式解决方案。关键建议是为市场制定高效算法,共同解决参与中的可用性和异质性的挑战,以及信息在IoT网络中数据交换的信任转移和经济价值的转移。拟议方法通过加强相关数据装置之间的合作机会来建立数据市场,以避免信息泄漏。我们为此,我们开发了一个网络范围的优化问题,使类似数据类型的IoT设备之间的联盟的社会价值最大化;同时,它最大限度地减少网络外差成本,即数据相关性导致的信息渗漏的影响以及机会成本。最后,我们揭示了所拟订的问题的结构,即与相关数据相关设备之间的协作机会,以避免信息泄漏。我们为此开发了一个网络范围优化的问题,从而最大限度地增加类似数据类型的IoT装置之间的联合值;同时,将网络外差成本降低,即由于数据关联性以及机会成本。最后,我们揭示了所拟订的问题的结构结构,作为分布式的联盟联盟结果,在Simmeralalalal-ha 上,并分解了我们提议的Syal-ha 。