In unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system, rotary-wing UAV can be dispatched to fly close to ground terminals (GTs) to execute their offloaded tasks. This can extend GTs' computing capability and save their energy cost. However, to enhance the energy efficiency of UAV propulsion, ensure successful completion of each GT's mission, and maintain a stable UAV-GT uplinks, it is crucial to design a rational UAV 3D trajectory and mission offloading strategy. To address this joint problem, We further build a deep reinforcement learning (DRL)-based framework in the UAV-enabled MEC system, which applies Double Deep Q-learning (D-DQN) and convex optimization techniques to jointly work out the near-optimal UAV 3Dtrajectory and task offloading strategy among GTs in an online manner. With the proposed framework, the UAV-enabled MEC system can finish each GT task quickly and save the UAV mission time and propulsion energy effectively. Numerical results show that the DLR-based framework outperforms the benchmark solutions, e.g., In scenario 1, the throughput, the average data transferring rate through UAV-GT links, and the average energy efficiency of UAV compared to DQN are improved from 64.24Kb to 72.99Kb, 4.72Kbps to 20.75Kbps, 0.32bits/J to 0.36bits/J, respectively, and almost the same as the existing Travelling Salesman Problem (TSP) solution, making the energy efficient edge computing truly viable in UAV-enabled MEC system.

Micro-electro-mechanical-systems (MEMS) inertial measurement unit (IMU) outputs are corrupted by significant sensor errors. The navigation errors of a MEMS-based inertial navigation system will therefore accumulate very quickly over time. This requires aiding from other sensors such as Global Navigation Satellite Systems (GNSS). However, it will still remain a significant challenge in the presence of GNSS outages, which are typically in urban canopies. This paper proposed a rotary inertial navigation system (INS) to mitigate navigation errors caused by MEMS inertial sensor errors when external aiding information is not available. A rotary INS is an inertial navigator in which the IMU is installed on a rotation platform. Application of proper rotation schemes can effectively cancel and reduce sensor errors. A rotary INS has the potential to significantly increase the time period that INS can bridge GNSS outages and make MEMS IMU possible to maintain longer autonomous navigation performance when there is no external aiding. In this research, several IMU rotation schemes (rotation about X-, Y- and Z-axes) are analyzed to mitigate the navigation errors caused by MEMS IMU sensor errors. As the IMU rotation induces additional sensor errors, a calibration process is proposed to remove the induced errors. Tests are further conducted with two MEMS IMUs installed on a tri-axial rotation table to verify the error mitigation by IMU rotations.

Intelligent Transportation Systems (ITSs) can be applied to inform and incentivize travellers to help them make cognizant choices concerning their trip routes and transport modality use for their daily travel whilst achieving more sustainable societal and transport authority goals. However, in practice, it is challenging for an ITS to enable incentive generation that is context-driven and personalized, whilst supporting multi-dimensional travel goals. This is because an ITS has to address the situation where different travellers have different travel preferences and constraints for route and modality, in the face of dynamically-varying traffic conditions. Furthermore, personalized incentive generation also needs to dynamically achieve different travel goals from multiple travellers, in the face of their conducts being a mix of both competitive and cooperative behaviours. To address this challenge, a Rule-based Incentive Framework (RIF) is proposed in this paper that utilizes both decision tree and evolutionary game theory to process travel information and intelligently generate personalized incentives for travellers. The travel information processed includes travellers’ mobile patterns, travellers’ modality preferences and route traffic volume information. A series of MATLAB simulations of RIF was undertaken to validate RIF to show that it is potentially an effective way to incentivize travellers to change travel routes and modalities as an essential smart city service.

It has been identified that the inertial system is not a completely observable system in the absence of maneuvers. Although the velocity errors and the accelerometer bias in the vertical direction can be solely observable, other error states, including the attitude errors, the accelerometer biases in the east and north directions, and the gyro biases, are just jointly observable states with velocity measurements, which degrades the estimation accuracy of these error states. This paper proposes an innovative method to improve the system observability for a Micro-Electro-Mechanical-System (MEMS)-based Inertial Navigation System (INS) in the absence of maneuvers by rotary motions of the Inertial Measurement Unit (IMU). Three IMU rotation schemes, namely IMU continuous rotation about the X, Y and Z axes are employed. The observability is analyzed for the rotating system with a control-theoretic approach, and tests are also conducted based on a turntable to verify the improvements on the system observability by IMU rotations. Both theoretical analysis and the results indicate that the system observability is improved by proposed IMU rotations, the roll and pitch errors, the accelerometer biases in the east and north directions, the gyro biases become observable states in the absence of vehicle maneuvers. Although the azimuth error is still unobservable, the enhanced estimability of the gyro bias in the vertical direction can effectively mitigate the azimuth error accumulation.

How to limit the drifts of the navigation errors in an inertial navigation system (INS) with low-cost sensors is one of the main challenges for the land vehicle navigations. In this paper, we present a novel hybrid navigation strategy to integrate the Micro-Electric-Mechanic-systems (MEMS) INS, odometer (OD) and global navigation satellite systems (GNSS), with aim to enhance the positioning accuracy of the inertial system during GNSS outages. To accurately estimate the INS error states, the neural network (NN) is proposed to mimic the velocity of the navigation frame with the data from the MEMS INS, odometer, as well as the non-holonomic constraints (NHC). The long short-term memory (LSTM) NN is adopted in our approach due to its ability to adaptively use the data in the past. The road tests are conducted with two different MEMS IMUs to verify the proposed navigation strategy. Comparing to the traditional integrated MEMS INS/OD/GNSS system based on the extended Kalman filtering (EKF), our hybrid approach provides over 60% improvements in terms of the root mean square (RMS) and maximum horizontal position errors during GNSS outages.

Micro-electro-mechanical-system accelerometers and gyroscopes outputs are corrupted by significant random errors. The rotation modulation technique, which was mainly applied to ring laser gyro and fiber optical gyro, has been recently employed to compensate the micro-electro-mechanical-system inertial sensor errors in an inertial navigation system. As the fluctuating error processes in micro-electro-mechanical-system inertial sensors are difficult to be modeled or estimated, the inertial measurement unit rotating is an efficient way to automatically compensate for these random errors. This research applied the rotation modulation technique to the low-cost micro-electro-mechanical-system sensors and proposed an inertial system with rotating accelerometers and gyros for the land vehicle navigations. As previous research only studied the low-cost rotary inertial systems in the static conditions or simulation scenarios, the kinematic field tests are carried out to take the first look on the details and results of the low-cost rotary system for vehicular navigation. Accordingly, as the sensor random errors are very difficult to be dealt with, we conducted the analysis for the effect of inertial measurement unit rotation imposed on the random errors of the micro-electro-mechanical-system inertial sensors. Two different micro-electro-mechanical-system inertial measurement units are tested with a single-axis indexing table. The test results indicate the error mitigations are also related to the error characteristics of the inertial measurement unit as the navigation errors are more efficiently compensated for the micro-electro-mechanical-system inertial measurement unit that contains mainly the time-correlated noise than the one features significant white noise.