As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized control and solve the joint optimization problems related to offloading policy and resource allocation. For reducing algorithm execution time, we apply machine learning methods and construct an adaptive learning-based framework consisting of offloading decision-making, radio resource slicing and algorithm parameters updating. In particular, the offloading policy can be rapidly derived from an estimation algorithm based on a deep neural network, which uses an experience replay training method to improve model accuracy and adopts an asynchronous sampling trick to enhance training convergence performance. Extensive simulations with different parameters are conducted to maintain the trade-off between accuracy and efficiency of the IRAO algorithm. Compared with other candidates, the results illustrate that the IRAO algorithm can achieve superior performance in terms of scalability, effectiveness and efficiency.

With the increasing popularity of terminals and applications, the corresponding requirements of services have been growing significantly. In order to improve the quality of services in resource restrained user devices and reduce the large latency of service migration caused by long distance in cloud computing, mobile fog computing (MFC) is presented to provide supplementary resources by adding a fog layer with several servers near user devices. Focusing on cloud-aware MFC networks with multiple servers, we formulate a problem with the optimization objective to improve the quality of service, relieve the restrained resource of user device, and balance the workload of participant server. In consideration of the data size of remaining task, the power consumption of user device, and the appended workload of participant server, this paper designs a machine learning-based algorithm which aims to generate intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. Based on the proposed algorithm and software-defined networking technology, the tasks can be executed cooperatively by the user device and the servers in the MFC network. Besides, we conducted some experiments to verify the algorithm effectiveness under different numerical parameters including task arrival rate, avaliable server workload, and wireless channel condition. The simulation results show that the proposed intelligent adaptive algorithm achieves a superior performance in terms of latency and power consumption compared to candidate algorithms.

Recent meta-learning models often learn priors from observed tasks using a network optimized via stochastic gradient descent (SGD), which usually takes more training steps to convergence. In this paper, we propose an accelerated Bayesian meta-learning structure with a stochastic inference network (ABML-SIN). The proposed model aims to solve the training procedure of Bayesian meta-learning to improve the training speed and efficiency. Current approaches of meta-learning hardly converge within a few descent steps, owing to the small number of training samples. Therefore, we introduce an accelerated gradient descent learning network based on teacher–student architecture to learn the meta-latent variable ${\theta }_t$ for task t. With this amortized fast inference network, the meta-learner is able to learn the task-specific latent ${\theta }_t$ within a few training steps; thus, it improves the learning speed of the meta-learner. To refine the latent variables generated from the transductive amortization network of the meta-learner, SIN—followed by a conventional SGD-optimized network—is introduced as the student–teacher network to online-update the parameters. SIN extracts the local latent variables and accelerates the convergence of the meta-learning network. Our experiments on simulation data demonstrate that the proposed method provides generalization and scalability on unseen samples, and produces competitive/superior uncertainty estimations on few-shot learning tasks on two widely adopted 2D datasets with fewer training epochs compared to the state-of-the-art meta-learning approaches. Furthermore, the parameters generated by SIN act as perturbations on latent weights, enhancing the probability of accelerating the training efficiency of the meta-learner. Extensive qualitative experiments show that our method performs well across different meta-learning tasks in both simulated and real-world circumstances.

This paper introduces an automatic reading device of standard metal tank based on image recognition and automatic control technology. The device can identify the lowest point of the concave liquid level in the liquid level pipe of the standard metal tank, and automatically read the value of the metering neck to calculate the liquid volume in the tank. It solves the problem that the difference of liquid level reading caused by different line of sight angle in manual reading, which leads to the difference of liquid volume calculation. The first class standard metal tank is used for capacity comparison, while the difference between automatic reading and manual reading of 20L standard liquid volume is no more than 0.03%. This work provides a feasible direction for the develop and improvement of new standard metal tank in the future.