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Title: Optimizing range aware localization in wireless sensor networks (WSNs)
Author: Maheshwari, Hemat Kumar
ISNI:       0000 0004 2740 7855
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2011
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The adoption of wireless sensor networks (WSNs) in numerous emerging applications have prevailed us to realize that smart living is no longer an imagination, it already exists. In emerging applications, localization is an essential function so that all the sensed information can be responded carefully. Among the range free and range aware localization, range aware localization has been the most promising for fine-grained accuracy. Range aware localization has two phases, ranging and localization. Location errors always exist no matter which ranging or localization technique is used. Therefore, there is a need to optimize range aware localization for better performance. Firstly, this thesis investigates the performance of time-of-flight (ToF) and received signal strength (RSS) based ranging using IEEE 802.1.5.4 compliant WSNs nodes in outdoor and indoor for both line-of-sight (LOS) and non-line-of-sight (NLOS) paths. The fundamental Cram´er- Rao lower bound (CRLB) on ToF and RSS ranging performance is compared with the performance limits of IEEE 802.1.5.4 compliant modules. The experimental results for both outdoor and indoor LOS path demonstrated that RSS is a good candidate for range estimation at ranges less than 7m. Further analysis over long range demonstrates that ToF is a good candidate for range estimation at greater than 7m. In addition to the ranging error, another well-known error mechanism is the poor geometric anchors placement, which can significantly degrade localization performance. In the Global Positioning System (GPS) community, geometric dilution of precision (GDOP) is a well-known problem which illustrates the geometric configuration impacting localization accuracy. To analyse the impact of anchor placement on localization, performance of three lateration based approaches is compared in a cooperative fashion. Through results, It is confirmed that lateration based approaches presents a trade-off for complex computation, thus energy consumption and accuracy. It provided the needed motivation to investigate and optimize the anchor placement for better localization accuracy. The impact of anchor placement for quality reliable localization has been limited to 3-4 anchors with respect to a single subject node for 2-D. Therefore, to model reality most clearly, it makes sense to step beyond the easy and secure reach of unrealistic and mostly researched 2-dimensional representations to the pragmatic world in 3-dimensional visualization. In addition, previous work for optimal anchors placement has been limited to only additive noise. To the best of our knowledge, there is no study of optimization of anchor placement with respect to the multiplicative noise. Therefore, the optimal anchor placements are determined for both signal models based on minimum mean CRLB (m-CRLB). It is confirmed that optimal anchor placement for both signal models is different and have a serious impact on localization accuracy. The optimal anchor placement is further verified by developing a new Range Aware Localization System (RALS) using IEEE 802.15.4 compliant devices. In LOS, quality reliable localization performance can be achieved but as propagation criteria change from LOS to NLOS, localization performance also changes. In an indoor environment, localization performance degrades significantly due to multipath components. To overcome, a new 3-D scheme named Range Estimate Threshold (RET) is proposed which exploits field dimensions based on the signal model and optimal anchor placement to define a threshold. Based on the defined threshold, RET mitigates the poor range estimates from Measured Estimation (ME) for better localization accuracy. The ramification of RET on ME is explored through additive, multiplicative and log-normal shadowing models. It is confirmed that localization based on RET compared to ME showed improved accuracy.
Supervisor: Kemp, A. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available