Device-enhanced mobile edge computing (MEC) is an emerging technology designed to handle intensive and delay-sensitive tasks through device-to-device (D2D) communication. In this paper, we present a terminal-edge collaborative computing network to investigate device-enhanced distributed tasks scheduling (DDTS) with specific application deployment. Our optimization focuses on offloading choices, bandwidths, and computing frequencies, aiming to minimize execution costs, including processing delay and energy consumption. We decouple the joint multiple goals optimization problem into several sub-problems which are solved by math optimization methods except the NP-hard offloading choices sub-problem. This NP-hard problem is modeled as a multitask scheduling game (MTSG), which we demonstrate to be a potential game with at least one Nash equilibrium solution. However, considering further the dynamic nature of real-world application deployment and the complexity of large-scale games, the problem evolves into a stochastic game with a Markov policy (SGMP). Thus, we propose a multi-agent DDTS algorithm based on a dueling double deep Q-network (D3QN) to approximate an optimal solution. Extensive experiments confirm the feasibility and efficiency of our approach.

As an emerging computing paradigm, mobile edge computing (MEC) provides processing capabilities at the network edge, aiming to reduce latency and improve user experience. Meanwhile, the advancement of containerization technology facilitates the deployment of microservice-based applications via edge node collaboration, ensuring highly efficient service delivery. However, existing research overlooks the resource contention among microservices in MEC. This neglect potentially results in inadequate resources for microservices constituting latency-sensitive applications, leading to increased response time and ultimately compromising quality of service (QoS). To solve this problem, we propose the Contention-Aware Multi-Application Microservice Deployment (CAMD) algorithm for collaborative MEC, balancing rapid response for applications with low-latency requirements and overall processing efficiency. The CAMD algorithm decomposes the overall deployment problem into manageable sub-problems, each focusing on a single microservice, then employs a heuristic approach to optimize these sub-problems, and ultimately arrives at an optimized deployment scheme through an iterative process. Finally, the superiority of the proposed algorithm is evidenced through intensive experiments and comparison with baseline algorithms.

Mobile edge computing mitigates the shortcomings of cloud computing caused by unpredictable wide-area network latency and serves as a critical enabling technology for the Industrial Internet of Things (IIoT). Unlike cloud computing, mobile edge networks offer limited and distributed computing resources. As a result, collaborative edge computing emerges as a promising technology that enhances edge networks' service capabilities by integrating computational resources across edge nodes. This paper investigates the task scheduling problem in collaborative edge computing for IIoT, aiming to optimize task processing performance under long-term cost constraints. We propose an online task scheduling algorithm to cope with the spatiotemporal non-uniformity of user request distribution in distributed edge networks. For the spatial non-uniformity of user requests across different factories, we introduce a graph model to guide optimal task scheduling decisions. For the time-varying nature of user request distribution and long-term cost constraints, we apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time subproblems that do not require prior knowledge of future system states. Given the NP-hard nature of the subproblems, we design a heuristic-based hierarchical optimization approach incorporating enhanced discrete particle swarm and harmonic search algorithms. Finally, an imitation learning-based approach is devised to further accelerate the algorithm's operation, building upon the initial two algorithms. Comprehensive theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed schemes.

Body condition score (BCS) of dairy cows is the direct reflection of their nutritional status. The timely estimation of BCS is beneficial to improving dairy cow health, milk production and reproduction. In this work, we propose an intelligent Edge-IoT platform with deep learning for estimating BCS of dairy cow, by integrating inference capability of deep learning and low latency of edge computing in IoT framework. Through capturing images of dairy cow’s back with the RGB-D camera, inference module deployed in the edge computing device firstly performs cow detection to localize the separate area of each dairy cow, and then performs individual identification and estimating BCS of dairy cows simultaneously. The existing systems are mainly commercial systems such as DeLaval and HerdVision, they use electronic ear tags with radio-frequency identification sensors for cow identification. Compared to existing systems, in the proposed platform, combined the finetuned YOLOv7 model and Avoid Repeated Inference (ARI) algorithm to detect dairy cow. An EfficientID model combined with metric learning is designed for cow identification, and an EfficientBCS model with Coordinate Attention (CA) is proposed for estimating BCS. The dairy cow’s identity (ID) and BCS are finally transmitted to the cloud analysis center. Experimental results show that the accuracy of estimating BCS reached 85% within 0.5 range error conducted on the test set collected in the dairy farm. The total inference time for one dairy cow is 3.138 seconds. Results show that the platform can be served as an excellent application of dairy cow body condition scoring.

The distance between the intuitionistic fuzzy sets (IFSs) is a notable and has been widely used information measure to enhance decision-making performance. However, different numerical results are derived when using different distance measures. Therefore, it is worthwhile to explore in depth how to select an appropriate formula for distance computation. This paper uses the line integral to define the distance between IFSs. The presented study divided into three folds. First, the existing distances between IFSs are examined, and their flaws are listed. Second, the distances between IFSs are redefined based on the analysis of the geometric importance of the line integral. More importantly, some existing distances happen to be special cases of the distance we define. Finally, we introduce the accuracy function into the defined distance for evaluating the accuracy of distance by applying the physical meaning of line integral. In other words, the distance accuracy is emphasized as a crucial standard by which to assess the effectiveness of distances between IFSs. To demonstrate the stated measures, some numerical examples are provided to show the superiority of our approach.

With the evolutionary development of computing-intensive and delay-insensitive applications, partial computing offloading in cooperative edge computing networks is considered as a promising technology to reduce tasks execution delay. However, existing researches focus on either splitting one task to several subtasks without exact proportion or splitting each of multiple tasks into hard two parts. In this paper, we consider splitting multiple computing-intensive tasks to several subtasks simultaneously. Accordingly, a joint tasks segmentation and parallel scheduling with cooperative computing problem is formulated to minimize total tasks execution delay. To tackle this intractable mixed integer non-convex problem, firstly, we decouple it into separated multiple tasks segmentation and subtasks parallel scheduling problems. Secondly, the multiple tasks segmentation problem is further decomposed into single task segmentation problem, where the optimal task segmentation ratio function is proposed and proved. Thirdly, the Advantage Actor Critic (A2C) algorithm is applied to choose computation node for subtasks parallelly in an online manner for the time-varying network. Finally, the multiple tasks segmentation scheme is incorporated into A2C algorithm to achieve end-to-end joint optimization of tasks segmentation and parallel scheduling with cooperative computing. Simulation results represent the superiority and effectiveness of the proposed algorithm compared with the benchmarks, such as binary tasks offloading.

Mathematical models are disappointing due to uneven distribution of the air gap magnetic field and significant unmodeled dynamics in magnetic bearing systems. The effectiveness of control deteriorates based on an inaccurate mathematical model, creating slow response speed and high jitter. To solve these problems, a model-free adaptive control (MFAC) scheme is proposed for a three-degree-of-freedom hybrid magnetic bearing (3-DoF HMB) control system. The scheme for 3-DoF HMB depends only on the control current and the objective balanced position, and it does not involve any model information. The design process of a parameter estimation algorithm is model-free, based directly on pseudo-partial-derivative (PPD) derived online from the input and output data information. The rotor start-of-suspension position of the HMB is regulated by auxiliary bearings with different inner diameters, and two kinds of operation situations (linear and nonlinear areas) are present to analyze the validity of MFAC in detail. Both simulations and experiments demonstrate that the proposed MFAC scheme handles the 3-DoF HMB control system with start-of-suspension response speed, smaller steady state error, and higher stability.

As the ideal sliding motion can't be formed in the practical system using a sliding mode control, the sliding mode controller for switched reluctance motor has the problem of large speed ripples. In this paper, a biologically inspired shunting model is incorporated into the variable structure sliding mode control for a switched reluctance motor in order to improve its performance. The simulation by MATLAB demonstrates the effectiveness of the proposed control scheme. The simulation results show that the switched reluctance motor using the proposed biologically inspired sliding mode control has the better speed tracking performance than using the conventional sliding mode control. In addition, the switched reluctance motor controller keeps the features of parameter insensitivity and rapid recovery from load disturbances.