TP3.5: Proximity Services

Subproject manager
Prof. Dr. Jörg Ott

Proximity services are a form of services, which are not only produced based on the user’s location (like location-based services, LBS), but also based on the relative distance between two or more users or between a user and an object. They can be understood as LBS with a relative frame of reference. The goal of this subproject is to create a technical platform for proximity services and to realize a selection of services based on this platform. We provide decentralized mechanisms (device to device, local infrastructure) for the detection of physical proximity to fulfill the following key attributes: discovers things relevant to you, senses your environment, filters things relevant to you, knows what’s around you and interacts with your surroundings. The challenges in this field are multifaceted. Always-on services like proximity detection require continuous discovery, which drains the battery. This is especially a problem for mobile clients with limited battery power. In addition, user privacy is a challenge because the automatic recognition of nearby users allows continuous location tracking. Besides that, proprietary platforms lead to mobile app silos, which is a major problem for system interoperability.

We created an IoT testbed for our prototypes. Therefore, we distributed Bluetooth Low Energy (BLE) beacons for indoor localization of mobile users. One question is the automatic configuration of the BLE beacons. Currently, the vendors provide only an app for manual parameter configuration. Our system can do this automatically by scanning for nearby beacons and update them via a backend, which stores the current configuration for each beacon. Figure 1 presents the system architecture including a web service to exchange beacon data with mobile clients and a database to store configuration changes. The user updates beacon configurations via a comfortable web frontend.

Figure 1: System for Beacon Management

Another prototype aims at close proximity detection of a few meters, where the users or devices share the same content regarding Wi-Fi signals and ambient sound. Figure 2 shows the GUI of our prototype. Initially, a Wi-Fi Direct service broadcasts the device’s available resources regarding CPU and battery. Afterwards, a metric choose one mobile client as temporary server, this device requests the required data from the clients and calculates whether the devices share the same environment. Therefore, each client sends the feature vectors of the Wi-Fi signals including received signal strength. Moreover, the device records ambient sound at a specific frequency and calculates locally sound vectors before releasing them to the temporary server to preserve user privacy. Finally, the users can send messages between group members. The groups are automatically generated by reasoning based on sensor data, which reflects the current device environment.

Please contact Prof. Dr. Jörg Ott or Michael Haus, if you would like to get more information about this subproject, our work in general or if you are interested in a cooperation to develop proximitx-based services. Motivated students who are looking for a thesis or a guided research in this field are always invited to contact us as well.

Figure 2: Proximity Detector

IoT Device Management