Querying in heterogeneous sensor networks is a challenging research issue due to a variety of real-world queries depending on users' preferences. Examples of queries are weather, nearby restaurants, navigation, etc. Users may ask for a breezy path starting from distinct points to distinct train stations. Sensing data collected from wide areas (city or country level) are needed to provide the real-world search as a service for users. Therefore, another challenge is a collaboration of heterogenous sensor networks because deploying sensors in wide areas is impractical. SensorMap [1] collects sensing data from independent sensor networks by allowing data owners to publish their data on the web. Sensing data from all owners are sent to a centric storage in SensorMap. However, distributed data storage is likely to be an efficient solution comparing to a centric storage in which bottleneck always occurs.

Extracting human's health condition using wireless sensors impose a challenge in the balance between data storage and CPU power. Taking available resources on nodes into consideration, we apply Step-wise Context Extraction (SCE) to AoK mule system [4]. SCE compresses data obtained at the mule into a context as the data is forwarded. In this demonstration, we show how the raw data is converted into the context in the AoK system.