Home router sharing based on trust (HORST) [1] is such a mechanism which addresses three use cases: data offloading, content caching/prefetching, and content delivery. The HORST mechanism eases data offloading to WiFi by sharing WiFi networks among trusted friends. Moreover, it places the content near to the end user such that users can access it with less delay and higher speed, which generally results in a higher Quality-of-Experience. 

The SmartenIT solution consists of a firmware for a home router, an OSN application, and a mobile device app. The HORST firmware establishes a private and a shared WiFi network (with different SSIDs) and manages the local storage of the home router as a cache. In order to participate, a user needs a flat rate Internet access at home, he has to install the HORST firmware to his home router, and he needs to install an application to his mobile device. The owner of the home router uploads the WiFi access information of the shared WiFi to the OSN application. Each user can share his WiFi information to other trusted users via the app and request access to other shared WiFis. As the application knows the position of the users, it can recommend WiFis near to the users, or automatically request access and connect the users for data offloading. Social cascades will be used to predict which content will be requested by which user. As the application also knows about the current and future users of each WiFi, the UNaDa on the home router can be used to cache or prefetch delay-tolerant content which will be delivered when the user is connected to the WiFi.

Every HORST system predicts the content consumption (i.e., when and where will which content be requested) of his owner based on location, activity patterns, interests, and information from the online social network such as content popularity and spreading. If a predicted content is not yet available in the local cache, it will be prefetched. If the user is connected to a friend’s home router, a prefetch command is sent to the HORST system on the friend’s router. For prefetching as well as for actual requests which cannot be served locally, HORST chooses the best source (either another HORST home router or a cloud source) based on overlay information and traffic measurements, and fetches the desired content. In regular intervals, HORST checks if the content in his own local cache is still relevant (either for local consumption or as a source for content delivery) and decides whether to keep or replace it. 

Finally, HORST federates all UNaDas to form an overlay content delivery network (CDN), which allows for efficient content placement and traffic management. Thereby, ISP costs can be included in the local decisions at a UNaDa, e.g., by taking into account the location of contents in terms of Autonomous Systems (AS). A similar approach is adopted by the Socially-aware Efficient Content Delivery (SECD) mechanism [4], also developed within SmartenIT. In particular, SECD employs both social information, i.e. relationships in the social graph and users’ interests, and AS information to construct overlays, within which content items (i.e. videos) are being disseminated. To this end, information (such as home router location, cache content, prefetch commands) is stored in the overlay and HORST conducts (active or passive) traffic measurements between the end-points. For the communication between the uNaDas and the distributed storage of meta-information, the RB-Tracker [3] is used due to its efficiency. Thus, RB-Tracker builds the basis of HORST and hides the complexity of the overlay management.

HORST’s system architecture requires the contribution of resources of end users, which needs an appropriate incentive. From the stakeholders, end users sticks out, as they are consumers of the services provided by the business-driven stakeholders such as ISPs. Therefore, besides monetary incentives, raising QoE of the end users and thus the perceived quality of the service works as an incentive for this group of stakeholders. HORST ensures resource contribution by incorporating an incentive mechanism coupling different resource contributions of end users to the system, namely the provisioning of storage capacity and offloading capacity, to the receivable QoE, based on the vINCENT incentive mechanism presented in [2]. In order to enhance the overall system performance, the incentive includes social networking data as a base of trust, thus increasing market liquidity.