In edge-enabled Internet of Things (IoT), computation offloading service is expected to offer users with better Quality of Experience (QoE) than traditional IoT. Unfortunately, the growing multiple tasks from users are occuring with the emergence of the IoT environment. Meanwhile, the current computation offloading with QoE is solved by deep reinforcement learning (DRL) with the issue of instability and slow convergence. Therefore, improving the QoE in edge-enabled IoT is still the ultimate challenge. In this article, to enhance the QoE, we propose a new QoE model to study the computation offloading. Specifically, the emerged QoE model can capture three influential elements: 1) service latency determined by local computing latency and transmission latency; 2) energy consumption according to local calculation and transmission consumption; and 3) task success rate based on the coding error probability. Moreover, we improve the deep deterministic policy gradients (DDPG) algorithm and propose a algorithm named the double-dueling-deterministic policy gradients (D ³ PG) based on the proposed model. Specifically, the actor network highly relies on the critic network, which makes the performance of the DDPG sensitive to the critic and thus leads to poor stability and slow convergence in the computation offloading process. To solve this, we redesign the critic network by using Double Q -learning and Dueling networks. Extensive experiments verify the better stability and faster convergence of our proposed algorithm than existing methods. In addition, experiments also indicate that our proposed algorithm can improve the QoE performance.

When there exist not enough historical defect data for building an accurate prediction model, semisupervised defect prediction (SSDP) and cross-project defect prediction (CPDP) are two feasible solutions. Existing CPDP methods assume that the available source data are well labeled. However, due to expensive human efforts for labeling a large amount of defect data, usually, we can only utilize the suitable unlabeled source data. We call CPDP in this scenario as cross-project semisupervised defect prediction (CSDP). Although some within-project semisupervised defect prediction (WSDP) methods have been developed in recent years, there still exists much room for improvement on prediction performance. In this paper, we aim to provide a unified and effective solution for both CSDP and WSDP problems. We introduce the semisupervised dictionary learning technique and propose a cost-sensitive kernelized semisupervised dictionary learning (CKSDL) approach. CKSDL can make full use of the limited labeled defect data and a large amount of unlabeled data in the kernel space. In addition, CKSDL considers the misclassification costs in the dictionary learning process. Extensive experiments on 16 projects indicate that CKSDL outperforms state-of-the-art WSDP methods, using unlabeled cross-project defect data can help improve the WSDP performance, and CKSDL generally obtains significantly better prediction performance than related SSDP methods in the CSDP scenario.

Rapid economic development has been observed worldwide, which has caused environmental problems to worsen. Thus, the Energy Internet (EI), which accesses renewable energy and provides high-quality power services, has recently become a hot issue. As a subnet of the EI, an energy local area network (ELAN) consists of renewable power generation equipment, controllable distributed power generation equipment, storage systems, electric vehicles, and a large number of loads. Energy management is required for economic, environmental, and safety considerations. This paper proposes an energy management optimization model that addresses ELAN operations and includes pollution treatment fees; this model provides intelligent control of the charging and discharging of plug-in hybrid electric vehicles (PHEVs). This model achieves a nonlinear energy management optimization for an ELAN. To promote optimal performance, an improved comprehensive learning particle swarm optimization (CLPSO) algorithm is presented; it combines Tabu Search (TS) and CLPSO to avoid local optima. To verify the performance of our model, two experimental scenarios are built. The simulation results show that our energy management optimization model fulfills the optimal allocation of energy and that the PHEV intelligent charging/discharging strategy promotes economic benefits for the network.

With the rapid development of society and the economy and the increasingly serious environmental problems, low-carbon communities accessing renewable energy and providing high-quality energy services have become popular research topics. However, a large number of volatile distributed generation (DG) power systems in the community are connected to the grid. It is difficult to stabilize and efficiently interact with fragmented and isolated energy management, and it is difficult to meet the low-carbon, stability and intelligent needs of energy management. Therefore, considering the operation cost, the pollution control cost, energy stability and plug-in hybrid electric vehicles (PHEVs), this paper proposes a new regional energy supply model-community energy Internet-and builds a low-carbon community energy adaptive management model for smart services. Then, based on the problem of energy supply instability, an adaptive feedback control mechanism developed on model predictive control (MPC) is introduced to adapt to the changing environment. Finally, a LSTM-RNN based tabu search (TS) is introduced, which overcomes the defect that the multi-objective particle swarm optimization (MOPSO) algorithm easily falls into a local optimum. The simulation results show that the proposed model can effectively realize the optimal allocation of energy, which effectively solves the problem of fragmented energy islands caused by distributed power access. It creates quality of service (QoS) benefits for users, such as cost, time and stability, and realizes wide interconnections, high intelligence and low-carbon efficiency of community energy management.