Intelligent applications have greatly promoted the development of fog computing. The protocols have emerged diversely, causing the problems in the interworking between terminal devices, which has seriously hindered the development of fog computing. Based on the Open Connectivity Foundation (OCF) protocol, this paper proposes an interworking solution for intelligent devices, and designs an OCF-based multi-protocol gateway to access non-OCF services. The main functions are divided into three categories: manager, adapter and device agent. In part, the manager provides centralized management services, the adapter provides communication services between the client and the server device, and the device agent provides access services for the server device. The semantic conversion scheme of OCF protocol with other protocols is proposed in the device agent, which solves the problem of interoperability between OCF client device and non-OCF server devices. Finally, the OCF multi-protocol interworking gateway was built, and the semantic conversion between the OCF protocol, AllJoyn protocol, and the ZigBee protocol was implemented. The interoperability between the OCF client, AllJoyn device, and ZigBee device was verified.

In various practical applications, such as autonomous vehicle and unmanned aerial vehicle navigation, Global Navigation Satellite Systems (GNSSs) are commonly used for positioning. However, traditional GNSS positioning methods are often affected by disturbances due to external observational conditions. For instance, in areas with dense buildings, tree cover, or tunnels, GNSS signals may be obstructed, resulting in positioning failures or decreased accuracy. Therefore, improving the accuracy and stability of GNSS positioning in these complex environments is a critical concern. In this paper, we propose a novel multi-sensor fusion framework based on multi-frame residual optimization for GNSS/INS/LiDAR to address the challenges posed by complex satellite environments. Our system employs a novel residual detection and optimization method for continuous-time GNSS within keyframes. Specifically, we use rough pose measurements from LiDAR to extract keyframes for the global system. Within these keyframes, the multi-frame residuals of GNSS and IMU are estimated using the Median Absolute Deviation (MAD) and subsequently employed for the degradation detection and sliding window optimization of the GNSS. Building on this, we employ a two-stage factor graph optimization strategy, significantly improving positioning accuracy, especially in environments with limited GNSS signals. To validate the effectiveness of our approach, we assess the system’s performance on the publicly available UrbanLoco dataset and conduct experiments in real-world environments. The results demonstrate that our system can achieve continuous decimeter-level positioning accuracy in these complex environments, outperforming other related frameworks.

The rapid growth of internet of things (IoT) applications has driven the demand for high-accuracy indoor localization. However, existing mainstream solutions have distinct limitations: while sensors such as vision, inertial, and light detection and ranging (LiDAR) provide high-precision relative motion tracking, their inherent cumulative drift compromises long-term robustness; meanwhile, high-precision technologies like ultrawideband (UWB) rely on expensive, dedicated infrastructure deployment. The emergence of 5G networks offers a unique solution by utilizing their existing communication base stations as positioning anchors. Leveraging its wideband signals, 5G has the potential to provide the absolute position References needed to correct relative errors without significant additional hardware costs, thereby overcoming the limitations of the aforementioned techniques. However, 5G-based positioning technologies face challenges, such as multipath interference, clock synchronization errors, and degraded performance during terminal mobility. To address these limitations, we propose a novel, tightly coupled 5G positioning reference signal (PRS) and inertial measurement unit (IMU) fusion framework. The core of our approach is a two-stage architecture. First, a front-end signal error estimator (EE) models the joint estimation of clock offset and integer ambiguity as a mixed-integer nonlinear programming (MINLP) problem. Crucially, it leverages IMU-predicted kinematics as a strong physical constraint to effectively identify and suppress measurements contaminated by multipath. Subsequently, a back-end, tightly coupled error-state Kalman filter (ESKF) fuses the time-differenced carrier phase (TDCP) and time difference of arrival (TDOA) measurements from the front-end with inertial navigation data. Simulation results conducted under 3GPP standard scenarios demonstrate that the proposed framework achieves a positioning accuracy improvement of approximately 40% in centimeter-level accuracy, in line-of-sight (LOS) environments with tactical IMU equipment. This provides a promising solution for 5G-based indoor localization systems. In real-world indoor field tests characterized by multipath effects, our proposed EE-ESKF algorithm achieved a 3-D root mean square error (RMSE) of 0.55 m, which is 33.6% higher than the standard ESKF algorithm.