SummaryWith the development of Internet of Things (IoT), more and more computation‐intensive tasks are generated by IoT devices. Due to the limitation of battery and computing capacity of IoT devices, these tasks can be offloaded to mobile edge computing (MEC) and cloud for processing. However, as the channel states and task generation process are dynamic, and the scales of task offloading problem and solution space size are increasing rapidly, the collaborative task offloading for MEC and cloud faces severe challenges. In this paper, we integrate the two conflicting offloading goals, which are maximizing the task finish ratio with tolerable delay and minimizing the power consumption of devices. We formulate the task offloading problem to balance the two conflicting goals. Then, we reformulate it as an MDP‐based dynamic task offloading problem. We design a deep reinforcement learning (DRL)‐based dynamic task offloading (DDTO) algorithm to solve this problem. Our DDTO algorithm can adapt to the dynamic and complex environment and adjust the task offloading strategies accordingly. Experiments are also carried out which show that our DDTO algorithm can converge quickly. The experiment results also validate the effectiveness and efficacy of our DDTO algorithm in balancing finish ratio and power.

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The development of Internet of Things (IoT) technology depends on technologies such as high-efficiency storage and high computing power. Mobile cloud computing (MCC) technology will be an important foundation for the development of IoT. The efficient scheduling of tasks in IoT devices in MCC environment is challenging. The requirements for task scheduling in MCC are becoming more and more complex. As the core problem in MCC, task scheduling aims to allocate tasks reasonably, achieve optimal scheduling strategies, and complete tasks effectively. In this paper, efficient delay-aware task scheduling algorithm (EDTSA) is proposed, with the optimization goal of minimizing task running time. The matching of tasks and virtual machines is modeled as a bipartite graph. The problem is divided into multiple subproblems to solve the optimal solution separately. The combined solution is used as the initial solution of the local search algorithm. The efficiency of the local search depends on the quality and nature of the initial solution. We can generate multiple initial solutions according to different division criteria. The initial solution is the combination of the optimal solutions of the subproblems, so the quality of the initial solution has been greatly improved and generating multiple initial solutions according to the division can reduce the probability of falling into the local optimal solution. This algorithm also divides the neighborhood to reduce unnecessary searches. Finally, we verify the efficiency and practicability of the algorithm through experiments.

With the rapid development of the Internet of Things (IoT), more and more computation-intensive tasks are generated by IoT devices. Due to their own limitations, IoT devices cannot process all tasks locally, and some tasks need to be offloaded to edge servers for processing. In addition, nonorthogonal multiple access (NOMA) technology allows multiple IoT devices to share the same frequency resource. IoT devices can use NOMA technology to transmit data to increase the data transmission rate. In this article, we study the problem of NOMA-enabled dynamic task offloading in heterogeneous networks. We formulate a stochastic optimization problem to minimize system energy consumption. Using stochastic optimization techniques, we transform this problem into a deterministic optimization problem and decompose it into five sub-problems to solve. At the same time, we propose a NOMA-enabled dynamic task offloading (NDTO) algorithm. Then, we mathematically analyze the performance of the NDTO algorithm. We conduct a series of parameter analysis experiments and comparative experiments, and the results verify the performance of the NDTO algorithm.

Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model.

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