ACO-based media content adaptation for e-learning environments

The advances of ubiquitous communication infrastructures, the rapid adoption of mobile devices and pervasive computing technologies has allowed e-learning users access to multimedia learning contents in e-learning environments. However, because of the diversity and heterogeneity of the mobile users, their preferences, and the rich multimedia learning content, it is a major challenge for the access of learning content by the desired devices in the e- learning environment to user's satisfaction in terms of QoS demands. In order to alleviate the challenge of learning content mismatch, content adaptation is essential. To this end, we propose an ACO-based multimedia content adaptation approach, which inherits the adoption of ACO-based path selection behavior in the path computation for appropriate learning content customization. We compare our proposed approach with other two competitive algorithms, measure the performance and find that our proposed algorithms outperforms the basic AntNet and Genetic in terms of success rate, latency, runtime comparison and convergence. The performance evaluations are conducted using NetLogo simulation environment.