Understanding people flows in indoor environments such as office buildings and shopping malls is becoming more significant since such information can be useful for smart building applications. For example, crowd navigation applications can be designed for those people with big baggage to choose less crowded routes. People density and flow information is also useful for more adaptive and flexible shop advertisement. Wi-Fi RSSI-based passive measurement is a cost-effective approach as it allows to involve already-installed Wi-Fi APs as a part of the system. Nevertheless, the existing approaches have not tackled the high mobility cases where pedestrians walk with relatively high speed. In this paper, we propose a method to count the number of pedestrians waking across the line between two Wi-Fi devices. The method focuses on the RSSI fluctuations seen when pedestrians pass the device line. We extract some feature values that represent such attenuation and variance of RSSI and build a multi-class classifier to identify the number of pedestrians. The experiments were conducted with different position arrangements of Wi-Fi devices and at different locations to assess the applicability of the method. The results indicate that the proposed method can estimate the number of pedestrians with high accuracy when the Wi-Fi devices are placed obliquely against the walking direction.

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.