With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm. By moving the services and functions located in the cloud to the proximity of users, edge computing can provide powerful communication, storage, networking, and communication capacity. The resource scheduling in edge computing, which is the key to the success of edge computing systems, has attracted increasing research interests. In this paper, we survey the state-of-the-art research findings to know the research progress in this field. Specifically, we present the architecture of edge computing, under which different collaborative manners for resource scheduling are discussed. Particularly, we introduce a unified model before summarizing the current works on resource scheduling from three research issues, including computation offloading, resource allocation, and resource provisioning. Based on two modes of operation, i.e., centralized and distributed modes, different techniques for resource scheduling are discussed and compared. Also, we summarize the main performance indicators based on the surveyed literature. To shed light on the significance of resource scheduling in real-world scenarios, we discuss several typical application scenarios involved in the research of resource scheduling in edge computing. Finally, we highlight some open research challenges yet to be addressed and outline several open issues as the future research direction.

In the era of 5G, with the increasing demands on computation and massive data traffic of the Internet of things (IoT), mobile edge computing (MEC) and ultra-dense network (UDN) are considered to be two enabling and promising technologies, which results in so-called ultra-dense edge computing (UDEC). Task offloading as an effective solution offers low-latency and flexible computation for mobile users in the UDEC network. However, the limited computing resources at the edge clouds and the dynamic demands of mobile users make it challenging to schedule computing requests to appropriate edge clouds. To this end, we first formulate the transmitting power allocation (PA) problem for mobile users to minimize energy consumption. Using quasi-convex technique, we address the PA problem and present a non-cooperative game model based on subgradient (NCGG). Then, we model the problem of joint request offloading and resource scheduling (JRORS) as a mixed-integer non-linear program to minimize the response delay of requests. The JRORS problem can be divided into two problems, namely the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a double decision-making problem and propose a multiple-objective optimization algorithm based on i-NSGA-II, referred to as MO-NSGA. Simulation results show that NCGG can save the transmitting energy consumption and has a good convergence property, MO-NSGA outperforms the existing approaches in terms of response rate and can maintain a good performance in a dynamic UDEC network.

This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production.

During harvesting and storage, slight bruises on apple surface caused by impact, compression, vibration, or abrasion are inevitable. To find an appropriate method to identify the bruised apples at five stages (1 min, 1 day, 2 days, 3 days and 4 days after bruising), 108 Fuji apples were collected as samples. Hyperspectral images of apples covering the wavelength between 400 and 1000 nm were acquired by the SOC710-VP hyperspectral imaging system. The standard normal variate (SNV) method was utilized for smoothing and denoising of the original hyperspectral data. Classification models, including Extreme Learning Machine (ELM), Partial Least Squares Linear Discriminant Analysis (PLS-DA) and Classification and Regression Tree (CART), coupled with a variable selection method named Minimum Redundancy Maximum Relevance (mRMR), were built to identify the bruised apples. The results showed that the ELM models exhibited the best classification capability, with the mean correct classification rate of 95.97%. The bruised samples are easier to be identified over time. Minimum noise fraction (MNF) method was implemented to classify the bruised region of apples based on the selected wavelengths. The overall classification accuracy of MNF is 92.9%, which indicates that MNF is an effective method for identifying bruised regions of apples.

Spatial–temporal graph modeling plays an important role in the fields of transportation, meteorology, and social networks. Traffic flow prediction is a classic spatial–temporal modeling task. Existing methods usually do not take into account the asynchronous spatial–temporal correlation in traffic data. In addition, due to the complexity and variability of traffic data, long-term traffic forecasting is highly challenging. In order to solve the above problems, this article proposes a new deep learning-based asynchronous dilation graph convolution network (ADGCN) to model the spatial–temporal graphs. We mine the asynchronous spatial–temporal correlation in the traffic network, and propose the asynchronous spatial–temporal graph convolution (ASTGC) operation to extract this special relationship. Furthermore, we extend the dilated 1-D causal convolution to a graph convolution. The receptive field of the model increases exponentially with the increase of the network depth. Experiments are conducted on three public traffic data sets, and the results show that the prediction performance of ADGCN is better than the existing counterpart methods, especially in long-term prediction tasks.

Dynamic Time Warping (DTW) is a widely used distance measurement in time series clustering. DTW distance is invariant to time series phase perturbations but has a quadratic complexity. An effective acceleration method must reduce the DTW utilization ratio during time series clustering; for example, TADPole uses both upper and lower bounds to prune off a large ratio of expensive DTW calculations. To further reduce the DTW utilization ratio, we find that the linear-complexity L1-norm distance (Manhattan distance) is effective enough when the time series only comprise small phase perturbations. Therefore, we propose a novel time series clustering by Minimizing Dynamic Time Warping Utilization (MiniDTW) algorithm to accelerate time series clustering. In MiniDTW, the dataset is first greedily summarized into seed clusters, which comprise time series of small phase perturbations, by L1-norm distance. Then, we develop a new Sparse Symmetric Non-negative Matrix Factorization (SSNMF) algorithm, which factorizes the DTW distance matrix of seed cluster centers, to merge the seed clusters into the final clusters. The experiments on UCR time series datasets demonstrate that MiniDTW, pruning 98.52% of the DTW utilization, is better than the counterpart method, TADPole, which only prunes 75.56% of the DTW utilization; and thus MiniDTW is 10 times faster than TADPole.