Smart grid delivers power with two-way flows of electricity and information with the support of information and communication technologies. Electric vehicles (EVs) with rechargeable batteries can be powered by external sources of electricity from the grid, and thus charging scheduling that guides low-battery EVs to charging services is significant for service quality improvement of EV drivers. The revolution of communications and data analytics driven by massive data in smart grid brings many challenges as well as chances for EV charging scheduling, and how to schedule EV charging in a smart and resilient way has inevitably become a crucial problem. Toward this end, we in this paper leverage the techniques of software defined networking and vehicular edge computing to investigate a joint problem of fast charging station selection and EV route planning. Our objective is to minimize the total overhead from users' perspective, including time and charging fares in the whole process, considering charging availability and electricity price fluctuation. A deep reinforcement learning (DRL) based solution is proposed to determine an optimal charging scheduling policy for low-battery EVs. Besides, in response to dynamic EV charging, we further develop a resilient EV charging strategy based on incremental update, with EV drivers' user experience being well considered. Extensive simulations demonstrate that our proposed DRL-based solution obtains near-optimal EV charging overhead with good adaptivity, and the solution with incremental update achieves much higher computation efficiency than conventional game-theoretical method in dynamic EV charging.

In conventional terrestrial cellular networks, mobile devices at the cell edge often suffer from poor channel conditions, and thus unmanned aerial vehicles (UAVs) are introduced in recent years to improve the reliability of communication links. However, with the rapid development of Internet of Things (IoT) technology, the emerging IoT applications have blooming demands for high computation capacity from the resource-constrained IoT mobile devices (IMDs), motivated by which, mobile edge computing has been envisioned as an appealing solution to the resource bottleneck problem of IMDs. In order to cope with poor communication performance and high computation demands of cell-edge IMDs, we in this paper leverage UAV-aided edge computing to collaboratively assist computation offloading, taking account of the limited battery life of both IMDs and the UAV. We investigate a joint optimization problem of collaborative computation offloading, bandwidth portion, bit allocation, and UAV trajectory design, aiming to minimize the weighted energy consumption of IMDs and the UAV. Extensive numerical results validate the necessity of introducing UAV-aided edge computing to cellular networks, and the advantages of our proposed scheme on energy savings.

The prompt development of wireless communication network and emerging technologies such as Internet of Things (IoT) and 5G have increased the number of various mobile devices (MDs). In order to enlarge the capacity of the system and meet the high computation demands of MDs, the integration of ultra-dense heterogeneous networks (UDN) and mobile edge computing (MEC) is proposed as a promising paradigm. However, when massively deploying edge servers in UDN scenario, the operating expense reduction has become an essential issue to be solved, which can be achieved by computation offloading decision-making optimization and edge servers' computing resource configuration. In consideration of the complicated state information and ever-changing environment in UDN, applying reinforcement learning (RL) to the dynamical systems is envisioned as an effective way. Toward this end, we combine the deep learning with RL and propose a deep Qnetwork based method to address this high-dimensional problem. The experimental results demonstrate the superior performance of our proposed scheme on reducing the processing delay and enhancing the computing resource utilization.