With the development of mobile Internet technology and the popularity of intelligent mobile terminals, the data traffic load of mobile mobile client users on the smart campus network platform has surged. How to reduce the data traffic of the smart campus network platform is an urgent problem to be solved. Firstly, this paper discussed the key technologies of Smart Campus network teaching platform under the background of 5G network, expounded the critical technologies of the transport layer of the Internet of Things(IoT) technology and analyzed from the development perspective of Internet of Things platform. Secondly, by investigating the online classroom data of five types of colleges and universities in China, and comparing the advantages and disadvantages of online classroom teaching and traditional classroom teaching, it is found that the number of online courses in colleges and universities has exploded in the second half of 2017. Next, this paper analyzed the demand of Smart Campus online teaching platform under the background of 5G network, thus established an online teaching platform based on the Four Initiatives operation model (Government-led, College sponsor, Teacher subject, Academic director). Finally, this paper adopted the improved VIRE localization algorithm to obtain the specific location information of the student users in the classroom, and then compares with the error obtained by the VIRE algorithm, and the error of the improved VIRE algorithm is smaller. In the process of obtaining information, the 5G network technology is used for data transmission, which can shorten the check-in time and improve the positioning accuracy.

It is anticipated that the backbone of Smart Cities concerning automation and networking will be formed by Unmanned Aerial Vehicles in the imminent future. Therefore, our research focuses on developing advanced microcontrollers embedded with Artificial Intelligence techniques for self-governing Unmanned Aerial Vehicles. The main objective of this research was to enable full automation for the execution of flight paths with non-trivial sequences that will be performed with centimetre-level accuracy. Also, by utilising dynamic flight plans and trajectories, we aim to secure autonomous aviation based on norms, with control loops and fundamental constraints. More specifically, we evolved a novel algorithmic technique for trajectory optimisation, which deploys a modification to the A* search algorithm, implemented by the Haversine formula and enhances accuracy using Vincenty's formula. Furthermore, realistic values for trajectory optimisation and obstacle avoidance were found through the implementation of a simulative investigation. The outcomes of our methodology indicate that the safety constraints associated with the integration of Unmanned Aerial Vehicles in the urban environment can be significantly mitigated. Consequently, their effectiveness will be increased in realising their diverse operations and capabilities.

Mobile Edge Computing (MEC) paradigm is designed to meet the user requirements by providing cloud services at the edge of the user network. Blockchain technology with the EC paradigm is reliable in delivering the edge services depending on user requirements and improving the distributed management of resources at ease. In this article, blockchain-assisted data offloading for Availability Maximization (BDO-AM) is introduced. This proposed approach is presented to thwart the non-probabilistic (NP) hardness problem of data availability due to prolonging backlogs. This approach classifies the different instances of data availability and delivery for the edge-connected end-user services/ applications. The classification is preceded using Nave Bayes classification for identifying the offloading instances to prevent unnecessary backlogs. The probability of data transmission, delivery, and offloading are independently analyzed for their likelihood, and the appropriate available time instances are allocated in a distributed manner. This validation helps to maximize data delivery by reducing the data drops and service delays.

With the emergence of delay-sensitive task completion, computational offloading becomes increasingly desirable due to the end-user’s limitations in performing computation-intense applications. Interestingly, fog computing enables computational offloading for the end-users towards delay-sensitive task provisioning. In this paper, we study the computational offloading for the multiple tasks with various delay requirements for the end-users, initiated one task at a time in end-user side. In our scenario, the end-user offloads the task data to its primary fog node. However, due to the limited computing resources in fog nodes compared to the remote cloud server, it becomes a challenging issue to entirely process the task data at the primary fog node within the delay deadline imposed by the applications initialized by the end-users. In fact, the primary fog node is mainly responsible for deciding the amount of task data to be offloaded to the secondary fog node and/or remote cloud. Moreover, the computational resource allocation in term of CPU cycles to process each bit of the task data at fog node and transmission resource allocation between a fog node to the remote cloud are also important factors to be considered. We have formulated the above problem as a Quadratically Constraint Quadratic Programming (QCQP) and provided a solution. Our extensive simulation results demonstrate the effectiveness of the proposed offloading scheme under different delay deadlines and traffic intensity levels.