Adaptive Contact Volume prediction in Delay Tolerant Networks

Several concepts behind opportunistic networks come from the studies on Delay Tolerant Networks (DTNs). The DTN architecture is suitable for a wide variety of heterogeneous networks and provides long-term storing and forward information switching to overcome communication disruptions. In most DTN solutions the focus of research on opportunistic networks has been on routing, bundle forwarding and message delivery validation. However current solutions do not have mechanisms to predict the amount of bundles, which can be transmitted during a DTN contact. In places where opportunistic connections are unstable, such as Amazon forest, it is critical to predict the amount of information that can be transmitted as a way to optimize bundles size, according to the duration of a contact and node speed. Information about the Contact Volume (CV), which is the amount of data that can be transferred during a time interval, allows the adoption of key strategies for optimizing message dispatch to improve the delivery rate and reduce the delivery delay in DTNs. Based on this observation, we present a set of experiments that validate an Adaptive Contact Volume Predicting Model in DTNs required to handle messages that are dispatched during an efficient contact. Our experiments consist of measurements using smartphones to define the amount of data transferred between two nodes, and the radio frequency signal strength measurements. Our results indicate clearly that efficient CV predictions are achieved by the proposed adaptive CV model.