Artificial Intelligence of Things (AIoT) aims to build a self-learning, self-adaptive, and self-evolving Internet of Things ecosystem, which has facilitated many promising intelligent services. Data is an important foundational element for many applications. Establishing a well-designed trading mechanism to collect the necessary data from various sources is essential to realize the vision of AIoT. In this article we investigate the data trading incentive mechanism between multiproviders and multibuyers for AIoT. To address the two-sided dilemma, we develop a joint optimization game to maximize the payoff of all market participants. A two-layer Stackelberg evolutionary game theoretic framework is developed to divide the optimization problem into two subproblems: one for data pricing by providers and the other for purchasing decisions by buyers. The subproblem of optimal data pricing for providers is modeled as a noncooperative game. Providers utilize the game's equilibrium solution to dynamically modify their pricing strategies in response to a changing competitive environment and demanding. This is because buyers have limited information, their behaviors are modeled via evolutionary game. By encouraging data providers to take the buyers' evolutionary dynamics into account has the potential to overcome the myopia behaviors. The equilibrium solution is obtained via replicator dynamics. Extensive experiments demonstrate the efficacy and efficiency of the proposed hierarchical interaction framework. Overall, our results show the proposed Stackelberg evolutionary game framework establishes a desired data market and achieves higher long-term revenue for both sides of participants in the market. The hierarchical framework can effectively prompts data trading in the market.

Unmanned Aerial Vehicles (UAVs) are playing an increasingly critical role in Internet of Things (IoT), and with AI advancements, they are evolving into intelligent, multi-functional platforms supporting crowdsensing applications in modern IoT ecosystems. Given the dynamic nature of UAV-based IoT services, it is crucial to evaluate whether UAVs meet essential performance characteristics like flexibility, robustness, and stability. Flight data of UAVs is fundamental for assessing these characteristics, and effective data management is key to enabling timely, accurate performance evaluations. In this paper, we propose a Hierarchical Tree Model (HTM) specifically designed to accommodate the characteristics of UAVs-generated data. This model is supported by a hierarchical tree-based storage structure that optimizes the organization and retrieval of time-series data. To enable characteristic evaluation, we use tags to store characteristic information. To enable characteristic evaluation, we use tags to store characteristic information and apply encoding/decoding algorithms for flexible operation. We extend the SQL syntax tree of IoTDB with new syntax and semantics, enhancing the IoTDB parser and optimizer for tag integration and data operation. Experimental results show that our framework enables more efficient real-time evaluation of UAVs performance and better scalability for large deployments. Our findings highlight the potential of this time-series data management approach to support the real-time evaluation of UAVs characteristics, facilitating more informed decision-making and resource allocation in IoT-driven environments.

Considering the increasing complexity of application scenarios, the evaluation of the intelligence levels for unmanned swarms has attracted scholarly attention. However, the existing evaluation studies cannot suitably reflect the intelligence of unmanned swarms, and they rarely provide a specific evaluation process. By introducing the collective intelligent behavior model for unmanned swarms based on the collective Observe–Orient–Decide–Act (OODA) loop, this study constructs a comprehensive evaluation index system that can systematically reflect the overall intelligence of unmanned swarms in complex scenarios. Considering the fuzziness and randomness in the processes of weight calculation and level evaluation, this study proposes a comprehensive evaluation method of the intelligence levels for unmanned swarms based on a group extension cloud model. This method calculates the weights of various evaluation indexes at different layers by adopting the group extension analytic hierarchy process. Moreover, it obtains the comprehensive evaluation conclusion of the intelligence levels for unmanned swarms by adopting the cloud model. Applying the proposed method, this study evaluates two specific types of unmanned aerial vehicle swarms. The results show that the proposed method can more flexibly and accurately evaluate the intelligence levels of unmanned swarms than the previous fully qualitative methods.