The intelligent internet of things (IIoTs) introduces new paradigms that merge cutting-edge technologies across internet of things and Artificial Intelligence (AI). One notable feature of IIoTs is the pervasive involvement of devices belonging to various participants, such as data contributors, computing resource suppliers and even the service subscribers, e.g. different manufacturers in Industrial IoTs. Data trading takes heterogeny of participants’ background and behaviors into account and is a promising approach for facilitating the development of IIoT systems. Data trading provides relational and reliable platforms for data and resource exchange among diverse participants, ranging from smart home users to large logistic suppliers. Trading strategies can be combined with other techniques such as edge computing and federated learning to significantly improve the functionality, efficiency and effectiveness of IIoTs.

However, the design of data trading in IIoTs is still in its infancy despite the known benefits of this technology and there are several barriers which limit the widespread involvement of participants. Firstly, the commodities in IIoTs are heterogeneous, however current solutions tend only to focus on datasets. In actuality, the commodies in IIoTs can also include intermediate results of participants and computing and sensing capabilities. Secondly, data trading may take place within multiple components of IIoTs. Data sensing and collection, in-network data processing and the AI model training all request the involvement of participants, which all potentially request the trading events. Thirdly, ownership, trustworthiness and bonus sharing are also important aspects of IIoTs. For example, the road traffic forecasting model is initialized by IIoT system owners but is trained by independent participants. Furthermore, it is difficult to decide whether participants should pay for the service. In general, data trading for IIoTs should involve a variety of commodities, fully cover the main components of IIoTs and consider critical factors such as rationality, fairness, trustworthiness, privacy preservation, efficiency, data and model utilities. These requirement exceed the design of current IoT data trading frameworks, in which a market is usually created and sensing datasets are set as trading products.

This Special Issue calls for novel methods, techniques and applications for data trading in IIoTs. In addition, this Special Issue seeks solutions for the many fundamental issues in the data processing of IIoTs We invite scholars, researchers and engineers to share pioneering research which contributes to the development of the data trading process and the whole design of IIoTs. Original research and review papers are welcome.

Potential topics include but are not limited to the following:

• Architectures and frameworks for data trading in IIoTs
• Pricing mechanisms for data, intermediate results, resources in IIoTs
• Data procurement in IIoTs
• Incentive mechanisms for data trading in IIoTs
• Budget-efficient training strategies for AI model learning in IIoTs
• Data privacy and security for data trading in IIoTs
• Trustworthiness and authentication for data trading in IIoTs
• Ownership and bonus sharing for data trading in IIoTs
• Game theory for data trading in IIoTs
• Network design for reliable data trading in IIoTs
• AI model design and building in data-trading-based IIoTs
• Service subscribing and executing in IIoTs
• Management of data, models and services for IIoTs
• Blockchains for data trading in IIoTs
• Federated learning with data trading in IIoTs