| Summary: | 博士 === 國立交通大學 === 資訊科學與工程研究所 === 97 === Recently, location-based services (LBSs) have emerged as one of the killer ap-plications for mobile computing. To support LBSs, location estimation mechanism is essential. In this dissertation, we are interested in a type of localization systems based on pattern-matching techniques in large-scale wireless networks. Among all localiza-tion systems, the pattern-matching systems are more cost-effective because they can rely on existing wireless network infrastructures and more resilient to the unpredictable signal fading effects.
In LBSs, the response time of location determination is critical, especially for real-time applications. This is especially true for pattern-matching localization, which relies on comparing an object's current signal pattern against a pre-established location database of signal patterns collected in the training phase. In this dissertation, we propose some cluster-enhanced schemes to speed up the positioning process while avoid the possible false cluster selection problem caused by this accelerated mechan-ism. Through grouping training locations with similar signal patterns together and characterizing them by a single feature vector, we show how to reduce the associated comparison cost so as to accelerate the pattern-matching process. To deal with signal fluctuations, several clustering strategies allowing overlaps are proposed.
Furthermore, we are also interested in achieving fine-grained localization in the pattern-matching localization systems. In such systems, fine-grained location estimation and quick location determination are conflicting concerns. For finer-grained loca-lization, we have to collect signal strength patterns at a larger number of training loca-tions. However, this may incur high computation cost during the pattern-matching process, thus incurring slow response. In this dissertation, we propose a novel discri-minant function (DF)-based localization methodology. Continuous and differentiable discriminant functions are designed to extract the spatial correlation of signal strength patterns of training locations that are close to each other. To realize this methodology, two algorithms are designed, called GDS-PL and GDS-INT, based on the concept of the gradient descent search.
Improving positioning accuracy is another important issue. Signal strength fluc-tuation is one of the major problems in a pattern-matching localization system. To al-leviate this problem, we propose a scrambling method to exploit temporal diversity and spatial dependency of collected signal samples. By means of scrambling, we can enlarge the sample space. Through recombining the limited observed signal patterns, samples with less interference are expected to appear. We present a methodology to illustrate how to apply these properties to enhance the positioning accuracy of several existing localization algorithms. Simulation studies and experimental results show that the scrambling method can greatly improve positioning accuracy, especially when the tracked object has some degree of mobility.
Finally, we care about the maintenance issue for a localization system. In most localization schemes, there are beacons being placed as references to determine the positions of objects or events appearing in the sensing field. The underlying assumption is that beacons are always reliable. In this work, we define a new Beacon Movement Detection (BMD) problem. Assuming that there are unnoticed changes of locations of some beacons in the system, this problem is concerned about how to automatically monitor such situations and identify such beacons based on the mutual observations among beacons only. Removal of such beacons in the localization engine may improve the localization accuracy.
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