Emergency task offloading strategy based on cloud-edge-end collaboration for smart factories

Dependent task offloading mechanism for cloud–edge-device collaboration

Mobile edge computing (MEC) is an emerging computing and communication model that brings extremely resourceful servers at a network's edge. This reduces the latency associated with cloud computing as applications executing on mobile devices do not always need to communicate with a cloud server because most of the functionalities provided by the cloud server are available on an edge server. Moreover, with the emergence of 5G really high amount of bandwidth is available to a mobile device, hence communication latency between the edge server and a mobile device is low. The combination of 5G and edge computing has a potential to better enable many interesting time-critical and/or computationally expensive computing and communication use cases, for example, connected cars. The stated combination enables development of computationally expensive applications for mobile devices and sensors nodes as these devices can offload their computational tasks to the edge server. Task offloading is a promising feature of edge computing, therefore here priority-class-based methods are presented to allocated CPU cycles to an offloaded task at the edge server. In one of the presented methods, a mobile device or a senor node assign their task to one of the available priority classes, and then offload the task to the edge server. Afterwards, the edge server assigns CPU cycles to such a task depending upon its priority class. Similarly, another method is presented that assigns priority to each device based on the type of tasks the device is offloading. While assigning CPU cycles to different offloaded tasks, the edge server uses their device priority. The simulation results demonstrate that the presented methods are capable of providing service differentiation.

The arrival of big data and the Internet of Things (IoT) era greatly promotes innovative in-network computing techniques, where the edge-cloud continuum becomes a feasible paradigm in handling multi-dimensional resources such as computing, storage, and communication. In this article, an energy constrained unmanned aerial vehicle (UAV)-aided mobile edge-cloud continuum framework is introduced, where the offloaded tasks from ground IoT devices can be cooperatively executed by UAVs acts as an edge server and cloud server connected to a ground base station (GBS), which can be seen as an access point. Specifically, a UAV is powered by the laser beam transmitted from a GBS, and can further charge IoT devices wirelessly. Here, an interesting task offloading and energy allocation problem is investigated by maximizing the long-term reward subject to executed task size and execution delay, under constraints such as energy causality, task causality, and cache causality. A federated deep reinforcement learning (FDRL) framework is proposed to learn the joint task offloading and energy allocation decision while reducing the training cost and preventing privacy leakage of DRL training. Numerical simulations are conducted to verify the effectiveness of our proposed scheme as compared to three baseline schemes.

The Savvy Manufacturing plant could be a concept that communicates the conclusion goal of fabricating digitization. A Smart Factory, within the most common sense, profoundly digitized shop floor that collects and offers information persistently through associated computers, gadgets, and generations. In this work, the factory is represented by five types of sensors. The reading of the sensor values is sent to one of the Edge servers and cloud computing. One Edge server is selected based on calculating the time it takes for each server. The highest least time priority is chosen to receive the data coming from the sensors. This paper suggests a way to reduce the delay by using the edge server in addition to cloud computing by using methods that overcome any malfunction in one of the servers via another one that can work without the need to stop the factory systems. Index Terms— Smart Factory, edge computing, delay in IoT.

Consideration is given to a relevant task of organizing cloud services for building the factories of the future: a digital factory and smart factory Industry 4.0. The digital factory majors in design activity resulting in preparation of technical documentation for an article. The smart factory majors in the production activity, which results in article manufacture following the unattended technology. Both types of enterprises use the cyberphysical equipment in the activity thereof, which function on the basis of cloud technologies, including the following cloud services: infrastructure, platform, virtual machine, program, with the user’s access thereto provided on the basis of interfaces of the Industrial Internet of Things. Presently there are no ready technical (technological) solutions providing for integration of cloud services into design and production infrastructure of the factories of the future. The schemes of interaction of components of the cloud services for design activity for the digital factory and for production activity of the smart factory majoring in instrument-building in the field of radio electronics are offered. The properties and assignments of components featuring practical application as part of automated stations of designers of digital factory and operators of the smart factory are described. The decisions on substantiating contents and description of methods of integrating heterogenous cloud services for information support of activity of factories of the future are the main research results.

This paper proposed a pickup and delivery (P&D) task provisioning and vehicle routing framework for carrier robots in a factory with edge servers assisting as the intermediary between robots and working stations, where the task requests come. The carrier robots must pick up and deliver each assigned load from and to designated locations with minimal traveled distance and without violating designated constraints. Edge servers are utilized to facilitate communication between the main server and delivery robots and assist the main server in deciding the best robot for each incoming task request and the updated route to facilitate execution of tasks by each carrier robot. The problem of pickup and delivery for each robot is modeled based on Dial-a-Ride Problem, and a discrete bio-inspired algorithm is proposed to solve this problem. The tasks are distributed to edge servers and carrier robots by taking their service loads into account. The simulation results show that the proposed framework can provide an effective solution towards optimizing the pickup and delivery process in a smart factory.

Smart factory evolves by adding new functions or upgrading existing functions to meet the needs of manufacturers in the use stage of the life cycle. To reduce complexity of smart factory, these functions must be carefully designed considering interactions with other functions. This study analyzes integration of functions situated in the different layers in the functional hierarchy of smart factory. In this study, a health monitoring system for cutting tools in a shop floor, which offers a function to manage the lifetime of cutting tools, is presented. The system is integrated with a production scheduling system, which offers a function to schedule machining processes considering efficient usage of machines as well as cutting tools, while maintaining the quality machining processes. Primary evaluation of the functional integration shows that activities in the shop floor regarding selection and replacement of cutting tools are considered in defining production schedules. It also shows that such functional integration results in increase in complexity regarding the behavioral model of humans in smart factory.

Today, with the presence of 5G communication systems, including Internet of Things (IoT) technology, there is a high demand for mobile devices (especially smartphones, tablets, wearable technology, and so on). Regarding this proliferation and high demand, the massive adoption of mobile devices (MDs) has led to an exponential increase in network latency; the heavy demand for cloud servers causes the degradation of data traffic, which considerably impacts the real-time communication and computing aspects of mobile devices. As a result, mobile edge computing (MEC), an efficient framework capable of enhancing processing, optimizing energy usage, and offloading computation tasks, is considered a promising solution. In current research, numerous models have been implemented to achieve resource allocation and task offloading. However, these techniques are ineffective due to privacy issues and a lack of sufficient resources. Hence, this study proposes secure task offloading and resource allocation strategies in mobile devices using the Probit Mish–Gated Recurrent Unit (PM-GRU) and Entropic Linear Interpolation-Serval Optimization Algorithm (ELI-SOA). Primarily, the tasks to be offloaded and their attributes are gathered from mobile users and passed to a local computing model to identify the edge server. Here, the task attributes and the server attributes are compared with a cache table using the Sorensen–Dice coefficient. If the attributes match, then details about the appropriate edge server are produced. If the attributes do not match, then they are inputted into a global scheme that analyzes the attributes and predicts the edge server based on the Probit Mish-Gated Recurrent Unit (PM-GRU). Then, the server information is preserved and updated in the cache table in the local scheme. Further, the attributes, along with the predicted edge server, are inputted into a system for privacy-preserving smart contract creation by using Exponential Earth Mover’s Distance Matrix-Based K-Anonymity (EEMDM-KA) to develop a secure smart contract. Subsequently, the traffic attributes in the smart contract are extracted, and the request load is balanced by using HCD-KM. Load-balanced requests are assigned to the edge server, and the optimal resources are allocated in the cloud server by using the Entropic Linear Interpolation-Serval Optimization Algorithm (ELI-SOA). Finally, the created smart contract is hashed based on KECCAK-512 and stored in the blockchain. With a high accuracy of 99.84%, the evaluation results showed that the proposed approach framework performed better than those used in previous efforts.

In 6G communication systems, the space-air-ground integrated network (SAGIN) integrates satellite and aerial networks, leveraging more communication and computational resources to assist ground networks in handling computationally intensive tasks. However, when multiple tasks compete for limited computational resources simultaneously, it can lead to imbalanced resource distribution and increased task processing delay. Therefore, this paper proposes an evolutionary game resource distribution strategy for 6G SAGIN (EGRDSS), which integrates 6G SAGIN with mobile edge computing, aiming to optimize system delay. First, we proposed a 6G space-air-ground integrated computing offloading network model, which combines air and space-based networks to augment task offloading options for user devices. Then the user devices can offload tasks to base station, unmanned aerial vehicle, or low earth orbit satellite edge servers for processing. Next, with the goal of minimizing task execution delay, we decompose the optimization problem into two subproblems: task offloading decisions and computational resource distribution. We thus propose a particle swarm task offloading optimization algorithm and an evolutionary dynamic game computational resource distribution algorithm to solve the respective subproblems. The particle swarm task offloading optimization algorithm achieves good convergence performance in global search. At the same time, we introduce evolutionary game theory to address the resource distribution problem, characterize the dynamic equilibrium among multiple users competing for limited computational resources, and enable global performance optimization in the complex and heterogeneous SAGIN environment. Simulation experiments validate that EGRDSS has advantages in optimizing system delay. Compared with random offloading, simulated annealing resource allocation algorithm and genetic resource allocation algorithm strategies, task execution delays are reduced by at least 8.32%, 11.6%, and 22%, respectively.

Federated Learning (FL) enables cooperative model training across dispersed edge devices while protecting data privacy and providing localized insights without the need for centralized data aggregation. In the Internet of Things (IoT), federated learning enables cooperative model training among dispersed edge devices while protecting data privacy and providing localized insights without the need for centralized data aggregation. Nevertheless, Federated Learning’s local model sharing approach makes it susceptible to Man-in-the-Middle (MITM) attacks. In FL, attackers have the ability to manipulate local models. As a result, a global model produced from the altered local models may be inaccurate. In this paper, we suggest a blockchain-based FL architecture to prevent intermediaries from readily altering the FL parameters throughout the transmission process. All of the clients' parameters are combined by a cloud server, which is acting as the federated parameter server. By integrating blockchain technology into the overall architecture, we link all cloud and edge servers. This paper uses a PoC algorithm based on the SHA-256 hashing function in order to validate the data. The results and comparison analysis show that the suggested framework has a low false-positive rate and a high accuracy in detecting MITM attacks in their early stages, with a detection rate ranging from 98 to 100%.

Edge computing, which locates edge servers with limited computing and storage resources at the edge of networks, is expected as a novel architecture for low-latency applications. In edge computing, users offload a task to edge servers due to poor computing resources and batteries of mobile devices so that the edge servers execute the offloaded task and return the result of it to users. Users can thus enjoy various applications without depending on the limitations of mobile devices. However, when the edge server’s load is too heavy, a large number of tasks will be offloaded to distant cloud servers. The long distance between users and cloud servers significantly degrades the quality of mobile applications. To prevent this problem, we propose flow splitting and aggregation schemes to improve offload delay to cloud servers in edge computing. This scheme splits TCP connections between users and cloud servers at the edge server, and then aggregates TCP connections between the edge server and cloud servers. We show the effectiveness of our scheme through simulation evaluations.

The smart factory as a key construct of industry 4.0: A systematic literature review

Interacting with the Web of Things

Mobile applications can leverage high-quality deep learning models such as convolutional neural networks and deep neural networks to provide high-performance cognitive services. Prior work on deep learning models-based mobile applications in a cloud-edge computing environment focuses on performing lightweight data pre-processing tasks on edge servers for cloud-hosted cognitive servers. These approaches have two major limitations. First, it is uneasy for the mobile applications to assure satisfactory user experience in terms of network communication delay, because the intermediary edge servers are used only to pre-process data (e.g., images and videos) and the cloud servers are used to complete the tasks. Second, these approaches assume the pre-trained deep learning models deployed on cloud servers are static, and will not attempt to automatically upgrade in a context-aware manner. In this article, we propose a cloud-edge collaboration framework that facilitates delivering cognitive services with long-lasting, fast response, and high accuracy properties. We fist deploy a shallow model (i.e., EdgeCNN) on the edge server and a deep model (i.e., CloudCNN) on the cloud server. EdgeCNN can provide durable and rapid response cognitive services, because edge servers not only provide computing resources for mobile applications, but also close to users. Then, we enable CloudCNN to assist in training EdgeCNN to improve the performance of the latter. Thus, EdgeCNN also provides high-accuracy cognitive services. Furthermore, because users may continue to upload data to edge servers in real-world scenarios, we propose to use the ongoing assistance of CloudCNN to further improve the accuracy of the shallow model. Experimental results show that EdgeCNN can reduce the average response time of cognitive services by up to 55.08 percent and improve accuracy by up to 26.70 percent.

Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices.