Cache-enabled UAVs are widely used as edge servers to provide on-demand content services for ground users. Most of prior works deploy UAVs based on user locations and explore the benefits of UAV caching under static content library. Motivated by this observation, we further consider heterogeneous user activity level and dynamic content library. We formulate a joint optimization problem of UAV deployment and content placement to minimize the average request delay, which is proved to be NP-hard. To tackle this problem efficiently, we decompose this optimization problem into two sub-problems, UAV deployment problem and content placement problem. Specifically, we first jointly consider user activity level and user locations for UAV deployment using weighted K-means method. After the UAV deployment, we propose a Q-learning algorithm to learn the content placement directly, rather than estimating the content popularity first, which is not effective when content library is dynamically changing. The simulation results show that the proposed algorithm outperforms the benchmark algorithms when dynamic content library and heterogeneous user activity level are considered.

Mobile crowdsensing (MCS) is a new paradigm of sensing by taking advantage of the rich embedded sensors of mobile user devices. However, the traditional server-client MCS architecture often suffers from the high operational cost on the centralized server (e.g., for storing and processing massive data), hence the poor scalability. Peer-to-peer (P2P) data sharing can effectively reduce the server's cost by leveraging the user devices' computation and storage resources. In this work, we propose a novel P2P-based MCS architecture, where the sensing data is saved and processed in user devices locally and shared among users in a P2P manner. To provide necessary incentives for users in such a system, we propose a quality-aware data sharing market, where the users who sense data can sell data to others who request data but not want to sense the data by themselves. We analyze the user behavior dynamics from the game-theoretic perspective, and characterize the existence and uniqueness of the game equilibrium. We further propose best response iterative algorithms to reach the equilibrium with provable convergence. Our simulations show that the P2P data sharing can greatly improve the social welfare, especially in the model with a high transmission cost and a low trading price.

Mobile Edge Computing (MEC) is a promising computing scheme to support computation-intensive AI applications in vehicular networks, by enabling vehicles to offload computation tasks to edge computing servers deployed on Road Side Units (RSUs) that approximate to them. In this work, we consider an MEC-enabled Vehicular Edge Network (VEN), where each vehicle can offload tasks to edge/cloud computing servers via vehicle-to-infrastructure (V2I) links or to other end-vehicles via vehicle-to-vehicle (V2V) links. In such a cloud-edge-end collaborative offloading scenario, we focus on the joint task offloading, scheduling, and resource allocation problem for vehicles, which is challenging due to the online and asynchronous decision-making requirement for each task. To solve the problem, we propose a Multi-layer Deep Reinforcement Learning (DRL) based approach, where each vehicle constructs and trains three modules to make different layers’ decisions: (i) Offloading Module (first layer), determining whether to offload each task, by using the Dueling and Double Deep Q-Network (D3QN) framework; (ii) Scheduling Module (second layer), determining where and how to offload each task in the offloading queues, together with the transmission power, by using the Parameterized Deep Q-Network (PDQN) framework; and (iii) Computing Module (third layer), determining how much computing resource to be allocated for each task in the computation queues, by using classic optimization techniques. We provide the detailed algorithm design and perform extensive simulations to evaluate its performance. Simulation results show that our proposed algorithm outperforms the existing algorithms in the literature, and can reduce the average cost by 25.86%–72.51% and increase the average satisfaction rate by 3.48%-90.53%.

In modern transportation systems, the road intersections where multiple roads merge usually become the traffic bottleneck. Existing coordination solutions such as the traffic signs tend to be ineffective. In order to exploit the full potential of the road infrastructure, we try to address the optimal vehicle coordination problem, by which the traffic throughput can be maximized. We first formulate it as a general centralized collision-free traffic scheduling framework. The involved vehicles could cooperate via instructions from the coordination center, by solving the essential non-convex coordination problem. Aiming at the dynamic arrival of vehicles, a corresponding coordination strategy is thus proposed to achieve better quality of service (QoS), including the traffic throughput and delay. The queue stability on each road can be guaranteed using the Rate Stability Theorem and Lyapunov Theorem. We then try to extend the proposed intelligent coordination framework to the multi-collision-set one, by which the traffic throughput can be significantly improved. A spatial domain based solution is given to solve the multi-collision-set coordination problem. Due to the essential non-convex formulation, high accuracy linearization approximate algorithms are also provided. In the numerical results, we show the vehicle maneuvers and statistical coordination time as the performance evaluation. They validate our analysis and show the performance advantages achieved by the proposed framework.

The backscatter and active RF radios can complement each other and bring potential performance gain. In this paper, we envision a dual-mode radio structure that allows each device to make smart decisions on mode switch between backscatter communications (i.e., the passive mode) or RF communications (i.e., the active mode), according to the channel and energy conditions. The flexibility in mode switching also makes it more complicated for transmission control and network optimization. To exploit the radio diversity gain, we consider a wireless powered device-to-device network of hybrid radios and propose a sum throughput maximization by jointly optimizing energy beamforming and transmission scheduling in two radio modes. We further exploit the user cooperation gain by allowing the passive radios to relay for the active radios. As such, the sum throughput maximization is reformulated into a non-convex. We first present a sub-optimal algorithm based on successive convex approximation, which optimizes the relays’ reflection coefficients by iteratively solving semi-definite programs. We also devise a set of heuristic algorithms with reduced computational complexity, which are shown to significantly improve the sum throughput and amenable for practical implementation.

Crowdsourced wireless community network enables the individual users to share their private Wi-Fi access points (APs) with each other, hence it can achieve a large Wi-Fi coverage with a small deployment cost via crowdsourcing. This paper presents a novel contract-based incentive framework to incentivize such a Wi-Fi network crowdsourcing under incomplete information (where each user has certain private information such as mobility pattern and Wi-Fi access quality). In the proposed framework, the network operator designs and offers a set of contract items to users, each consisting of a Wi-Fi access price (that a user can charge others for accessing his AP) and a subscription fee (that a user needs to pay the operator for joining the community). Different from the existing contracts in the literature, in our contract model, each user's best choice depends not only on his private information but also on other user's choices. This greatly complicates the contract design, as the operator needs to analyze the equilibrium choices of all users, rather than the best choice of each single user. We first derive the feasible contract that guarantees the user's truthful information disclosure based on the equilibrium analysis of the user choice, and then derive the optimal (and feasible) contract that yields a maximal profit for the operator. Our analysis shows that a user who provides a higher Wi-Fi access quality is more likely to choose a higher Wi-Fi access price and subscription fee, regardless of the user mobility pattern. Simulation results further show that when increasing the average Wi-Fi access quality of users, the operator can gain more profit, but (counter-intuitively) offer lower Wi-Fi access prices and subscription fees for users.