Recently, many satellites are being launched for providing global internet broadband service to individual consumers. Since satellites and users could move separately, providing seamless connectivity has become one of the most important tasks for mobile satellite networks. Current handover methods are based on either signal strength or service time, however, due to the randomness of user terminal (UT) arrivals and the unbalanced traffic distribution for high-mobility satellite networks, the success rate can hardly be guaranteed. To this end, we propose a QoE-driven intelligent handover mechanism for user-centric mobile satellite networks, through which the access satellites can be selected by predicted service time and communication channel resources. Accordingly, to ensure the service duration, a spatial relationship coupling model is proposed to predict relative motion pattern between UT and satellites; To improve the effectiveness of handover, an available channel estimation model is then developed based on the mobility pattern of adjacent satellites. Finally, reinforcement learning is adopted to maximize the UT's Quality of Experience (QoE) through predicted handover factors. Simulation results show that the proposed handover mechanism outperforms the conventional methods in terms of handover times, handover success rate, and end-to-end delay.

Three-dimensional sound-speed distribution is essential for large-scale underwater acoustic applications due to its influence on the signal propagation trajectory. However, it is labor, energy, and time consuming to measure sound speed by traditional methods because of the weak system maneuverability. In this article, an autonomous-underwater-vehicle-assisted underwater sound-speed inversion framework that collaborates ray tracing and artificial intelligence model is proposed to quickly obtain 3-D sound-speed distribution through multicoordinate inversions. An autoencoding-translation neural network is proposed to establish the nonlinear relationship from signal propagation time to the sound-speed profile (SSP), and the inversion time can be shortened with once forward propagation through the model. Robustness could be improved by inverting error-resistant implicit features into the SSP through the proposed translating neural network, whereas the implicit features are extracted from the autoencoder by denoising reconstruction of the input time information. To solve the overfitting problem and extend the training data set, virtual SSPs based on sparse feature points of real SSPs are generated. Simulation results show that our approach can provide a reliable and instantaneous monitoring of 3-D sound-speed distribution.

Due to the inherent mobility and enhanced communication capability of the unmanned aerial vehicle (UAV), it is foreseen that effective UAV-based sensing schemes can achieve faster deployment and flexible observation as compared to the traditional fixed sensing systems. In many sensing applications, multiple geographically distributed tasks need to be executed while satisfying a stringent requirement on the transmission delay of the sensory data, especially for the time-critical sensing tasks, such as disaster rescue and environment monitoring. To minimize the overall mission completion time in such delay constrained multi-task UAV-based sensing applications, it is imperative to properly design the inter-task trajectory, the UAV-BS association, and the sensing order. Nonetheless, finding the optimal sensing scheme is highly non-trivial since these three sub-problems are tightly coupled and involve non-convex constraints. To overcome these challenges, firstly, a novel space pruning based trajectory search (SPTS) algorithm is proposed by exploiting the geometric characteristics of the optimal inter-task UAV trajectory, which can achieve a substantially faster convergence as compared to the Ployblock algorithm. Secondly, a novel optimal UAV-BS association (OUBA) algorithm is presented to quickly identify the best BS for each sensing task. Thirdly, by combining these two proposed algorithms with the Lin-Kernighan-Helsgaun (LKH) algorithm, a lower bound of the mission completion time can be efficiently computed and a near-optimal solution can be obtained. Numerical simulations demonstrate that significant performance improvement can be achieved by the proposed scheme.

Billions of devices are connected in the Internet of Things (IoT) based sensor networks and they continuously generate a large volume of data. In order to get access to specific data, which is crucial to enable a myriad of new intelligent applications, efficient information retrieval becomes an imminent need for IoT. However, sensor information in the physical world can be heterogeneous, high dimensional and voluminous due to the complex and dynamic environments. In this paper, we first investigate several IoT search scenarios and propose a uniform representation model for sensor information recordings. Four query models are designed to represent all possible information query styles. With these models, we develop an information retrieval architecture for IoT. In essence, an indexing mechanism called efficiency maximization and cost minimization (EMCM) is proposed to solve the property selection problem in the process of index construction and update. Meanwhile, a novel real-time grid R-tree (RtGR-tree) structure is designed to support historical and real-time search for spatiotemporal observation data. Simulation results based on realworld IoT datasets show that storage space is considerably reduced with the sensor model. Furthermore, the proposed indexing mechanisms can improve retrieval efficiency and accuracy, and ensure scalability for large-sized data simultaneously.

Aiming at traffic efficiency improvement, vehicle platoon becomes an essential driving technique to control the synchronous driving of connected and automated vehicles. Since the mature automated driving technology and cooperative willingness cannot be ensured for all vehicles, a mixed traffic paradigm that incorporates both cooperative vehicles (CVs) and non-cooperative vehicles (NCVs) will be inevitable. Due to the traffic hindrance and intermittent connections caused by the randomly distributed NCVs, traffic efficiency would be limited in the mixed traffic. Therefore, a hindrance-aware platoon formation scheme is proposed for the mixed traffic in this paper, which aims at coordinating CVs to maximize overall traffic velocity while minimize collision risk and fuel consumption. Specifically, a vehicle connection model is first established to construct the hindrance-aware relationships among vehicles, based on which, the intermittent connections of CVs are formulated as a mixed integer nonlinear programming (MINLP) problem. To reduce the search space, a novel cross-vehicle matrix is designed to represent the hindrances that each CV faces, then a traffic-maneuver-tree construction algorithm is proposed to search for feasible and hindrance-aware platoon formation connections. A length-dynamic inverse influence zone is constructed, then two decision indicators of platoon leader and follower are decoupled by evaluating leadership qualifications of all CVs. Given the hindrance-aware connections and leadership qualifications, a hindrance-aware platoon formation algorithm is proposed to jointly optimize the speed, safety and energy efficiency of CVs. To verify the effectiveness of the platoon formation mechanism, extensive simulations are conducted, and the results reveal that the road utilization, traffic capacity improvement and safety assurance in mixed traffic can be enhanced.

We design an ocean surface drifting buoy system based on an unmanned aerial vehicle (UAV)-enabled wireless powered relay network in which the UAV acts as mobile hybrid access point that broadcasts energy to all buoys in the downlink and forwards information from the buoys to a ship signal tower (ST) in the uplink. In order to maximize the resource allocation efficiency of the system, due to the different initial energy reserve of the buoys, a novel communication mode selection strategy is proposed. In the direct transmission mode (DT mode), an energy-sufficient buoy transmits information directly to the ST, and in the relay transmission mode (RT mode), an energy-insufficient buoy relays information to the ST through the UAV. By applying the block coordinate descent and successive convex optimization, a joint UAV trajectory and resource allocation algorithm is proposed to maximize the minimum throughput of the buoys to work in the RT mode. Simulation results show that the proposed algorithm can significantly improve the minimum throughput of the ocean surface drifting buoys.