With the increasing applications of UWB indoor positioning technologies in industrial areas, to further enhance the positioning precision, the UWB/IMU combination method (UICM) has been considered as one of the most effective solutions to reduce non-line-of-sight (NLOS) errors. However, most conversional UICMs suffer from a high probability of positioning failure due to uncontrollable and cumulative errors from inertial measuring units (IMU). Hence, to address this issue, we improved the extended Kalman filter (EKF) algorithm of an indoor positioning model based on UWB/IMU tight combination with a double-loop error self-correction. Compared with conventional UICMs, this improved model consists of new modules for fixing time desynchronization, optimizing the threshold setting for UWB ranging, data fusion in NLOS, and double-loop error estimation, sequentially. Further, systematic error controllability analysis proved that the proposed model could satisfy the controllability of UWB indoor positioning systems. To validate this improved UICM, inevitable obstacles and atmospheric interferences were regarded as Gaussian white noises to verify its environmental adaptability. Finally, the experimental results showed that this proposed model outperformed the state-of-the-art UWB-based positioning models with a maximum deviation of 0.232 m (reduced by 83.93% compared to a pure UWB model and 43.14% compared to the conventional UWB/IMU model) and standard deviation of 0.09981 m (reduced by 88.35% compared to a pure UWB model and 22.21% compared to the conventional UWB-IMU model).

In cloud manufacturing, the dynamic characteristics of manufacturing services increase the complexity of supply and demand matching. A peer effect between service demanders and a synergy effect between service providers affect the final matching result. This paper proposes a two-sided matching model of service providers and demanders considering the peer and synergy effects. First, a dynamic evaluation index system is proposed, and the index weight of service providers and demanders is determined based on the fuzzy analytical hierarchy process. Second, a two-sided matching model is established based on peer and synergy effects. Finally, the proposed method is validated by the collaborative manufacturing of hydraulic cylinders. The results reveal that the model effectively matches service demanders with service providers, improving the overall satisfaction of both parties.

Wireless propagation model is of great significance for accurate 5G network deployment. Based on the data set provided by Huawei ModelArts platform, this paper uses the typical method of feature engineering to establish the intelligent wireless propagation model based on back propagation (BP) neural network. The first is feature design, which has designed 12 features from the traditional Cost 231-Hata empirical model and geometric location. Considering the coupling relationship between the features, the angle and distance related features are dimensioned. The second is data processing, using logarithmic transformation, One-Hot coding, missing value processing, etc. Then there is the feature selection, and the final feature set of the model is extracted by the standard deviation, the quantized value of the Pearson coefficient and the feature-reference signal receiving power (RSRP) scatter plot. Finally, the prediction model is built and verified. In order to compare the superiority of the designed features, this paper established and tested three BP neural network models with the same network structure but different inputs to predict the RSRP of different geographical locations. The experimental results verified the superiority of the designed feature set. And a suitable wireless propagation machine learning model was obtained.

The Internet-of-Things (IoT) technology realizes deep integration of the physical process and production information in a manufacturing workshop through the real-time acquisition of data and seamless interaction of equipment, showing valuable academic and applicable potential in upgrading the workshop environment and eliminating the effect of dynamics. This brings a new opportunity to increase workshop productivity. However, how to realize active perception, dynamic optimization, and real-time regulation of the manufacturing process based on IoT technology is a research hotspot facing technical and theoretical challenges. To address these issues, this study constructs a multiagent-based dynamic scheduling (MADS) workshop IoT architecture by embedding advanced ultrawide band (UWB) and 5th-generation (5G) communication technologies. Differing from conventional scheduling models, the proposed model optimally assigns processes to machines by designing a resource scheduling agency (RS Agency). The $NP$ -complete of the multiobjective optimization dynamic job-shop scheduling problem is proved and reduced to a cooperative bargaining game negotiation model (CBGNM) to rationalize the allocation of workshop resources. Then, a multiobjective weights tuning scheme enables effective and efficient responses to workshop exceptions. The performance comparisons with public data of the Kacem $8\times 8$ benchmark verify that the CBGNM has better results than the conventional scheduling methods and reaches maximum optimization of 41.7%, 39.3%, 47.2%, 32.9%, 54.5%, and 38.4% regarding makespan, critical machine workload, total processing time, fairness index, total energy consumption, and comprehensive index, respectively. The numerical experiments show that the game players have no significant coupling with their joint payoff and validate that their bargaining power varies with abnormal events.