Pattern Matching Techniques

Thepropagation character of electromagnetic waves in a static environment with constant beacon positions is subject to a specific signature. This effect is basis for pattern matching techniques, whereas signal characteristics are measured at many points in the room. This data is stored in a so-called signal map. Signal maps, for example, consist of many RSSI at different coordinates. Every sensor node initially saves one signal map, which is after deployment basis for a comparison algorithm.

In detail, sensor nodes measure RSSI at the location, where they were deployed. Then, measured values are compared with values in the signal map, whereas the best and thus most probable match gives a position estimate. This method is also known as “pattern recognition” or “fingerprinting” and developed by Bahl and Padmanabhan [BP].

6.4 Conclusions

Due to the strong limitations in sensor networks (e.g., very small form factor, limited capacity of energy), an efficient localization method requires small communication overhead and energy-aware algorithms to meet the conditions. In practice, a precise localization is impeded additionally by faulty input values caused by measurements of the environment. Supplementally, underlying ideal models lead in reality to distorted data as can be seen by the distance estimation based on circular signal attenuation.

Thelocalization error generally fluctuates depending on the specifics of the selected localization algorithm. But, the precision of the localization is significantly affected by the calibration of the sys-tem, e.g., the adjusted transmission power of the transceiver or the ratio between beacons and sensor nodes.

Faulty or heavily unsteady input data lead inevitably to an increasing localization error. Espe-cially, classical localization algorithms (trilateration, triangulation) or pattern matching are very vulnerable whereas proximity-based algorithms react more stably. In contrast, iterative methods and optimization methods behave more robust in systems with a high node density or with lots of measured data. Due to the redundant data, sensor nodes estimate positions optimally by using more information than needed. However, the resource requirements increase noticably in these networks.

Thus, choosing a localization algorithm usually depends on a lot of constraints imposed by the specific application.

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Power-Efficient Routing in