DES Localization Framework for Testbeds (DES-LOFT)

Indoor localization is a service that could be provided by many already deployed IEEE 802.11 wireless networks and enable to find people, printers, or rooms in large office buildings without any additional costs; yet there is limited  support by operating systems and few user space tools.  We implemented the DES Localization Framework for Testbeds (DES-LOFT) that enables the configuration, execution, visualization, and evaluation of experiments in a testbed environment. Its focus is on scenarios where a precision of about 1 to 2 “normal sized'' rooms is sufficient. In contrast to some wireless sensor networks or specialized localization systems, we assume that only IEEE 802.11 WLAN transceivers are available and the nodes are sparsely deployed creating a random network with varying node degree. While these assumptions apply for many real world networks, localization algorithms are often developed and tested in simulation environments. With DES-LOFT based implementations we try to identify and solve particular issues to create localization daemons that can be easily deployed and used in today's wireless networks.

Architecture

DES-LOFT consists of three major parts. The Node Agent is a daemon that runs on the mesh routers that enables the communication between the nodes, network-wide configuration and probing of the current state. A Proxy is run on a gateway node that provides access to all nodes in the network and caches data for subsequent queries to take load off the nodes. As last a GUI provides a management interface with a 3D view of the network showing two different locations of the mesh routers: real and localized position. Currently experiments are mostly run in an interactive-way using the GUI to allow fine granular control. The user can make crucial important decisions for the algorithms under study if a deterministic behavior has to be forced. Fully distributed and autonomous experiments are also possible.

Navigation

Besides simple localization, we are also working on a navigation feature for DES-LOFT. Users shall get feedback if they are moving towards or away from their destination and get instructions based on their current movement vector. We try to improve the localization and navigation based on stochastical analysis of recorded traces.