Thanks to the public and low cost availability of wireless high speed internet access students are increasingly equipped with mobile internet enabled devices to connect to university services like Learning Management System (LMS). But the applications services like LMS are still unable to adapt themselves to modern mobile devices with restrictions like re duced display size. By recognizing the device and its restrictions it is possible to optimize the LMS interface. Additionally by using device features like position, acceleration sensors, or the camera it is possible to detect the intentions of the user. The context of the user's situation determines which university services are helpful and interesting. Gathering these context information and reasoning on them is the foundation of our context-and service-oriented approach towards a mobile LMS.This enables us to personalize the mobile learning experience with location-sensitive lecture streaming, campus navigation, and ubiquitous features of the whole university computing infrastructure. By nsing a service-oriented architecture we are able to compose a variety of different university and external services towards a pervasive university.

With the widespread of mobile Internet-enabled devices we can consider nearly every user everywhere as a potential consumer of cloud services. Especially campus environments provide a multitude of heterogeneous scientific information and services to the users. Here different problems arise concerning the integration and dissemination of cloud services and resources. While location is usually considered as the core context for mobile service access, its value is very limited in evolving large-scale smart environments with a multitude of user roles, tasks, devices and services. This is even more challenging if multiple users work collaboratively in remote locations on the same project. Therefore, we propose with CASA, Context-Aware Service Access, a concept which can use different kinds of context as source and provides support for reactive and proactive actions based on the current situation. The evolving and changing aspects are handled with an extensive usage of crowdsourcing to encourage a decentralized and user-driven development. This shall lead to a novel open and extendable recommender system which also supports the adaption and integration of cloud-based services according to the situation. Additionally, as proof of concept, we present two use cases for smart campus and smart lecture rooms.

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