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.

To meet the surging demands on network throughput and spectrum resources arising with billions of Internet-of-Things mobile devices (IMDs), ultradense networks are envisioned to be a promising technology, which gives rise to the so-called ultradense Internet-of-Things (IoT) networks. Meanwhile, with the constant emergence of new IoT applications, the conflict between computing-intensive applications and resource-constrained IMDs is increasingly prominent. By offloading computing-intensive tasks to the edge servers in close proximity, mobile-edge computing is expected as an effective solution to address this issue. However, computation offloading research in ultradense IoT networks is still scarce until now. Toward this end, we provide this paper to study the energy-aware task offloading problem with multiple edge servers in ultradense IoT networks, where diverse kinds of computation tasks are randomly requested by the IMDs and the computing resources at the edge servers change dynamically. An iterative searching-based task offloading scheme is proposed as our solution, which jointly optimizes task offloading, computational frequency scaling, and transmit power allocation. Extensive numerical results demonstrate the superior performance of conducting task offloading among multiple edge servers, and corroborate the advantages of our scheme over existing works which either fixed computational frequency and transmit power, or neglected the impact of the IMDs' residual battery.

In order to meet the ever-increasing demands on computational and spectrum resources in the era of 5G and Internet of Things (IoT), mobile-edge computing (MEC) and ultra-dense heterogeneous network (UDN) have been envisioned as two promising technologies, which gives rise to the so-called ultra-dense edge computing. Note that existing works on task offloading for ultra-dense edge computing mostly considered simple task offloading scenarios, ignoring the random request for types of computation tasks from the mobile devices (MDs) and the random arrival of the tasks at the edge servers. Toward this end, we provide this paper to study the multi-user task offloading problem in ultra-dense edge computing with multiple types of tasks requested by the MDs. To minimize the MDs' energy consumption and thus prolong their battery lifetime, task offloading, computation frequency scaling, and transmit power allocation are jointly optimized in this paper. After that, the problem is divided into two subproblems, i.e., local energy minimization, and joint task offloading and transmit power allocation. A game-theoretical joint offloading scheme is proposed as our solution. Extensive numerical results corroborate the superior performance of our proposed scheme rather than those with single edge server, fixed computation frequency and transmit power at the MDs.

To meet the mobile devices' surging demands for throughput and computation resources in 5G networks, heterogeneous networks (HetNets) and multi-access edge computing (MEC) are expected to be two key distinct but complementary technologies. By offloading the computation tasks of the mobile devices (MDs) to the nearby MEC servers at the edge of radio access networks, MEC can largely augment the MDs' computation resources and battery lifetime. However, existing research on mobile-edge computation offloading only focused on multi-user single-MEC scenarios and little work can be found on designing computation offloading schemes for the case of multi-user multi-MEC. Toward this end, we investigate the problem of collaborative mobile-edge computation offloading in 5G HetNets and propose a game-theoretical computation offloading scheme. Numerical results corroborate that our collaborative computation offloading scheme for multiple MEC servers can not only reduce the overall computation overhead efficiently, but also achieve a Nash equilibrium in a finite number of steps.

The emergence of massive Internet of Things (IoT) mobile devices (MDs) and the deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT networks. In order to meet the diverse quality-of-service and quality of experience demands from the ever-increasing IoT applications, the ultradense IoT networks face unprecedented challenges. Among them, a fundamental one is how to address the conflict between the resource-hungry IoT mobile applications and the resource-constrained IoT MDs. By offloading the IoT MDs’ computation tasks to the edge servers deployed at the radio access infrastructures, including macro base station (MBS) and small cells, mobile-edge computation offloading (MECO) provides us a promising solution. However, note that available MECO research mostly focused on single-tier base station scenario and computation offloading between the MDs and the edge server connected to the MBS. Little works can be found on performing MECO in ultradense IoT networks, i.e., a multiuser ultradense edge server scenario. Toward this end, we provide this paper to study the MECO problem in ultradense IoT networks, and propose a two-tier game-theoretic greedy offloading scheme as our solution. Extensive numerical results corroborate the superior performance of conducting computation offloading among multiple edge servers in ultradense IoT networks.