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With the continuous development of cloud computing and edge computing technologies, the education sector is gradually applying these technologies to enhance the management and utilization efficiency of teaching resources. Therefore, to address the issue of information overload mentioned above, it is necessary to establish a personalized recommendation system based on user needs, preferences, and other information, recommending products, information, and resources that may be of interest to users. This can not only save users search time, but also alleviate the problem of information overload to some extent. On this basis, this article discussed a new content oriented recommendation method: constructing a user interest feature vector resource association matching model, and analyzing it to achieve recommendation of similar resources. The experimental results showed that the MAE (Mean Absolute Error) value of personalized recommendation based on CF (Collaborative Filtering) algorithm was below 0.8, which was smaller than other algorithms, indicating high accuracy of recommendation based on CF algorithm. [ABSTRACT FROM AUTHOR]

Abstract The proper use of protective equipment is very important to avoid fatalities. One sector in which this has a great impact is that of construction sites, where a large number of workers die each year. In this sector as in others, employers are responsible for providing their employees with this equipment. In addition, employers must monitor and ensure its correct use. These tasks are usually performed using manual procedures. Existing tools to automate this process are unreliable and present scalability issues. In this paper, we research the benefits of using a cloud-edge computing architecture to automate the monitoring of protective equipment. The solution we propose successfully addresses all the problems that appear in hostile and unstructured work environments such as construction sites. Although these sites are used as a use case, the approach presented can also be deployed in other sectors with similar characteristics and restrictions.

CloudEdgeAssetsOptimizer is a software tool designed to evaluate and optimize the assets within a Cloud and Edge computing network. Its primary purpose is to provide insights and estimations to ensure efficient resource allocation and decision-making. The developed software simulates the queues in Cloud and Edge devices, providing waiting times, battery consumption of Edge devices, and Servers’ load. With aforementioned parameters available, valuable insights for decision-making can be obtained to optimize the network. Alternatively, automated optimization can be performed using the embedded functions. CloudEdgeAssetsOptimizer aims to optimize the operational efficiency, enhance resource utilization, and ultimately improve the overall system performance. CloudEdgeAssetsOptimizer functions utilize the queueing theory principles, therefore it also enables users to explore and fine-tune the system parameters across diverse domains, including telecommunication networks, transportation and manufacturing systems.

Abstract Achieving efficient and secure sharing of data in the Internet of Vehicles (IoV) is of great significance for the development of smart transportation. Although blockchain technology has great potential to promote data sharing and privacy protection in the context of IoV, the problem of securing data sharing should be payed more attentions. This paper proposes an IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing. Firstly, to improve protocol’s efficiency, a dual-chain structure empowered by alliance chain is introduced as the model architecture. Secondly, for the space problem characterized by data storage and security, we adopt distributed storage with the help of edge devices. Finally, to both ensure the efficiency of consensus protocol and protect the privacy of vehicles and owners simultaneously, we improve DPoS consensus algorithm to realize the efficient operation of the IoV data sharing model, which is closer to the actual needs of IoV. The comparison with other data sharing models highlights the advantages of this model, in terms of data storage and sharing security. It can be seen that the improved DPoS has high consensus efficiency and security in IoV.

Due to the swift development of the Internet of Things (IoT), massive advanced terminals such as sensor nodes have been deployed across diverse applications to sense and acquire surrounding data. Given their limited onboard capabilities, these terminals tend to offload data to servers for further processing. However, terminals cannot transmit data directly in regions with restricted communication infrastructure. With the increasing proliferation of unmanned aerial vehicles (UAVs), they have become instrumental in collecting and transmitting data from the region to servers. Nevertheless, because of the energy constraints and time-consuming nature of data processing by UAVs, it becomes imperative not only to utilize multiple UAVs to traverse a large-scale region and collect data, but also to overcome the substantial challenge posed by the time sensitivity of data information. Therefore, this paper introduces the important indicator Age of Information (AoI) that measures data freshness, and develops an intelligent AoI optimization data processing approach named AODP in a hierarchical cloud–edge architecture. In the proposed AODP, we design a management mechanism through the formation of clusters by terminals and the service associations between terminals and hovering positions (HPs). To further improve collection efficiency of UAVs, an HP clustering strategy is developed to construct the UAV-HP association. Finally, under the consideration of energy supply, time tolerance, and flexible computing modes, a gray wolf optimization algorithm-based multi-objective path planning scheme is proposed, achieving both average and peak AoI minimization. Simulation results demonstrate that the AODP can converge well, guarantee reliable AoI, and exhibit superior performance compared to existing solutions in multiple scenarios.

The rapid development of mobile device applications put tremendous pressure on edge nodes with limited computing capabilities, which may cause poor user experience. To solve this problem, collaborative cloud-edge computing is proposed. In the cloud-edge computing, an edge node with limited local resources can rent more resources from a cloud node. According to the nature of cloud service, cloud service can be divided into private cloud and public cloud. In a private cloud environment, the edge node must allocate resources between the cloud node and the edge node. In a public cloud environment, since public cloud service providers offer various pricing modes for users’ different computing demands, the edge node also must select the appropriate pricing mode of cloud service; which is a sequential decision problem. In this stydy, we model it as a Markov decision process and parameterized action Markov decision process, and we propose a resource allocation algorithm cost efficient resource allocation with private cloud (CERAI) and cost efficient resource allocation with public cloud (CERAU) in the collaborative cloud-edge environment based on the deep reinforcement learning algorithm deep deterministic policy gradient and P-DQN. Next, we evaluated CERAI and CERAU against three typical resource allocation algorithms based on synthetic and real data of Google datasets. The experimental results demonstrate that CERAI and CERAU can effectively reduce the long-term operating cost of collaborative cloud-side computing in various demanding settings. Our analysis can provide some useful insights for enterprises to design the resource allocation strategy in the collaborative cloud-side computing system.

In the field of edge-cloud computing environments, there is a continuous quest for new and simplified methods to automate the deployment and runtime adaptation to application lifecycle changes. Towards that end, cloud providers promote their own service description languages to describe deployment and adaptation processes, whereas application developers opt for cloud-agnostic open standards capable of modeling applications. However, not all open standards are able to capture concepts that relate to the adaptation of the underlying computing environment to changes in the application lifecycle. In our quest for a formal approach to encapsulate these concepts, this study presents various Cloud Modeling Languages (CMLs). In this study, when referring to CMLs, we are discussing service description languages, domain-specific languages, and open standards. The output of this study is a review that performs a classification on CMLs based on their effectiveness in describing deployment and adaptation of applications in both cloud and edge environments. According to our findings, approximately 90.9% of the examined languages offer support for deployment descriptions overall. In contrast, only around 27.2% of examined languages allow developers the choice to specify whether their application components should be deployed on the edge or in a cloud environment. Regarding runtime adaptation descriptions, approximately 54.5% of the languages provide support in general.

Cloud-edge computing is a hybrid model of computing where resources and services provided via the Internet of Things (IoT) between large-scale and long-term data informs of the cloud layer and small-scale and short-term data as edge layer. The main challenge of the cloud service providers is to select the optimal candidate services that are doing the same work but offer different Quality of Service (QoS) values in IoT applications. Service composition in cloud-edge computing is an NP-hard problem; therefore, many meta-heuristic methods introduced to solve this issue. Also, the correctness of meta-heuristic and machine learning algorithms for evaluating service composition problem should be proven using formal methods to guarantee functional and non-functional specifications. In this paper, a hybrid Artificial Neural Network-based Particle Swarm Optimization (ANN-PSO) Algorithm presented to enhance the QoS factors in cloud-edge computing. To illustrate the correctness and improve the reachability rate of candidate composited services and QoS factors for the proposed hybrid algorithm, we present a formal verification method based on a labeled transition system to check some critical Linear Temporal Logics (LTL) formulas. The experimental results illustrated the high performance of the proposed model in terms of minimum verification time, memory consumption, and guaranteeing critical specifications rules as the Linear Temporal Logic (LTL) formulas. Also, we observed that the proposed model has optimal response time, availability, and price with maximum fitness function value than other service composition algorithms.