Smart city emerges a promising paradigm for improving operational efficiency of city and comfort of people. With embedded multi-sensors, Unmanned Aerial Vehicles (UAVs) hold great potential for collecting sensing data and providing social services in smart city. However, due to the limited battery lifetime and processing capacities of UAVs, the efficient offloading scheme of UAVs is urgently needed in smart city. Therefore, in this article, a vehicle-assisted computing offloading architecture for UAVs is proposed to improve offloading efficiency by harnessing the moving vehicles in smart city. We first develop an offloading model for UAVs to determine the offloading strategy. Next, to select the optimal vehicles for offloading, we formulate a matching scheme based on the preference lists of UAVs and vehicles to derive the optimal matching between UAVs and vehicles. After that, to improve the offloading efficiency and maximize the utilities of UAVs and vehicles, the transaction process of computing data between UAVs and vehicles is modeled as a bargaining game. Moreover, an offloading algorithm for UAVs and vehicles is proposed to obtain the optimal strategy. Finally, simulations are performed to validate the efficiency of the proposed offloading scheme. The results demonstrate that the proposed offloading scheme can significantly save resource and improve the utilities of UAVs and vehicles.

Vehicle Assisted Computing Offloading for Unmanned Aerial Vehicles in Smart City

For large UAV networks, timely communication is needed to accomplish a series of missions accurately and effectively. The relay technology will play an important role in UAV networks by helping drones communicate with long-distance drones, which solves the problem of the limited transmission power of drones. In this article, relay selection is seen as the entry point to improve the performance of self-organizing networks with multiple optimizing factors. Different from ground relay models, relay selection in UAV communication networks presents new challenges, including heterogeneous, dynamic, dense and limited information characteristics. More effective schemes with distributed, fast, robust and scalable features are required to solve the optimizing problem. After discussing challenges and requirements, we find that the matching game is suitable to model the complex relay model. The advantages of the matching game in self-organizing UAV communications are discussed. We provide extensive applications of matching markets, and then propose a novel classification of matching game which focuses on the competitive relationship between players. Specifically, basic preliminary models are presented and future research directions of matching game in UAV relay models are discussed.

Self-Organizing Relay Selection in UAV Communication Networks: A Matching Game Perspective

In this paper, we study the distributed relay assignment problem in multi-channel multi-radio unmanned aerial vehicle (UAV) communication networks. Multi-UAVs are driven by the overall task and fly in certain formation, where UAVs with different tasks have various transmission requirements. Source UAVs equipped with multi-radio can select more than one relay radios to achieve high data rate, and each relay radio can be shared by multiple source UAVs. We construct distributed game models to promote the global transmission performance by self-organizing coordination among UAVs. Specifically, the channel competition relationship between relay UAVs is modeled as a congestion game model, while the task-driven relay selection among UAVs is modeled as a many-to-many matching market without substitutability. With the proposed game models, the optimizing of local optimized process will lead to the improvement of global transmission results. After that, we design algorithms for the stable and changeable topology structures, respectively. Based on the given formation shape of UAVs, a learning matching algorithm is proposed to reach the optimum result with a large probability. A fast potential matching algorithm is propose to deal with the topological change of UAV networks. We prove that two proposed algorithms can both achieve the stable matching results. Simulation results show that the proposed relay assignment approaches yield good performances in terms of the global transmission satisfaction and fairness. Particularly, the result of the learning algorithm is close to the global optimum and the fast potential matching approach is robust to the perturbation of UAV networks.

Task-Driven Relay Assignment in Distributed UAV Communication Networks

Unmanned aerial vehicles (UAVs) can be deployed in the air to provide high probabilities of line of sight (LoS) transmission, thus UAVs bring much gain for wireless communication systems. In this paper, we study a UAVs-aided self-organized device-to-device (D2D) network. Relay deployment, channel allocation and relay assignment are jointly optimized, aiming to maximize the capacity of the relay network. On account of the coupled relationship between the three optimization variables, an alternating optimization approach is proposed to solve this problem. The original problem is divided into two sub-problems. The first one is that of optimizing the channel allocation and relay assignment with fixed relay deployment. Considering without central controller, a reinforcement learning algorithm is proposed to solve this sub-problem. The second sub-problem is that of optimizing the relay deployment with fixed channel allocation and relay assignment. Assuming no knowledge of channel model and exact positions of the communication nodes, an online learning algorithm based on real-time capacity is proposed to solve this sub-problem. By solving the two sub-problems alternately and iteratively, the original problem is finally solved. Simulation results show that the UAVs-aided D2D network can achieve a high capacity via the joint optimization of relay deployment, channel allocation, and relay assignment.

Joint Optimization of Relay Deployment, Channel Allocation, and Relay Assignment for UAVs-Aided D2D Networks

For large unmanned aerial vehicle (UAV) networks, the timely communication is needed to accomplish a series of missions accurately and effectively. The relay technology will play an important role in UAV networks by helping drones communicating with long-distance drones, which solves the problem of the limited transmission power of drones. In this paper, the relay selection is seen as the entry point to improve the performance of self-organizing network with multiple optimizing factors. Different from the ground relay models, the relay selection in UAV communication networks presents new challenges, including heterogeneous, dynamic, dense and limited information characteristics. More effective schemes with distributed, fast, robust and scalable features are required to solve the optimizing problem. After discussing the challenges and requirements, we find that the matching game is suitable to model the complex relay model. The advantages of the matching game in self-organizing UAV communications are discussed. Moreover, we provide extensive applications of matching markets, and then propose a novel classification of matching game which focuses on the competitive relationship between players. Specifically, basic preliminary models are presented and some future research directions of matching game in UAV relay models are discussed.

In this paper, we investigate the problem of opportunistic UAV transmission in D2D communication networks. UAVs are supposed to help transmissions of D2D users when they are employed to perform flying missions with given trajectories. On one hand, users can select appropriate UAVs as real-time relays according to the topology in the sky at different moments. On the other hand, due to flight characteristics, UAVs can receive the uploading data when they are approaching transmitters, and then offload the data to corresponding receivers in the appropriate later time. Users need to select and adjust transmission modes dynamically, including multi-UAV selection, time allocation of data loading and offloading, as well as the competition of channel access. We design a hierarchical game model to analyze the complicated relationship among devices. Specifically, a predictable dynamic matching market is constructed to address the issue of UAV selection and time allocation, while the problem of channel access is studied by the congestion game. After that, distributed algorithms are proposed and the properties of convergence are discussed. Simulation results confirm that the effective opportunistic UAV transmission approach can improve the global network significantly, while unreasonable optimization approaches may lead to the decline of the transmission performance.

Opportunistic Utilization of Dynamic Multi-UAV in Device-to-Device Communication Networks

Based on matching game theory, this paper puts forward a novel-distributed relay-assignment approach to optimise the throughput satisfaction of source nodes, which is scarcely investigated in former works. The assignment problem is modelled as a matching market, where source nodes apply to relay nodes according to their throughput requirements, in the meantime, relay nodes obtain pay offs by assisting source nodes to transmit data. The proposed game appears in the category of matching games with both peer effects and dynamic quotas. The choice of each source node is related to not only the quality of channel gain but also the results of other source nodes choices. Dynamic quotas of matching game are considered because of the limited resource of relay nodes and dynamic requirements of source nodes, which adds difficulty of relay assignment. To address this issue, a novel self-organising matching approach is proposed. Then, the proposed algorithm is proved to converge to a two-sided stable matching between source nodes and relay nodes. Shown in the simulation results, the proposed distributed matching algorithm outperforms the existing schemes in terms of both global satisfaction and fairness within a reasonable convergence time. Copyright © 2016 John Wiley & Sons, Ltd.

Distributed satisfaction-aware relay assignment: a novel matching-game approach

In this paper, we tackle the comprehensive problem jointing channel access, relay selection, and available time assignment for distributed relay networks which includes multiple source nodes and relay nodes. Driven by various communication requirements, source nodes search for relay nodes to improve the quality of transmission. Due to the interaction relationship, source nodes have to compete for channels and time resource of relay nodes with other source devices. To promote the satisfaction performance among source nodes, we divide the optimization problems of channel access and relay selection into two sub-problems, respectively. Then the distributed hierarchical game models are constructed, in which the problem of channel access is modeled as a congestion game model, and relay selection jointing time assignment is modeled as a matching model with dynamic quotas. Combining two game models, a distributed hierarchical scheme is designed, which is shown to reach a stable result and the properties are studied. The simulation and experimental results show that the proposed distributed QoE-aware method has obvious performance improvement in terms of satisfaction, fairness, and convergence.

Distributed Channel Access, Relay Selection and Time Assignment for QoE-Aware Relay Networks

In this paper, we study the problem of distributed relay selection in wireless networks using a game theoretic approach. Specifically, we consider a system model where one relay node can be shared by multiple source-destination pairs. Our objective is to find the relay selections of source nodes to optimize the total capacity. The relay selection problem is formulated as a congestion game with player-specific payoff functions and the existence of Nash equilibrium (NE) is demonstrated. Then we propose a stochastic learning automata (SLA) based distributed relay selection approach to obtain the NE without information exchange among source nodes. Simulation results show that the proposed distributed relay selection approach achieves satisfactory performance, when compared with other solutions.

Kun Xu

Papers

Task-Driven Relay Assignment in Distributed UAV Communication Networks

Opportunistic Utilization of Dynamic Multi-UAV in Device-to-Device Communication Networks

Distributed satisfaction-aware relay assignment: a novel matching-game approach

Distributed Channel Access, Relay Selection and Time Assignment for QoE-Aware Relay Networks

A game theoretic learning solution for distributed relay selection on throughput optimization