EDUCATIONAL CONTENT SHARING FOR LEARNING PLATFORMS USING OPPORTUNISTIC NETWORKING

Abstract

The advancement of mobile technology has allowed learning platforms to move in to the mobile world, offering their content through mobile devices which are getting cheaper and more capable. While this may seem like a good opportunity to provide online education to rural areas in Sri Lanka, the lack of proper infrastructure for internet connectivity in such areas has proven to be a practical barrier to achieving it. This research suggests a solution to overcome this barrier by using the concepts of opportunistic networks (OPPNETs) for educational content delivery and sharing. The objective of the proposed solution is to build an efficient and practical mechanism – by using the small-world property of OPPNETs and by using standard protocols to implement – to store and share educational content between mobile devices where the internet connectivity maybe intermittent. The proposed solution utilizes the Betweenness Centrality metric in order to identify the significant nodes in the device neighborhood which play the decisive role in network connectivity and message transmission. The solution also utilizes infrastructure supported opportunistic networks – where support elements from the available infrastructure, such as fixed Wi-Fi routers, are used for the formation of the network – and the spray-and-wait protocol for message routing in order to overcome the device constraints such as limited battery power. A new content structure for educational content is also proposed in this research in order to efficiently store share the content between devices. The proof-of-concept of the proposed solution was built using the Node.js language and uses HTTP and RFC-6455 protocols for communication. Evaluation was done by setting up several devices on an infrastructure guided opportunistic network, and by programmatic manipulation of each device’s connectivity in order to simulate different scenarios. Several connection scenarios were tested and evaluated based on message latency. The scalability of the proposed solution was also evaluated by simulating various load levels to the network. The evaluation concluded that the proposed solution meets the objectives of this research and is scalable to be implemented on a typical social scenario of a learning platform.