With the development of the Internet of underwater things (IoUT), two critical problems have been prominent, i.e. the energy constraint of underwater devices and large demand for data collection. In this paper, we introduce an autonomous underwater vehicle (AUV) aided underwater acoustic sensor networks (UWSNs) to solve these problems. To improve the performance of UWSNs, we formulate an optimization problem to maximize the energy consumption utility, which is defined to balance the energy consumption and network throughput. To solve this optimization problem, we decompose it into four parts. Firstly, due to the constraint of communication distance, we construct a cluster-based network and formulate the selection of cluster heads as a maximal clique problem (MCP). Secondly, the clustering algorithm is proposed. Thirdly, we design a novel media access control (MAC) protocol to coordinate data transmission between AUV and cluster heads, among intra-cluster nodes, as well as among inter-cluster nodes. Finally, path planning of AUV is formulated as a traveling salesman problem to minimize AUV travel time. Based on the above analysis, two algorithms, namely AUV-aided energy-efficient data collection (AEEDCO) and approximate AUV-aided energy-efficient data collection (AEEDCO-A), are developed accordingly. Simulation results show that the proposed algorithms perform well and are very promising in UWSNs with demand for large-scale communication, large system capacity, long term monitoring, and high data traffic load.

The accuracy of location information, mainly provided by the Global Positioning System (GPS) sensor, is critical for internet of things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT) based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods.

Integration of the global positioning system (GPS) with other sensors to improve positioning and navigation is a viable solution for intelligent vehicles. In particular, integrating GPS with an inertial measurement unit (IMU) and vision sensor is a common consideration for enhancing the performance of vehicle navigation systems. However, the accuracy and availability of GPS are seriously degraded by Non-line-of-sight (NLOS) reception and multipath interference in urban environments. Combining the unreliable GPS positioning results with other sensors will degrade the performance of the integrated navigation system. Therefore, we propose a novel measurement quality control algorithm for a loosely coupled GPS, IMU, and stereo visual odometer (VO) integration for urban vehicle navigation. In the proposed algorithm, an adaptive fusion strategy based on geometric dilution of precision (GDOP) and a measurement fault detection and exclusion (FDE) based on the K-means clustering considering predicted pseudorange errors are employed to mitigate NLOS/multipath effects. The validation of the proposed integrated solution incorporating the measurement quality control is carried out. The results show that the algorithm achieves positioning accuracy in terms of 3D root mean square error (RMSE) of 2.85 m and 7.17 m, in mid and urban canyons, respectively. This level of performance is superior to that of the traditional GPS/IMU/VO integration. The corresponding improvements are 49.0% in the mid urban canyon, and 79.6% in the deep urban canyon respectively.

Global Navigation Satellite Systems (GNSS) integrated with multiple sensors have been widely applied in many Intelligent Transport Systems (ITS). Intelligent vehicles, increasingly a key component of the future transport system, require high-performance positioning, navigation and timing (PNT) technologies. This cannot be achieved by current 2-dimnsional systems. This is because accurate comprehensive state information (time, position, derivatives and attitude) is required to estimate amongst others, the vehicle’s power usage as well as to enable precise environmental mapping based on accurate state of the relevant sensors and path planning including in complex multi-level intersections. Because of the multipath effects and signal interruption in urban environments, comprehensive vehicle state estimation is not always available at the required level of performance. To address this issue, we propose an effective method to integrate the Inertial Measurement Unit (IMU), Global Positioning System (GPS) and monocular Visual Odometry (VO) for urban vehicle navigation. A robust Extended Kalman Filter (EKF) based two-step integration algorithm is developed with a non-holonomic constraint (NHC). In particular, the NHC is not only applied on the offline VO error modelling process, but also on the online sensor fusion process to improve the 3D vehicle state estimation. The proposed IMU/GPS/VO integration scheme is tested with various sensor levels in different urban environments. The results show that the proposed IMU/GPS/VO fusion algorithm could deliver a 3D RMSE of 3.285m, which outperforms the other conventional candidate fusion schemes in the noisy GNSS urban areas. The further test in the urban with outages has demonstrated that the proposed algorithm delivers an overall 3D RMSE of 1.290m, 0.073m/s and 0.486 degrees, in terms of positioning, velocity and attitude, respectively. It is also demonstrated that the proposed low-cost VO integration with IMU/GPS could achieve similar performance with the high-cost odometer based integration in deep urban areas but with advantages of higher flexibility and lower cost.