Mobile-edge Computing (MEC) has aroused significant attention for its performance to accelerate application's operation and enrich user's experience. With the increasing development of green computing, energy harvesting (EH) is considered as an available technology to capture energy from circumambient environment to supply extra energy for mobile devices. In this paper, we propose an online dynamic tasks assignment scheduling to investigate the tradeoff between energy consumption and execution delay for MEC system with EH capability. We formulate it into an average weighted sum of energy consumption and execution delay minimization problem of mobile device with the stability of buffer queues and battery level as constraints. Based on Lyapunov optimization method, we obtain the optimal scheduling about the CPU-cycle frequencies of mobile device and transmit power for data transmission. Besides, the dynamic online tasks offloading strategy is developed to modify the data backlogs of queues. The performance analysis shows the stability of the battery energy level and the tradeoff between energy consumption and execution delay. Moreover, the MEC system with EH devices and task buffers implements the high energy efficient and low latency communications. The performance of the proposed online algorithm is validated with extensive trace-driven simulations.

Mobile Edge Computing (MEC) brings abundant cloud resources to the edge of the network and provides great opportunities to improve user's quality of experience. While many recent studies have investigated the problem of computation offloading, service caching is also an important design topic of MEC. Service caching stores application-related databases or libraries in advance and enables corresponding user tasks to be offloaded. Due to the limited resources in the edge server, service caching decisions have to be made judiciously to maximize the system performance. In this paper, we study the problem of joint service caching, computation offloading, transmission and computing resource allocation in a general scenario of multiple users with multiple tasks. We aim to minimize the overall computation and delay costs for all users and formulate the optimization problem as a quadratically constrained quadratic program (QCQP) which is non-convex and NP-hard. To solve this challenging problem, we propose an efficiently approximate algorithm based on semidefinite relaxation (SDR) approach and alternating optimization which always computes a locally optimal solution. Moreover, we extend the study to the scenario where each user has a computation cost constraint. Simulation results show that our algorithm can minimize the system cost effectively by utilizing the available system resources.

With the advances in the Internet-of-Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate the negative impact of EV charging on the power grid. This article investigates an EV charging scheduling problem to reduce the charging cost while shaving the peak charging load, under unknown future information about EVs, such as arrival time, departure time, and charging demand. First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information. We develop an actor–critic learning-based smart charging algorithm (SCA) to schedule the EV charging against the uncertainties in EV charging behaviors. The SCA learns an optimal EV charging strategy with continuous charging actions instead of discrete approximation of charging. We further develop a more computationally efficient customized actor–critic learning charging (CALC) algorithm by reducing the state dimension and thus improving the computational efficiency. Finally, simulation results show that our proposed SCA can reduce EVs’ expected cost by 24.03%, 21.49%, 13.80%, compared with the eagerly charging algorithm, online charging algorithm, reinforcement learning (RL)-based adaptive energy management algorithm, respectively. CALC is more computationally efficient, and its performance is close to that of SCA with only a gap of 5.56% in the cost.

Real-time traffic information can efficiently reflect the traffic condition in urban scenarios. However, how to design an efficient real-time information sharing mechanism to achieve the real-time traveling time estimation (TTE) and dynamic path planning still remains a challenging problem. In this paper, we first propose an effective real-time traffic information sharing mechanism based on a distributed transportation system with road-side units, which has lower computing complexity and less redundancy. Then, considering current traffic information, we present a method to estimate the traveling time from a source to a destination in the road network, called TTE, which acts as a metric for path planning. Furthermore, we propose a real-time path planning algorithm based on TTE comparison via information collection to avoid traffic congestion. We verify the complexity and redundancy of our proposed real-time information sharing mechanism via C ^# . Moreover, we perform extensive simulations in VanetMobiSim to evaluate the proposed real-time path planning algorithm, which uses the real-world traffic scenario and urban map of Songjiang district in Shanghai. The results show that our proposed algorithm outperforms the static path planning algorithm.

With the rapid developments of electric vehicles (EVs) in recent years, it is desirable to improve the energy efficiency to prolong the limited life of battery and extend the cruising range of EVs. In real EVs, the battery thermal management system is installed to cool the battery and maintain the expected high power output. In this article, we propose a novel energy management strategy based on deep reinforcement learning (DRL) considering battery thermal effects on energy efficiency. The main idea is to formulate energy management as an optimization problem, further extract the state sequence features of the vehicle via gated recurrent unit (GRU), and finally, propose a double deep $Q$ network (double DQN)-based algorithm to obtain the optimal strategy. Comparisons of our double DQN algorithm and existent fuzzy control, as well as two other conventional reinforcement learning (RL) algorithms, are conducted under New European Driving Cycle, FTP-75, HWFET, and US06 cycles, and the results demonstrate that the proposed algorithm achieves an energy reduction of more than 6.7% during aggressive driving.

Mobile-edge computing (MEC) has been recognized as a promising solution to provide efficient communication and computation capabilities for mobile users (MUs). However, the problem relating to parallel offloading and load balancing with multiple cooperative MEC servers and massive delay-sensitive execution workloads remains to be investigated. In this paper, we study a joint parallel offloading and load balancing policy for such an MEC system. We formulate a long-term system average-cost (i.e., weighted sum of energy consumption and execution delays) stochastic programming problem under MU battery level stability and delay constraints. Our aims include optimizing the data sizes for uplink offloading transmissions for optimized communication and computation resource allocation among multi-users, and maximizing the computation capability utilization of cooperative MEC servers by load balancing. To solve this problem, we first design a Lyapunov-based centralized cost management algorithm (LYP-CCMA) to obtain the optimal system average-cost under the battery level stability constraints. Further, we propose two algorithms based on alternating direction method of multipliers (ADMM) to implement distributed resources allocation. Simulation results verify our analysis and demonstrate the superior performance of our proposed schemes over several baseline schemes.