Advances in content transformation in heterogeneous tactical wireless networks

Content transformation is a viable technique for managing information delivered to tactical MANET nodes interconnected via a quasi-static network (QSN), especially during the period when available bandwidth is limited due to mobility, terrain, weather, jamming, etc. In previous work, we proposed Heterogeneous Intelligent Filtering (HIF) in multi-domain heterogeneous networks, for intelligent active data filtering and transformation to match network capacity and end-user capability. The need for data filtering was determined solely based on network conditions, for example, bandwidth available to the destined MANET nodes at the time. In this work2, we report on enhancing HIF to automatically sense and take into consideration end-user intent and interests in data filtering. We illustrate how a speech-to-text engine can be used to deliver text in place of original speech, and save bandwidth usage, when a circumstance (e.g., a noisy environment) warrants such filtering. We also present several topic-of-interest- and location-based filtering features, and describe how one can combine them to create user intent filtering rules for selectively delivering and/or rerouting messages of interests in applications such as C2MINCS and XMPP-based Chat. We then describe additional HIF extensions and enhancements for deployment and operation support, including the use of HIF as a data dissemination service for individual applications, a distributed primary-backup HIF server mechanism for fault tolerance, and a policy management framework for managing the configuration of HIF components.