Adaptive Content Precaching Scheme Based on the Predictive Speed of Vehicles in Content-Centric Vehicular Networks

Content-Centric Vehicular Networks (CCVNs) are considered as an attractive technology to efficiently distribute and share contents among vehicles in vehicular environments. Due to the large size of contents such as multimedia data, it might be difficult for a vehicle to download the whole of a content within the coverage of its current RoadSide Unit (RSU). To address this issue, many studies exploit mobility-based content precaching in the next RSU on the trajectory of the vehicle. To calculate the amount of the content precaching, they use a constant speed such as the current speed of the vehicle requesting the content or the average speed of vehicles in the next RSU. However, since they do not appropriately reflect the practical speed of the vehicle in the next RSU, they could incorrectly calculate the amount of the content precaching. Therefore, we propose an adaptive content precaching scheme (ACPS) that correctly estimates the predictive speed of a requester vehicle to reflect its practical speed and calculates the amount of the content precaching using its predictive speed. ACPS adjusts the predictive speed to the average speed starting from the current speed with the optimized adaptive value. To compensate for a subtle error between the predictive and the practical speeds, ACPS appropriately adds a guardband area to the precaching amount. Simulation results verify that ACPS achieves better performance than previous schemes with the current or the average speeds in terms of the content download delay and the backhaul traffic overhead.

Adaptive Content Planning and Delivery With Assessment Methodology Using Swarm Intelligence on Cloud Computing

University education has been using traditional approaches to impart knowledge. Advancements in technology create a need to change the teaching-learning process's direction to keep up with the current technology pace. Students and teachers should update their skill sets to suit the current needs of technology. Content and syllabus delivery should be planned and delivered to create a more adaptive, self-paced, and personalized learning experience. Evaluation of tests and assignments requires an intelligent system that uses technologies like swarm intelligence and artificial intelligence. Research carried out in this field helps mold the curriculum as per the student's requirements. Swarm intelligence, combined with cloud computing, enables adaptive content planning and delivery. Swarm intelligence techniques and algorithms help design personalized content and delivery. In contrast, cloud infrastructure provides the required computing capability and storage to perform academic tasks on a standard application platform for students and teachers with a cost-effective solution.

Adaptive Content Frame Skipping for Wyner–Ziv-Based Light Field Image Compression

Light field (LF) imaging introduces attractive possibilities for digital imaging, such as digital focusing, post-capture changing of the focal plane or view point, and scene depth estimation, by capturing both spatial and angular information of incident light rays. However, LF image compression is still a great challenge, not only due to light field imagery requiring a large amount of storage space and a large transmission bandwidth, but also due to the complexity requirements of various applications. In this paper, we propose a novel LF adaptive content frame skipping compression solution by following a Wyner–Ziv (WZ) coding approach. In the proposed coding approach, the LF image is firstly converted into a four-dimensional LF (4D-LF) data format. To achieve good compression performance, we select an efficient scanning mechanism to generate a 4D-LF pseudo-sequence by analyzing the content of the LF image with different scanning methods. In addition, to further explore the high frame correlation of the 4D-LF pseudo-sequence, we introduce an adaptive frame skipping algorithm followed by decision tree techniques based on the LF characteristics, e.g., the depth of field and angular information. The experimental results show that the proposed WZ-LF coding solution achieves outstanding rate distortion (RD) performance while having less computational complexity. Notably, a bit rate saving of 53% is achieved compared to the standard high-efficiency video coding (HEVC) Intra codec.