Just like the internet, Internet of things (IoT) with right standards and technologies, will impact multiple aspects of human lives including Smart Home, Smart Car and Healthcare. Healthcare of things (HoT) would enhance healthcare using the insight provided by sensor data. Healthcare users will subscribe to HoT services that use sensors to get activity data (such as steps walked, hours slept and so on) along with other health vitals (such as heart rate, temperature and weight) to improve user's health. Similarly Smart home users subscribe to a Smart home cloud service that will use data from home sensors. Most of these IoT scenarios have multiple producer, multiple consumer (MPMC) environments, with multiple sensors as producers of data, and multiple IoT services as consumers of data. Current literature and standards provide many protocols for data paths (data transfers) in these MPMC environments that can be used for IoT. However, data paths in MPMC environment must be easily configurable and manageable by end users in order for IoT to be more appealing to a broader user community. Multiple sensors with multiple applications from each manufacturer to control them would not be preferred by users. Also, IoT users need to be able easily choose between multiple options for service providers. In this demonstration, we highlight how user experience perspectives impact the data framework design in MPMC environments, using our drops data framework infrastructure. We hope to have these concepts included IoT standardization efforts. This demonstration was also made at CES at the IEEE booth.

Given the abundance of online information available to mobile users, particularly tourists and weekend travelers, recommender systems that effectively filter this information and suggest interesting participatory opportunities will become increasingly important. Previous work has explored recommending interesting locations; however, users would also benefit from recommendations for activities in which to participate at those locations along with suitable times and days. Thus, systems that provide collaborative recommendations involving multiple dimensions such as location, activities and time would enhance the overall experience of users.The relationship among these dimensions can be modeled by higher-order matrices called tensors which are then solved by tensor factorization. However, these tensors can be extremely sparse. In this paper, we present a system and an approach for performing multi-dimensional collaborative recommendations for Who (User), What (Activity), When (Time) and Where (Location), using tensor factorization on sparse user-generated data. We formulate an objective function which simultaneously factorizes coupled tensors and matrices constructed from heterogeneous data sources. We evaluate our system and approach on large-scale real world data sets consisting of 588,000 Flickr photos collected from three major metro regions in USA. We compare our approach with several state-of-the-art baselines and demonstrate that it outperforms all of them.