Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. As a consequence, smart campuses and universities need to provide connectivity to IoT nodes and gateways, and deploy architectures that allow for offering not only a good communications range through the latest wireless and wired technologies, but also reduced energy consumption to maximize IoT node battery life. In addition, such architectures have to consider the use of technologies like blockchain, which are able to deliver accountability, transparency, cyber-security and redundancy to the processes and data managed by a university. This article reviews the state of the start on the application of the latest key technologies for the development of smart campuses and universities. After defining the essential characteristics of a smart campus/university, the latest communications architectures and technologies are detailed and the most relevant smart campus deployments are analyzed. Moreover, the use of blockchain in higher education applications is studied. Therefore, this article provides useful guidelines to the university planners, IoT vendors and developers that will be responsible for creating the next generation of smart campuses and universities.

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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A Review on Computational Intelligence Techniques in Cloud and Edge Computing

As an emerging business computing model, cloud computing needs to deal with the scientific workflow submitted by user groups. How to efficiently schedule massive tasks of scientific workflow is an important problem in cloud computing. In order to minimize the total execution time of workflow, reduce the consume of cloud resources, reduce execution costs of users, a new task scheduling algorithm based on task duplication and task grouping is proposed in this paper. The new algorithm is composed of four steps. Firstly, the join nodes are duplicated, a DAG is converted into an in-tree graph, then all tasks are divide into task groups, it reduces communication overhead between tasks; then some task groups are merged by utilizing the idle time between tasks in a task group, it reduces the use of the processors; lastly, Assign the tasks to processors by making full use of the idle time of the processors, it increases resource utilization. The new algorithm is compared with TDS and TDCS by simulation platform CloudSim. The performance indicators for comparison include makespan of workflow, the number of used processors and resource utilization. The experiment results show that the new algorithm has a smaller makespan of workflow, fewer processors are used, and has higher resource utilization for both compute-intensive and data-intensive workflow, especially for data-intensive workflow, the new algorithm has obvious advantages on the three performance indicators.

An improved task scheduling algorithm for scientific workflow in cloud computing environment

In cloud computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO) scheduling algorithm. To reduce tasks’ waiting time, we propose a task scheduling algorithm based on fuzzy clustering algorithms. We construct a task model, resource model, and analyze tasks’ preference, then classify resources with fuzzy clustering algorithms. Based on the parameters of cloud tasks, the algorithm will calculate resource expectation and assign tasks to different resource clusters, so the complexity of resource selection will be decreased. As a result, the algorithm will reduce tasks’ waiting time and improve the resource utilization. The experiment results show that the proposed algorithm shortens the execution time of tasks and increases the resource utilization.

Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing

Flexible Subspace Clustering: A Joint Feature Selection and K-Means Clustering Framework

In this paper, the assignment performance problem in the process of the allocation resources has not been considered in the present most cloud computing data center, this paper proposes a new cloud computing resources allocation strategy, on the basis of cost model of the parallel task performance, using the improved Fuzzy Clustering Algorithm (CBFCM -- C Cost based Fuzzy Clustering Algorithm) to classify the cloud computing resources set on the service level of user, and the jobs submitted by guest were analyzed using four different scheduling algorithms, and the generated resource scheduling scheme. The experimental results show that job scheduling strategy based on CBFCM resources classification in the cloud computing services have certain advantages. AFCFS job scheduling algorithm in average response time for ART, the average waiting time AWT, average operating cost of ACP performance items, has great advantage than the other three algorithm.

The Research on Resource Scheduling Based on Fuzzy Clustering in Cloud Computing

With the deepening of the integration of Internet technology and education teaching, this paper analyzes the learning mode and operation mode of high quality resources platform both at home and abroad, and highlights the importance of resource sharing platform in the construction of lifelong education system in Jiangsu, using cloud computing, big data, Services and other technologies, put forward the function framework of Jiangsu lifelong educational resources platform. Moreover, from the seven levels, respectively, the resource platform for a detailed description of the function, for the construction of lifelong educational resources platform in Jiangsu to provide technical solutions. Finally, it analyzes the application process and possible problems of the resource platform, and puts forward some positive countermeasures from the aspects of capital investment, quality assurance, strengthening cooperation and effective promotion.

Research on the Construction of Resource Sharing Platform Based on MicroService

Based on the analysis of the traditional passive information service and the locating technology of campus wireless LAN, a new idea of message pushing based on user location is discussed, and the architecture of information service platform based on campus location is studied, and the function is designed in order to strengthen campus management. The paper presents an experimental location-based information service platform which can active push personalized service messages to users based on different location. The platform also can be used for location management and location analysis, and the early warning would be triggered by the abnormal results. Finally, in order to maximize the effectiveness of the platform, this paper discusses the basic requirements of the platform in the actual operation, and the next step of the experimental platform research objectives.

Research on Campus Information Service Platform Based on Locating System of Campus Wireless LAN

In this paper, the requirements of next generation regional network of JSOU based on IPv6 has been analyzed, and the construction principle has been formulated by Jiangsu Open University building goals. The plan of step by step to the next generation regional network of JSOU based on IPv6 has been put forward based on the architecture of JSOU, the construction planning and the specific content has been researched to each stage. At last, the feasibility and superiority of the campus network of JSOU based on IPv6 has been verified by building a virtual experimental environment.

Wang Xiaojun

Papers

The Research on Resource Scheduling Based on Fuzzy Clustering in Cloud Computing

Research on the Construction of Resource Sharing Platform Based on MicroService

Research on Campus Information Service Platform Based on Locating System of Campus Wireless LAN

Research on Planning of Next Generation Regional Network of Jiangsu Open University Based on IPv6