Location Estimation in Wireless Networks
Type of DegreeDissertation
Computer Science and Software Engineering
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Wireless location determination has attracted much attention lately due to its many applications in mobile (sensor) networking including target tracking and network intrusion detection. However, it is challenging due to the complexities of the wireless radio propagation characteristics exacerbated by the mobility of the mobile. In this dissertation, we propose realistic localization mechanisms for both indoor and outdoor environments. For the indoor localization, a common practice is to mechanically generate a table showing the radio signal strength at different known locations in the building. A mobile user's location at an arbitrary point in the building is determined by measuring the signal strength at the location in question and determining the ocation by referring to the above table using a LMSE (least mean square error) criterion. Obviously, this is a very tedious and time consuming task. This dissertation proposes a novel and automated location determination method called ARIADNE. Using a two dimensional construction floor plan and only a single actual signal strength measurement, ARIADNE dynamically generates an estimated signal strength map comparable to those generated manually by actual measurements. Given the signal measurements for a mobile, a proposed clustering algorithm searches that signal strength map to determine the current mobile's location. Extensive experiments with various deployment strategies have been carried out to evaluate the ARIADNE system at two different buildings. The results indicate that the ARIADNE system outperforms all other existing indoor localization schemes. For outdoor wireless sensor networks, this dissertation proposes two reliable and precise distributed localization algorithms: iterative multidimensional scaling (IT- MDS) and simulated annealing multidimensional scaling (SA- MDS). It uses only radio communication constraints to infer node distances, and adapts the multidimensional scaling algorithm (MDS) in the localization research. The research analytically establishes the upper- bound on the estimation error. The proposed techniques can estimate all node positions even with limited and imprecise network knowledge. Analysis and test runs show that the proposed methods are independent of the topology randomness and the range measurement errors. Simulation results for the proposed methods yield an average estimation error of about 25% of radio transmission range. Besides the detailed research on localization, recently there has been an increasing interest in exploring wireless communications, prototypes and measurements on real test- beds. In order to find a realistic tuning of simulation models, this research investigates one of the critical fundamentals - the realistic radio range irregularity (RRI) model - according to the measurements made under various settings. Using the RRI model, a set of representative localization algorithms are evaluated and compared. Through detailed analysis and extensive simulations, the dissertation points out how the localization performance is affected by the use of simplistic models. The RRI model reflects and highlights the weaknesses of those algorithms and allows the design of countermeasures. This dissertation also introduces a constrained-greedy forwarding radio propagation method to remedy the negative effects encountered under actual operating environments.