Extensive delay-sensitive and computation-intensive tasks are involved in emerging vehicular applications. These tasks can hardly be all processed by the resource constrained vehicle alone, nor fully offloaded to edge facilities (like road side units) due to their incomplete coverage. To this end, we refer to the new paradigm of vehicular collaborative edge computing (VCEC) and make the best use of vehicles’ idle and redundant resources for energy consumption reduction within the VCEC system. To realize this target, we are faced with several nontrivial challenges, including short-term decision making coupled with long-term queue delay constraints, information uncertainty, and task offloading conflicts. Accordingly, we apply Lyapunov optimization to decouple the original problem into three sub-problems and then tackle them one by one: the first sub-problem is resolved by Lagrange multiplier method; the second is handled by UCB learning-matching approach; the third is addressed by a carefully designed greedy method. Scenarios without volatility and real-world road topology with realistic vehicular traffics are utilized to evaluate the proposed solution. Results from extensive numerical simulations demonstrate that our solution can achieve superior performances compared with the benchmark methods, in terms of energy consumption, learning regret, task backlog, and end-to-end delay.

The Space-Air-Ground Power Internet of Things (SAG-PIoT) can meet the communication needs of Power Internet of Things (PIoT) devices in localities with insufficient ground base station coverage. Considering that the battery capacity of PIoT devices is limited and difficult to be replaced, we use unmanned aerial vehicles (UAVs) to assist wireless power transmission (WPT) in the SAG-PIoT to ensure the network service quality and safe and stable operation of PIoT. In this paper, we put forward a novel WPT enabled SAG-PIoT architecture, based on which we investigate the dynamic task offloading and resource scheduling, and propose a joint online optimization algorithm for task assignment, local computing resource allocation, association control and UAV computing resource allocation to minimize the long-term time-averaged network operation cost. Since the coupling of task offloading and resource scheduling in the long-term stochastic optimization problem, it is decoupled into three subproblems to be solved separately using Lyapunov optimization. Moreover, we analyze the stability and sustainability of the proposed algorithm and reveal the tradeoff between the network stability and optimal network operation cost. Simulations show that the proposed algorithm has advantageous performances with respect of network stability, energy consumption and network operation cost compared to other benchmark algorithms.

Aerial platform-based network has been invoked as an appealing assistance for terrestrial cellular networks. Caching at edge UAVs is an effective emerging solution for relieving the wireless backhaul congestion and reducing content fetching latency for hotspot areas. This paper investigates a non-orthogonal multiple access (NOMA)-enabled multi-UAV collaborative caching network, where the multimedia contents are delivered from UAVs to ground users. UAVs cache popular contents and collaboratively share with each other. The optimization problem of joint caching decision, 3D trajectory planning and spectrum resource allocation is formulated for minimizing the system content retrieving delay. It is an NP-hard mixed integer nonconvex optimization issue with extra difficulty that the content popularity is highly dynamic. To tackle this challenging problem, we decouple the original issue into two subproblems: (1) UAV collaborative caching decision making and (2) joint optimization of UAV trajectory planning, power allocation, and channel reusing. We develop a multi-agent proximal policy optimization (MAPPO)-based algorithm for the former to achieve the maximum weighted content hit ratio and propose a matching-DRL solution for the latter. Numerical evaluations confirm that the proposed approach is capable of rapid convergence and achieves superior performance in terms of content hit ratio, content retrieving delay, and system throughput compared to other benchmark algorithms.

UAV-based air-ground integrated computing networks (AGIN) have gained significant traction in remote areas for the Power Internet of Things (PIoT). This paper considers an AGIN-PIoT, where computing tasks generated by ground PIoT devices are offloaded to aerial UAVs that perform edge computing. Jointly optimizing task offloading and UAV trajectory poses challenges such as many decision variables, information uncertainty, and long-term queue delay constraints. Due to the limited battery capacity of PIoT devices and UAVs, our objective is to minimize system energy consumption under long-term queue delay constraints by jointly optimizing task offloading, trajectory planning, and computing resource assignment. In light of Lyapunov optimization, we decompose the original challenging optimization problem into two sub-problems: (1) task offloading and UAV trajectory planning and (2) aerial edge resource allocation. Accordingly, we develop a multi-agent deep reinforcement learning-based algorithm called AGIN-MADDPG for the former to achieve the maximum accumulative reward and propose a greedy solution for the latter. Extensive experiments and numerical results demonstrate that our approach can avoid the problem of gradient vanishing and outperforms other benchmark methods in terms of power consumption, task backlog, queue delay, and system throughput.

By combining information communication technology with power grid, the smart grid-oriented Power Internet of Things (PIoT) has become a critical technology to guarantee the safe and reliable power grid operation and improve system energy efficiency. Nevertheless, PIoT devices have only limited communication and computing resources since they are mostly deployed in remote areas that may be out of service coverage of existing terrestrial 5G networks. To overcome the resource limitation, we leverage Air-Ground Integrated C-NOMA Heterogeneous PIoT Networks (PAGIC HetNets), and study the core challenges in PAGIC HetNets. As PIoT devices are normally powered by battery, we aim at minimizing the energy consumption of PIoT devices and thoroughly investigate the problem of task offloading and resource allocation with minimal energy consumption. This problem belongs to a mixed integer nonlinear programming (MINLP) with extra difficulty that the long-term queuing delay and short-term constraints are coupled. To tackle the difficulty, we use Lyapunov optimization to transform this hard problem into three subproblems. The first subproblem is task splitting and local computing resource assignment at the PAGIC user side, which we solve with the Lagrangian multiplier method. The second subproblem is queue-aware channel reusing, and matching theory is adopted to solve it. The third subproblem is optimizing the aerial server resource allocation, for which we propose a greedy-based solution. Numerical simulations demonstrate that our approach can obtain excellent performance in terms of energy consumption, spectrum efficiency, task backlog, and queuing delay with lower complexity compared with several benchmark methods.

Since the existing terrestrial fifth generation (5G) network has limited coverage, it is difficult to meet the growing demand for seamless network connection. Meanwhile, current network resource allocation methods mainly research on how to improve system performance only from the perspective of resource utilization, but rarely take users’ specific needs for network content into consideration. This brings severe challenges to efficient network service and flexible resource allocation. Therefore, we construct the content service-oriented resource allocation model for space–air–ground integrated sixth generation networks (SAGIN 6G), and formulate the three-sided matching issue among the space–air–ground integrated network equipment (SAGINE), content sources, and users. In this model, users request to establish connection with SAGIN which forwards users’ request to content service provider (CSP). CSP manages the creation of content data, and finally returns the requested content to users through SAGIN. For the content service-oriented resource allocation in SAGIN, finding the optimal stable three-sided matching with the largest cardinality is an NP-complete problem. Therefore, to efficiently solve the above issue, we design some reasonable restrictions and convert it to a restricted three-sided matching problem with size and cycle preferences. We further develop the content-oriented resource allocation algorithm (COR2A) and the user-oriented resource allocation algorithm (UOR2A) in a distributed manner. Extensive simulations verify our approach outperforms traditional benchmark resource allocation schemes in terms of system throughput, CSP revenue, and user experience.